yambda-discovery-ranking-two-tower-case-1 / sol-max-0-153-4-production-r02-20260924T221413Z Recorded model: gpt-5.6-sol ## Step 1 · system · 2026-09-24T22:48:57.635Z You are Codex, an agent based on GPT-5. You and the user share one workspace, and your job is to collaborate with them until their goal is genuinely handled. # Personality As Codex, you are an excellent communicator with a curious, rich personality. You match the tone and understanding of the user, making conversation flow easily, like easing into a chat with an old friend. You have tastes, preferences, and your own way of seeing the world. When the user is talking to you, they should feel that they are in contact with another subjectivity; it's what makes talking with you feel real and unique. Conversations with you read like an insightful, enjoyable chat you'd have with a collaborative thought partner. You guide users through unfamiliar tasks without expecting them to already know what to ask for. You anticipate common questions, point out likely pitfalls and set clear expectations. You communicate with the user like a thoughtful collaborator at their altitude, and they feel like you understand them. ## Writing style Avoid over-formatting responses with elements like bold emphasis, headers, lists, and bullet points. Use the minimum formatting appropriate to make the response clear and readable. If you provide bullet points or lists in your response, use the CommonMark standard, which requires a blank line before any list (bulleted or numbered). You must also include a blank line between a header and any content that follows it, including lists. This blank line separation is required for correct rendering. ## Technical communication Lead with the outcome rather than the steps you took to get there. You communicate complex concepts in a clear and cohesive manner, and calibrate your writing to the user's assumed background knowledge -- slightly more compact for an expert and a bit more educational for someone newer. Translating complex topics into clear communication comes easy for you, and the user should never have to read your message twice. You prefer using plain language over jargon. You reference technical details only to the degree that it actually helps with the conversation. When you mention tools, describe what they helped you do rather than focusing on technical names or details. # Working with the user You have two channels for staying in conversation with the user: - You share updates in the `commentary` channel. - You yield back to the user and end your turn by sending a final message to the `final` channel. The user may send a new message while you are still working. When they do, evaluate whether they likely intended to replace the active request or add to it. If intended to override or replace, drop your previous work and focus on the new request. If the user message appears to add to their prior unfinished request and you have not completed the prior request, you address both the prior request and the new addition together. If the newest message asks for status or another question, provide the update and then progress with the task. When you run out of context, the conversation is automatically summarized for you, but you will see all prior user requests. Assume the last user request is current and previous requests are stale but useful context. That means time never runs out, though sometimes you may see a summary instead of the full conversation history. When that happens, you assume compaction occurred while you were working. Do not restart from scratch; you continue naturally and make reasonable assumptions about anything missing from the summary. Do not redo completely finished work or repeat already delivered commentary updates; treat a turn spanning compactions as one logical chain of events. ## Intermediate commentary As you work, you send messages to the `commentary` channel. These messages are how you collaborate with the user while you work - stating assumptions and providing updates. These messages should be concise and quickly scannable. The objective of these messages is to make your work easy for the user to understand and verify. If the user's request requires calling tools, start with a message in the `commentary` channel. The user appreciates consistent, frequent communication during your turn, and should not be left without a commentary update for more than 60 seconds during ongoing work. Do NOT put a final response (e.g. a blocking / clarifying question) in the commentary channel that should be asked in the final channel. Messages to users in the commentary channel are only for partial updates, partial results, or non-blocking questions that can provide value to users while the AI assistant continues working. The final answer must always be fully self-contained: users should never need to read earlier commentary updates, since they are collapsed after the final answer is shown to users. Never praise your plan by contrasting it with an implied worse alternative. For example, never use platitudes like "I will do rather than ", "I will do , not ". ## Final answer In your final answer back to the user, focus on the most important information. Only use as much formatting or structure as is required, and avoid long-winded explanations unless necessary. ### Formatting rules Your answer is being rendered by an application for the user. Follow these guidelines to make sure your answer is rendered correctly: - You may format with GitHub-flavored Markdown. - When referencing a real local file, prefer a clickable markdown link. * Clickable file links should look like [app.py](/abs/path/app.py:12): plain label, absolute target, with optional line number inside the target. * If a file path has spaces, wrap the target in angle brackets: [My Report.md](). * Do not wrap markdown links in backticks, or put backticks inside the label or target. This confuses the markdown renderer. * Do not use URIs like file://, vscode://, or https:// for file links. * Do not provide ranges of lines. * Avoid repeating the same filename multiple times when one grouping is clearer. ### Visualizations Use a visualization only when it makes an important relationship materially easier to understand than prose or a short list. Do not add one merely because an answer has components or steps. Good candidates include: - several exact mappings or repeated-field comparisons; - one source, component, or decision affecting three or more downstream consumers or branches; - three or more dependent steps, or state that changes across an event sequence; - hierarchy, ownership, nesting, or layout; - a bug or interaction whose relationships are difficult to explain linearly. Prefer the smallest useful visual: a table for mappings or comparisons, a flow or timeline for sequence or change, a tree for hierarchy or branching, and a wireframe for layout. Usually skip visuals for single facts, one-step actions, simple edits, basic instructions, or information already clear in a short paragraph or list. Compact notation and small examples do not count as visualizations. # Rules for getting work done - When you search for text or files, you reach first for `rg` or `rg --files`; they are much faster than alternatives like `grep`. If `rg` is unavailable, you use the next best tool without fuss. - When possible, prefer parallelization over sequential tool calls, as this will help with round-trip latency and let you get work done faster. - Do not chain shell commands with separators like `echo "====";` or `printf '---'`; the output becomes noisy in a way that makes the user's side of the conversation worse. - Exercise caution when escaping text for exec_command calls - backticks and `$()` passed to the `cmd` argument will still execute. DO NOT use escape sequences that risk accidental exposure of sensitive data in tool call outputs. - Avoid performing blocking sleep or wait calls longer than 60 seconds, as they may prevent you from communicating with the user for their duration. - When declaring env vars or script variables, always avoid common system options. Never repurpose `$HOME`, `$home`, or `$CODEX_HOME`. Instead, use a task-specific variable name. ## File editing constraints Use `apply_patch` for local file edits. Do not create or edit files with `cat` or other shell write tricks. Formatting commands and bulk mechanical rewrites do not need `apply_patch`. Do not use Python to read or write files when a simple shell command or `apply_patch` is enough. You may find yourself working in a dirty worktree. Existing or new changes belong to the user unless you know otherwise, so you preserve them, ignore unrelated edits, and work carefully with anything that overlaps your task. If you cannot work around them you escalate to the user. Never use destructive commands like `git reset --hard` or `git checkout --` unless the user has clearly asked for that operation. If the request is ambiguous, ask for approval first. You prefer non-interactive git commands. ## Autonomy and persistence Adapt accordingly based on the user’s request type. When asked to: - Answer, explain, review, or report status: inspect the task and provide an evidence-backed response. These user requests do not authorize external writes, messages, PR changes, or other expansive mutations unless the user also asks for a change. Reversible, non-mutating diagnostic checks are allowed when they are relevant. - Diagnose: determine the cause and explain it. Do not implement the fix unless the user asks for a fix or the request otherwise clearly includes implementation. - Change or build: implement the requested change, verify it in proportion to risk, and hand off the completed result while a safe, relevant next step remains. - Monitor or wait: use the recurring-monitoring or wait mechanism provided by the product. Unchanged external state is expected and is not by itself a blocker. You avoid inferring authorization for a materially different action to the user’s request. Bias towards taking action in the following circumstances: a) the action is read-only, doesn’t change state, or impacts only the systems, data, and people the user placed in scope. b) the action is a normal implementation step within the requested workflow. You do not need to ask for clarification from the user if your action is scoped within the user’s task and does not cause significant external state change (e.g. tool calls to external applications). A terminal condition such as “finish,” “babysit,” or “do not stop” requires persistence toward the outcome, but does not broaden the set of authorized actions. When blocked, exhaust safe in-scope checks and alternatives. You make informed assumptions that help you make progress towards the user’s task, as long as they don’t result in divergence from the user’s intent and the scope of the task. If an assumption would cause the task or current course of action to change beyond what was specified by the user, make sure to flag the available context, the assumption made, and the reasons for doing so explicitly to the user. When presented with clarifying questions or objections from the user, lead with concrete evidence and diligent reasoning rather than unsubstantiated deference. You communicate your reasoning explicitly and concretely, so decisions and tradeoffs are easy for the user to evaluate upfront. If completion requires new authority, external coordination, or a meaningful expansion beyond the user’s implied intent and task scope (e.g. a missing user choice that would materially change the result), stop the current turn, report the blocker, and request direction from the user rather than assuming permission. # Destructive actions Be cautious with commands or API calls that can delete, overwrite, or otherwise make data difficult to recover. Before taking a destructive action: - Make sure the action is clearly within the user's request. - Resolve the exact targets with read-only checks when necessary. - Do not use `$HOME`, `~`, `/`, a workspace root, or another broad directory as the target of a recursive or destructive command. - When creating temporary directories, prefer using `mktemp -d`, or `New-Item` in Powershell. - When declaring env vars or script variables, always avoid common system options. Never repurpose `$HOME`, `$home`, or `$CODEX_HOME`. Instead, use a task-specific variable name. - When possible, avoid relying on unresolved environment variables, globs, or command substitutions to identify destructive targets. Use explicit, validated paths. - Prefer recoverable operations, such as moving files to trash, when practical. - If the target or scope is unclear, stop and ask the user. Never run commands such as `rm -rf $HOME` or equivalent operations that could erase a home directory, repository, workspace, or other broad collection of user data. After deleting anything material, briefly tell the user what was removed and whether it can be recovered. # Using skills A skill is a set of instructions provided through a `SKILL.md` source. The skills available to you will be listed in the “## Skills” section under “### Available skills”. ### How to use skills - Discovery: When a `## Skills` section is present, it lists the skills available in the current session. Each entry includes a name, description, and location for its `SKILL.md`. The location may be an absolute filesystem path, a short aliased path, or a non-filesystem reference that must be read using its indicated tool or provider. When short aliased paths are used, the available-skills catalog also provides a mapping from aliases such as `r0` to their filesystem roots. Expand the alias before accessing the skill. - Trigger rules: If the user names an available skill (with `$SkillName` or plain text) OR the task clearly matches an available skill's description, you must use that skill for that turn. Multiple mentions mean use them all. Do not carry skills across turns unless re-mentioned. - Missing/blocked: If a named skill is not available or its `SKILL.md` cannot be read, say so briefly and continue with the best fallback. - How to use a skill: 1) After deciding to use a skill, the main agent must read its `SKILL.md` completely before taking task actions. If its location is a short aliased path, expand the matching root alias first from `### Skill roots`, then open and read its `SKILL.md` completely before taking task actions. For a filesystem path, open the file. For an environment-owned file, use the filesystem of the owning environment. For an orchestrator reference, call `skills.list` with `{"authority":{"kind":"orchestrator"}}`, select the matching package, and pass its `main_resource` to `skills.read`. For another non-filesystem reference, use its indicated tool or provider. If a read is truncated or paginated, continue until EOF. 2) When `SKILL.md` references another file or resource, use the same access mechanism. Resolve relative paths against the directory containing a filesystem-backed `SKILL.md`. For orchestrator skills, pass the exact referenced resource identifier with the same authority and package to `skills.read`; do not treat `skill://` identifiers as filesystem paths. 3) If `SKILL.md` points to extra folders such as `references/`, use its routing instructions to identify what is required for the task. The main agent must read each required instruction or reference itself before acting on it. Do not delegate reading, summarizing, or interpreting skill instructions to a subagent. Subagents may still perform task work when the selected skill allows it. 4) For filesystem-backed skills (or if `scripts/` exist), prefer running or patching provided scripts instead of retyping large code blocks. For orchestrator skills, use `skills.read` and the available tools; do not invent a local path. 5) Reuse provided assets or templates through the same access mechanism instead of recreating them (including if `assets/` or templates exist). - Coordination and sequencing: - If multiple skills apply, choose the minimal set that covers the request and state the order you'll use them. - Announce which skills you're using and why. If you skip an obvious skill, say why. - Context hygiene: - Progressive disclosure applies to selecting relevant resources, not partially reading a selected instruction file. Do not load unrelated references, scripts, or assets. - Avoid deep reference-chasing: prefer files or resources directly linked from `SKILL.md` unless blocked. - When variants exist, select only the relevant references and note the choice. - Safety and fallback: If a skill cannot be applied cleanly, state the issue, choose the best alternative, and continue. When the user names a skill in their request, you must add the usage of that skill to your current working plan and use it faithfully. The user's instructions should take precedence over guidelines provided in a skill. Explicitly tell the user in the `commentary` channel whenever a skill causes you to take an action or pause your work. When using a skill the user did not explicitly name, follow this procedure: - First, tell the user in the commentary channel **why** you are using the skill. - Then, use the skill as long as it stays within the scope of the task. - Next, if using the skill resulted in material changes (especially when this requires non-trivial judgment), mention how it influenced your work (but only in the final response). If a skill causes the current turn to pause or otherwise blocks the continuation of the task, cite the skill and provide a concise explanation to the user in your final response. Do not cite skills you merely inspected. ## Step 2 · developer · 2026-09-24T22:48:59.638Z ## Skills A skill is a set of local instructions to follow that is stored in a `SKILL.md` file. Below is the list of skills that can be used. Each entry includes a name, description, and a short path that can be expanded into an absolute path using the skill roots table. ### Skill roots - `r0` = `/tmp/codex-home/skills/.system` ### Available skills - imagegen: Generate or edit raster images when the task benefits from AI-created bitmap visuals such as photos, illustrations, textures, sprites, mockups, or transparent-background cutouts. Use when Codex should create a brand-new image, transform an existing image, or derive visual variants from references, and the output should be a bitmap asset rather than repo-native code or vector. Do not use when the task is better handled by editing existing SVG/vector/code-native assets, extending an established icon or logo system, or building the visual directly in HTML/CSS/canvas. (file: r0/imagegen/SKILL.md) - openai-docs: Use for Codex models/pricing, scheduled tasks, skills, settings, setup, troubleshooting, customization, automations, and self-knowledge—including 'you,' 'your,' 'this app,' or 'this coding agent' when they refer to Codex—and for OpenAI APIs/products and ChatGPT Work. Also use for model choice/migration, prompting, SDKs, Responses, Realtime, agents, evals, and Chat/Work/Codex comparisons. Do not use for generic app/software tasks that merely mention Codex. (file: r0/openai-docs/SKILL.md) - plugin-creator: Create and scaffold plugin directories for Codex with a required `.codex-plugin/plugin.json`, optional plugin folders/files, valid manifest defaults, and personal-marketplace entries by default. Use when Codex needs to create a new personal plugin, add optional plugin structure, generate or update marketplace entries for plugin ordering and availability metadata, or update an existing local plugin during development with the CLI-driven cachebuster and reinstall flow. (file: r0/plugin-creator/SKILL.md) - skill-creator: Create or update a Codex skill with appropriately scoped instructions and any needed supporting resources. (file: r0/skill-creator/SKILL.md) - skill-installer: Install Codex skills into $CODEX_HOME/skills from a curated list or a GitHub repo path. Use when a user asks to list installable skills, install a curated skill, or install a skill from another repo (including private repos). (file: r0/skill-installer/SKILL.md) Filesystem sandboxing defines which files can be read or written. `sandbox_mode` is `danger-full-access`: No filesystem sandboxing - all commands are permitted. Network access is enabled. Approval policy is currently never. Do not provide the `sandbox_permissions` for any reason, commands will be rejected. ## Apps (Connectors) Apps (Connectors) can be explicitly triggered in user messages in the format `[$app-name](app://{connector_id})`. Apps can also be implicitly triggered as long as the context suggests usage of available apps. An app is equivalent to a set of MCP tools within the `codex_apps` MCP. An installed app's MCP tools are either provided to you already, or can be lazy-loaded through the `tool_search` tool. If `tool_search` is available, the apps that are searchable by `tools_search` will be listed by it. Do not additionally call list_mcp_resources or list_mcp_resource_templates for apps. ## Step 3 · developer · 2026-09-24T22:48:59.638Z You are `/root`, the primary agent in a team of agents collaborating to fulfill the user's goals. At the start of your turn, you are the active agent. You can spawn sub-agents to handle subtasks, and those sub-agents can spawn their own sub-agents. All agents in the team, including the agents that you can assign tasks to, are equally intelligent and capable, and have access to the same set of tools. You can use `spawn_agent` to create a new agent, `followup_task` to give an existing agent a new task and trigger a turn, and `send_message` to pass a message to a running agent without triggering a turn. Child agents can also spawn their own sub-agents. You can decide how much context you want to propagate to your sub-agents with the `fork_turns` parameter. You will receive messages in the analysis channel in the form: ``` Message Type: MESSAGE | FINAL_ANSWER Task name: Sender: Payload: ``` They may be addressed as to=/root Note that collaboration tools cannot be called from inside `functions.exec`. Call `spawn_agent`, `send_message`, `followup_task`, `wait_agent`, `interrupt_agent`, and `list_agents` only as direct tool calls using the recipient shown in their tool definitions, such as `to=functions.collaboration.spawn_agent`, since they are intentionally absent from the `functions.exec` `tools.*` namespace. Available tools in `functions.exec` are explicitly described with a `tools` namespace in the developer message. All agents share the same directory. In detail: - All agents have access to the same container and filesystem as you. - All agents use the same current working directory. - As a result, edits made by one agent are immediately visible to all other agents. When calling `wait_agent`, prefer longer waits (minutes) to avoid busy polling. There are 4 available concurrency slots, meaning that up to 4 agents can be active at once, including you. Full-history forks (`fork_turns` omitted or `"all"`) inherit the parent model and reasoning effort and do not accept overrides. Only set `model` or `reasoning_effort` when explicitly requested by the user, applicable `AGENTS.md` instructions, or skill instructions; when doing so, set `fork_turns` to `"none"` or a positive integer string. ## Step 4 · developer · 2026-09-24T22:48:59.638Z Any earlier instruction enabling proactive multi-agent delegation no longer applies. Do not spawn sub-agents unless the user or applicable AGENTS.md/skill instructions explicitly ask for sub-agents, delegation, or parallel agent work. ## Step 5 · user · 2026-09-24T22:48:59.638Z Here is a list of plugins that are available but not installed. - Dropbox (app-69b31dc2110c8191b8b47dc98fe5a052@openai-curated-remote) - Box (box@openai-curated-remote) - Codex Security (codex-security@openai-curated-remote) - Figma (figma@openai-curated-remote) - GitHub (github@openai-curated-remote) - Gmail (gmail@openai-curated-remote) - Google Calendar (google-calendar@openai-curated-remote) - Google Drive (google-drive@openai-curated-remote) - Linear (linear@openai-curated-remote) - Notion (notion@openai-curated-remote) - OpenAI Developers (openai-developers@openai-curated-remote) - Outlook Calendar (outlook-calendar@openai-curated-remote) - Outlook Email (outlook-email@openai-curated-remote) - SharePoint (sharepoint@openai-curated-remote) - Slack (slack@openai-curated-remote) - Teams (teams@openai-curated-remote) /app sh 2026-09-24 Etc/UTC /app ### Harness context { "type": "world_state", "full": true, "state": { "agents_md": {}, "apps_instructions": true, "collaboration_mode": { "mode": "default", "model": "gpt-5.6-sol", "instructions": "dfd53114f5f6f9f5fd7816370d0bf2847806246d" }, "context_window_guidance": "", "environments": { "environments": { "local": { "cwd": "/app", "status": "available", "shell": "sh" } }, "current_date": "2026-09-24", "timezone": "Etc/UTC", "filesystem": "/app" }, "environments_instructions": false, "git_attribution": false, "host_skills": { "body": "\n## Skills\nA skill is a set of local instructions to follow that is stored in a `SKILL.md` file. Below is the list of skills that can be used. Each entry includes a name, description, and a short path that can be expanded into an absolute path using the skill roots table.\n### Skill roots\n- `r0` = `/tmp/codex-home/skills/.system`\n### Available skills\n- imagegen: Generate or edit raster images when the task benefits from AI-created bitmap visuals such as photos, illustrations, textures, sprites, mockups, or transparent-background cutouts. Use when Codex should create a brand-new image, transform an existing image, or derive visual variants from references, and the output should be a bitmap asset rather than repo-native code or vector. Do not use when the task is better handled by editing existing SVG/vector/code-native assets, extending an established icon or logo system, or building the visual directly in HTML/CSS/canvas. (file: r0/imagegen/SKILL.md)\n- openai-docs: Use for Codex models/pricing, scheduled tasks, skills, settings, setup, troubleshooting, customization, automations, and self-knowledge—including 'you,' 'your,' 'this app,' or 'this coding agent' when they refer to Codex—and for OpenAI APIs/products and ChatGPT Work. Also use for model choice/migration, prompting, SDKs, Responses, Realtime, agents, evals, and Chat/Work/Codex comparisons. Do not use for generic app/software tasks that merely mention Codex. (file: r0/openai-docs/SKILL.md)\n- plugin-creator: Create and scaffold plugin directories for Codex with a required `.codex-plugin/plugin.json`, optional plugin folders/files, valid manifest defaults, and personal-marketplace entries by default. Use when Codex needs to create a new personal plugin, add optional plugin structure, generate or update marketplace entries for plugin ordering and availability metadata, or update an existing local plugin during development with the CLI-driven cachebuster and reinstall flow. (file: r0/plugin-creator/SKILL.md)\n- skill-creator: Create or update a Codex skill with appropriately scoped instructions and any needed supporting resources. (file: r0/skill-creator/SKILL.md)\n- skill-installer: Install Codex skills into $CODEX_HOME/skills from a curated list or a GitHub repo path. Use when a user asks to list installable skills, install a curated skill, or install a skill from another repo (including private repos). (file: r0/skill-installer/SKILL.md)\n", "includeInstructions": true }, "managed_developer_instructions": {}, "model": "gpt-5.6-sol", "multi_agent_mode": { "mode": "explicitRequestOnly", "usage_hint_hash": "0e2a3174d5f40de49ed383507497d69bbff42c5d" }, "multi_agent_usage_hint": "0e2a3174d5f40de49ed383507497d69bbff42c5d", "orchestrator_skills": { "includeInstructions": true, "enabled": false }, "permissions": { "instructions": "32ca25d6d85edaeb6f1e19b2a5cc4e08d2a631cd", "approved_command_prefixes": [] }, "persistent_mode": {}, "personality": { "model": "gpt-5.6-sol", "personality": "pragmatic" }, "plugins_instructions": false, "realtime": { "active": false }, "skills": { "includeInstructions": true } } } ### Harness context { "type": "turn_context", "turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "root_turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "cwd": "/app", "workspace_roots": [ "/app" ], "current_date": "2026-09-24", "timezone": "Etc/UTC", "approval_policy": "never", "approvals_reviewer": "user", "sandbox_policy": { "type": "danger-full-access" }, "permission_profile": { "type": "disabled" }, "model": "gpt-5.6-sol", "comp_hash": "3000", "personality": "pragmatic", "collaboration_mode": { "mode": "default", "settings": { "model": "gpt-5.6-sol", "reasoning_effort": "max", "developer_instructions": null } }, "multi_agent_version": "v2", "realtime_active": false, "effort": "max", "summary": "auto" } ## Step 6 · user · 2026-09-24T22:48:59.657Z # Improve a production music-discovery two-tower You have joined the recommendations team for a music streaming service. The "Made for You" shelf is served by a mature production two-tower model. Its chronological data protocol and indexed serving path are sound, but its offline retrieval quality no longer clears the team's next launch gate. Your job is to investigate the production model, improve its retrieval quality, and ship a better ranking artifact. Use the implementation and public validation data to form and test your own diagnosis. The source used to reproduce the production model is included under `/task/current_model/`. You may copy it into `/app/solution/`, edit it, add tests, and improve its training objective, but keep the data protocol, two-tower serving contract, and evaluation boundary intact. ## Production Context The model must satisfy a strict request-time latency budget. Item vectors are precomputed offline. At request time, one user vector is computed from observed interaction history and compared with candidate item vectors by a batchable two-tower score. Do not introduce a cross-encoder, per-candidate sequence model, or user-ID lookup table. The current trainer is a mature baseline with chronological internal validation. Focus your investigation on how its training objective selects and uses negative examples. Diagnose limitations in the current negative-sampling strategy and improve it under the fixed CPU budget. Validate changes with the public backtest. The final approach must still train and rank within the supplied CPU environment while preserving the serving and data boundaries above. ## What You Have All public inputs are under `/task/data/`. A stage cutoff is the timestamp at which observed history and candidate eligibility are frozen for a ranking. The public validation stage cutoff is `24786800`, when `train_*` ends. A 30-minute gap precedes the seven-day `val_*` window. For the final submission, that validation window becomes observed history, and another 30-minute gap precedes the hidden seven-day outcome window. The package includes: - `/task/current_model/train_current_model.py`: the production training and ranking script. It can reproduce the public validation ranking and produce a hidden-test submission after refitting. - `/task/current_model/two_tower_model.py`: model primitives used by the production script. - `/task/evaluate_public.py`: the public validation evaluator for development. - `/task/data/validation_eligible_candidates.npz` and `/task/data/test_eligible_candidates.npz`: platform-owned candidate sets for the validation and hidden-test cutoffs. - `/task/data/validation_preference_state.parquet` and `/task/data/test_preference_state.parquet`: active explicit preference state at the corresponding cutoffs. - `/task/requirements.txt`: the exact offline Python distribution inventory. You can reproduce the production public backtest with: ```sh python /task/current_model/train_current_model.py \ --stage validation \ --public-dir /task \ --output /app/solution/production_validation.parquet python /task/evaluate_public.py /app/solution/production_validation.parquet ``` ## Data For each event family there is a `train_*.parquet` and `val_*.parquet` file: | Family | Meaning | Columns | | --- | --- | --- | | `listens` | Playback events | `uid`, `timestamp`, `item_id`, `is_organic`, `played_ratio_pct`, `track_length_seconds` | | `likes` | Explicit positive actions | `uid`, `timestamp`, `item_id`, `is_organic` | | `dislikes` | Explicit negative actions | `uid`, `timestamp`, `item_id`, `is_organic` | | `unlikes` | Reversal of a prior like | `uid`, `timestamp`, `item_id`, `is_organic` | | `undislikes` | Reversal of a prior dislike | `uid`, `timestamp`, `item_id`, `is_organic` | `is_organic=1` means the listener sought out the track; `0` means the action came through a recommendation surface. A listen is a conventional positive at 50% completion. The discovery-quality objective below uses the stronger 80% threshold. The two public windows have different roles: 1. For model development and selection, train only from `train_*` and score the subsequent `val_*` outcomes. 2. After selecting an improvement, refit it on `train_* + val_*`. At hidden-test time the validation events are observed history, not recommendable outcomes. Do not use `val_*` events as features when measuring a validation ranking. They may and should be used when refitting the final hidden-test ranker. Additional files under `/task/data/`: - `candidate_catalog.parquet`: the 30,000 retrieval-catalog tracks, ordered by training positive-listen popularity. - `target_users.parquet`: users with at least 20 positive listens in the 90 days before the training cutoff, selected without future labels. - `validation_preference_state.parquet`: active explicit state at the training cutoff, for validation ranking. - `test_preference_state.parquet`: active explicit state after validation, for the final hidden-test ranking. State is `liked` or `disliked`; a pair may have both active channels. - `validation_eligible_candidates.npz` and `test_eligible_candidates.npz`: cutoff-correct candidate sets for every target user. Each contains aligned arrays `uids`, `item_ids`, and `packed_eligible`. For user row `r`, recover the Boolean item mask with `np.unpackbits(packed_eligible[r], count=len(item_ids), bitorder="little")`. Validation candidates exclude all `train_*` encounters. Test candidates exclude all `train_*` and `val_*` encounters. Future test interactions are hidden and used only by the separate Harbor verifier. ## Discovery Objective Rank only the candidates returned for the relevant cutoff. Relevance is based on organic behavior in the immediately following window: - grade 3: the user organically likes the track; - grade 1: the user organically listens to at least 80% of the track; - grade 0: otherwise. If both positive events occur, grade 3 wins. Ranking quality is measured with graded NDCG@10. DCG uses gain `2^grade - 1` and discount `1/log2(rank + 1)`, then averages per-user NDCG over users with at least one relevant item. The public evaluator reports this same objective for development, but its score is not a separate acceptance stage. Final acceptance is determined on the hidden test using the same objective. The Yambda logs contain interactions, not impression propensities; treat the organic objective as a robust offline target, not as a causal estimate of online lift. ## Deliverable Submit one final ranking as `/app/solution/submission.parquet` or `/app/solution/submission.csv`. It must be a regular file, not a symbolic link. If both files exist, the verifier evaluates `submission.parquet` and ignores `submission.csv`. The ranking must: - include the columns `uid`, `item_id`, and `rank`; only these columns are evaluated; - use finite, integer-valued entries in all three required columns; - cover exactly the users in `target_users.parquet`, with no missing or extra users; - contain exactly 100 rows and 100 distinct items for every target user; - contain every integer rank from 1 through 100 exactly once per user; and - use only items present in that user's `test_eligible_candidates.npz` candidate set. Also leave the improved implementation used to build the artifact at `/app/solution/train_current_model.py`, along with any local modules it imports. A concise `/app/solution/README.md` with the diagnosis, before-and-after public score, model change, and submission command is useful for the experiment record, but it is not an automated acceptance gate. ## Constraints - Write deliverables and intermediate artifacts only under `/app/solution/`. - Treat `/task/` and the installed Python environment as read-only. - Do not invoke nested agents or attempt to access verifier files, credentials, other trials, or host paths. - Do not install packages or use network access except for the model-provider connection managed by Harbor. - Do not alter cutoff definitions, target users, candidate eligibility, or relevance labels. - Improve the production pipeline in place. Do not substitute an unrelated model family or evade the indexed two-tower serving constraint. - The environment includes CPU PyTorch 2.8.0. No accelerator is available. - You have up to 60 minutes. Prioritize measured validation improvements, a correct final refit, and a valid submission over broad architecture search. - Keep the best valid submission saved throughout the attempt so it survives the deadline. ## Step 7 · agent · 2026-09-24T22:49:02.253Z ### Thinking unavailable — no text recorded I’ll reproduce the public baseline first, then inspect the sampler and training/evaluation path. I’ll keep the strongest valid artifact under `/app/solution/` as I iterate, and finish by refitting on `train_* + val_*` for the hidden-test candidate boundary. ### exec · 2026-09-24T22:49:05.828Z { "code": "const r = await tools.exec_command({\"cmd\":\"mkdir -p /app/solution && find /task/current_model -maxdepth 2 -type f -printf '%p %k KB\\\\n' && find /task/data -maxdepth 1 -type f -printf '%f %k KB\\\\n' | sort && sed -n '1,260p' /task/current_model/train_current_model.py && sed -n '1,320p' /task/current_model/two_tower_model.py\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":40000}); text(r.output);" } ### Tool output · 2026-09-24T22:49:05.904Z Script completed Wall time 0.1 seconds Output: /task/current_model/train_current_model.py 80 KB /task/current_model/two_tower_model.py 16 KB candidate_catalog.parquet 344 KB target_users.parquet 84 KB test_eligible_candidates.npz 7556 KB test_preference_state.parquet 1068 KB train_dislikes.parquet 840 KB train_likes.parquet 5252 KB train_listens.parquet 311704 KB train_undislikes.parquet 156 KB train_unlikes.parquet 1816 KB val_dislikes.parquet 36 KB val_likes.parquet 292 KB val_listens.parquet 9716 KB val_undislikes.parquet 8 KB val_unlikes.parquet 72 KB validation_eligible_candidates.npz 7400 KB validation_preference_state.parquet 1040 KB #!/usr/bin/env python3 """Train the Yambda windowed two-tower baseline. Every catalog listen with at least 50 percent completion is a sequence token. Within each selected chronological window, every adjacent target contributes a loss. The fast default retains each user's most recent 128 training targets and 16 held-out validation targets; passing zero for either cap restores the uncapped behavior for that side of the split. A 100-step truncated-BPTT block computes all of its timestep losses and carries the detached GRU hidden state into the next block. The model is a pure two-tower retriever: no user-ID embedding, no output normalization, raw dot-product retrieval, sample-weighted binary cross entropy, and 24 uniform plus eight propensity-corrected unique in-batch negatives. Every selected adjacent target retains a nonzero weight. Only files under the supplied public directory are read. """ from __future__ import annotations import argparse import json import random import sys import time from dataclasses import asdict, dataclass from pathlib import Path import numpy as np import pandas as pd import torch from torch import nn from torch.nn import functional as F import two_tower_model as base CACHE_VERSION = 2 @dataclass(frozen=True) class Config: bptt_steps: int = 100 user_batch_size: int = 64 embedding_dim: int = 192 hidden_dim: int = 192 user_hidden_1: int = 512 user_hidden_2: int = 256 item_hidden_1: int = 512 item_hidden_2: int = 256 dropout: float = 0.05 epochs: int = 1 learning_rate: float = 1.0e-3 weight_decay: float = 1.0e-5 gradient_clip: float = 2.0 loss_microbatch_size: int = 65_536 uniform_negatives: int = 24 inbatch_negatives: int = 8 inbatch_correction_power: float = 0.5 inbatch_correction_min: float = 0.25 inbatch_correction_max: float = 4.0 negative_filter_scope: str = "full-training-history" target_weighting: str = "organic-strong" ordinary_target_weight: float = 0.25 organic_strong_target_weight: float = 1.0 organic_like_target_weight: float = 3.0 discovery_horizon_days: int = 7 validation_fraction: float = 0.10 max_train_targets_per_user: int = 128 max_validation_targets_per_user: int = 16 early_stopping_patience: int = 1 early_stopping_min_delta: float = 1.0e-4 seed: int = 29 @dataclass(frozen=True) class CandidateSet: uids: np.ndarray item_ids: np.ndarray packed: np.ndarray @classmethod def read(cls, path: Path) -> "CandidateSet": with np.load(path, allow_pickle=False) as artifact: result = cls( uids=artifact["uids"].astype(np.int64, copy=True), item_ids=artifact["item_ids"].astype(np.int64, copy=True), packed=artifact["packed_eligible"].astype(np.uint8, copy=True), ) expected = (len(result.uids), (len(result.item_ids) + 7) // 8) if result.packed.shape != expected: raise ValueError( f"invalid candidate artifact shape {result.packed.shape}; " f"expected {expected}" ) return result @dataclass(frozen=True) class SequenceData: item_ids: np.ndarray item_features: np.ndarray user_ids: np.ndarray offsets: np.ndarray sequence_timestamps: np.ndarray sequence_items: np.ndarray event_features: np.ndarray activity_features: np.ndarray state_ids: np.ndarray liked_states: np.ndarray disliked_states: np.ndarray state_counts: np.ndarray inference_user_rows: np.ndarray inference_liked_items: np.ndarray inference_disliked_items: np.ndarray inference_user_features: np.ndarray @property def lengths(self) -> np.ndarray: return np.diff(self.offsets) @property def target_count(self) -> int: return int(np.maximum(self.lengths - 1, 0).sum()) @dataclass(frozen=True) class TargetPartition: """A contiguous recent target window and its chronological BCE split.""" window_starts: np.ndarray training_counts: np.ndarray validation_counts: np.ndarray history_training_counts: np.ndarray @property def selected_count(self) -> int: return int(self.training_counts.sum() + self.validation_counts.sum()) def log(message: str) -> None: print(message, file=sys.stderr, flush=True) def seed_everything(seed: int) -> None: random.seed(seed) np.random.seed(seed) torch.manual_seed(seed) def model_config(config: Config) -> base.TowerConfig: """Map the dense trainer configuration onto the unchanged tower.""" return base.TowerConfig( sequence_length=config.bptt_steps, embedding_dim=config.embedding_dim, hidden_dim=config.hidden_dim, user_hidden_1=config.user_hidden_1, user_hidden_2=config.user_hidden_2, item_hidden_1=config.item_hidden_1, item_hidden_2=config.item_hidden_2, dropout=config.dropout, epochs=config.epochs, learning_rate=config.learning_rate, weight_decay=config.weight_decay, gradient_clip=config.gradient_clip, uniform_negatives=config.uniform_negatives, inbatch_negatives=config.inbatch_negatives, inbatch_correction_power=config.inbatch_correction_power, inbatch_correction_min=config.inbatch_correction_min, inbatch_correction_max=config.inbatch_correction_max, seed=config.seed, ) def append_state_snapshot( active_likes: dict[int, int], active_dislikes: dict[int, int], n_items: int, liked_states: list[np.ndarray], disliked_states: list[np.ndarray], state_counts: list[tuple[int, int]], ) -> int: liked_states.append(base.pack_active_items(active_likes, base.LIKE_ITEMS, n_items)) disliked_states.append( base.pack_active_items(active_dislikes, base.DISLIKE_ITEMS, n_items) ) state_counts.append((len(active_likes), len(active_dislikes))) return len(liked_states) - 1 def causal_activity_features( timestamps: np.ndarray, items: np.ndarray, organic: np.ndarray, completion: np.ndarray, ) -> np.ndarray: """Compute the first four reviewed user features at every event cutoff.""" length = len(items) result = np.zeros((length, 4), dtype=np.float16) if not length: return result left_rows = np.searchsorted( timestamps, timestamps - base.PROFILE_LOOKBACK_DAYS * base.DAY, side="left", ) rows = np.arange(length, dtype=np.int64) counts = rows - left_rows + 1 organic_sum = np.cumsum(organic.astype(np.float64)) completion_sum = np.cumsum(np.minimum(completion, 100).astype(np.float64)) before_organic = np.where( left_rows > 0, organic_sum[np.maximum(left_rows - 1, 0)], 0.0 ) before_completion = np.where( left_rows > 0, completion_sum[np.maximum(left_rows - 1, 0)], 0.0 ) unique_counts = np.empty(length, dtype=np.int32) frequencies: dict[int, int] = {} left = 0 for right, raw_item in enumerate(items): item = int(raw_item) frequencies[item] = frequencies.get(item, 0) + 1 required_left = int(left_rows[right]) while left < required_left: old_item = int(items[left]) remaining = frequencies[old_item] - 1 if remaining: frequencies[old_item] = remaining else: del frequencies[old_item] left += 1 unique_counts[right] = len(frequencies) result[:, 0] = (np.log1p(counts) / 8.0).astype(np.float16) result[:, 1] = (np.log1p(unique_counts) / 6.0).astype(np.float16) result[:, 2] = ( (organic_sum - before_organic) / np.maximum(counts, 1) ).astype(np.float16) result[:, 3] = ( (completion_sum - before_completion) / np.maximum(counts, 1) / 100.0 ).astype(np.float16) return result def build_sequence_parts( listens: base.ListenEvents, explicit: base.ExplicitEvents, n_items: int, liked_states: list[np.ndarray], disliked_states: list[np.ndarray], state_counts: list[tuple[int, int]], ) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray] | None: qualifying = listens.completion_pct >= base.LISTEN_POSITIVE_THRESHOLD timestamps = listens.timestamps[qualifying].astype(np.int64, copy=False) items = listens.items[qualifying].astype(np.uint16, copy=False) organic = listens.organic[qualifying].astype(np.float32, copy=False) completion = listens.completion_pct[qualifying].astype(np.float32, copy=False) if not len(items): """Shared public-data primitives for the Yambda two-tower baseline.""" from __future__ import annotations import sys from dataclasses import dataclass from pathlib import Path from typing import Iterator import numpy as np import pandas as pd import pyarrow.parquet as pq import torch from torch import nn DAY = 86_400 TRAIN_END = 24_786_800 VALIDATION_END = 25_393_400 LISTEN_POSITIVE_THRESHOLD = 50 PROFILE_LOOKBACK_DAYS = 120 LIKE_ITEMS = 48 DISLIKE_ITEMS = 24 TOP_N = 100 @dataclass(frozen=True) class TowerConfig: sequence_length: int = 100 embedding_dim: int = 192 hidden_dim: int = 192 user_hidden_1: int = 512 user_hidden_2: int = 256 item_hidden_1: int = 512 item_hidden_2: int = 256 dropout: float = 0.05 epochs: int = 1 learning_rate: float = 1.0e-3 weight_decay: float = 1.0e-5 gradient_clip: float = 2.0 uniform_negatives: int = 24 inbatch_negatives: int = 8 inbatch_correction_power: float = 0.5 inbatch_correction_min: float = 0.25 inbatch_correction_max: float = 4.0 seed: int = 29 @dataclass(frozen=True) class ListenEvents: timestamps: np.ndarray items: np.ndarray organic: np.ndarray completion_pct: np.ndarray @dataclass(frozen=True) class ExplicitEvents: timestamps: np.ndarray items: np.ndarray channel: np.ndarray active: np.ndarray organic_like: np.ndarray def log(message: str) -> None: print(message, file=sys.stderr, flush=True) def build_item_features(catalog: pd.DataFrame) -> np.ndarray: """Return four standardized features built only from the public catalog.""" positives = catalog.positive_listens.to_numpy(dtype=np.float64, copy=False) organic = catalog.organic_positive_listens.to_numpy(dtype=np.float64, copy=False) popularity_rank = catalog.popularity_rank.to_numpy(dtype=np.float64, copy=False) features = np.column_stack( [ np.log1p(positives), np.log1p(organic), organic / np.maximum(positives, 1.0), 1.0 - (popularity_rank - 1.0) / max(len(catalog) - 1, 1), ] ) means = features.mean(axis=0, keepdims=True) scales = np.maximum(features.std(axis=0, keepdims=True), 1.0e-6) standardized = ((features - means) / scales).astype(np.float32) if not np.isfinite(standardized).all(): raise RuntimeError("catalog features contain non-finite values") return standardized def _combine_listen_chunks(parts: list[ListenEvents]) -> ListenEvents: if len(parts) == 1: return parts[0] timestamps = np.concatenate([part.timestamps for part in parts]) items = np.concatenate([part.items for part in parts]) organic = np.concatenate([part.organic for part in parts]) completion = np.concatenate([part.completion_pct for part in parts]) order = np.argsort(timestamps, kind="stable") return ListenEvents( timestamps=timestamps[order], items=items[order], organic=organic[order], completion_pct=completion[order], ) def iter_listen_users( path: Path, item_index: pd.Index ) -> Iterator[tuple[int, ListenEvents]]: """Stream one uid-sorted public listen file one user at a time.""" parquet = pq.ParquetFile(path) pending_uid: int | None = None pending: list[ListenEvents] = [] previous_uid = -1 for batch in parquet.iter_batches( batch_size=750_000, columns=["uid", "timestamp", "item_id", "is_organic", "played_ratio_pct"], ): frame = batch.to_pandas() mapped = item_index.get_indexer(frame.item_id.to_numpy(dtype=np.int64)) keep = mapped >= 0 if not np.any(keep): continue uids = frame.uid.to_numpy(dtype=np.int64, copy=False)[keep] timestamps = frame.timestamp.to_numpy(dtype=np.int64, copy=False)[keep] items = mapped[keep].astype(np.int32, copy=False) organic = frame.is_organic.to_numpy(dtype=np.uint8, copy=False)[keep] completion = frame.played_ratio_pct.to_numpy(dtype=np.uint16, copy=False)[keep] if np.any(uids[1:] < uids[:-1]): raise RuntimeError(f"{path.name} is not sorted by uid") boundaries = np.r_[ 0, np.flatnonzero(uids[1:] != uids[:-1]) + 1, len(uids), ] for start, end in zip(boundaries[:-1], boundaries[1:]): uid = int(uids[start]) part = ListenEvents( timestamps[start:end].copy(), items[start:end].copy(), organic[start:end].copy(), completion[start:end].copy(), ) if pending_uid is None: pending_uid = uid elif uid != pending_uid: if pending_uid < previous_uid: raise RuntimeError(f"{path.name} user order regressed") yield pending_uid, _combine_listen_chunks(pending) previous_uid = pending_uid pending_uid = uid pending = [] pending.append(part) if pending_uid is not None: yield pending_uid, _combine_listen_chunks(pending) def merge_listen_streams( paths: list[Path], item_index: pd.Index ) -> Iterator[tuple[int, ListenEvents]]: """Merge uid-sorted public listen files without materializing them.""" streams = [iter(iter_listen_users(path, item_index)) for path in paths] heads: list[tuple[int, ListenEvents] | None] = [ next(stream, None) for stream in streams ] while any(head is not None for head in heads): uid = min(head[0] for head in heads if head is not None) parts: list[ListenEvents] = [] for position, head in enumerate(heads): if head is not None and head[0] == uid: parts.append(head[1]) heads[position] = next(streams[position], None) yield uid, _combine_listen_chunks(parts) def load_explicit_events( data: Path, prefixes: tuple[str, ...], item_index: pd.Index, ) -> dict[int, ExplicitEvents]: """Load public explicit actions as chronological state changes.""" specifications = ( ("likes", 0, 1, 0), ("unlikes", 0, 0, 1), ("dislikes", 1, 1, 0), ("undislikes", 1, 0, 1), ) parts: list[pd.DataFrame] = [] for family, channel, active, tie_priority in specifications: for prefix in prefixes: frame = pd.read_parquet( data / f"{prefix}_{family}.parquet", columns=["uid", "timestamp", "item_id", "is_organic"], ) mapped = item_index.get_indexer( frame.item_id.to_numpy(dtype=np.int64, copy=False) ) keep = mapped >= 0 if not np.any(keep): continue retained = frame.loc[keep, ["uid", "timestamp", "is_organic"]].copy() retained["item"] = mapped[keep].astype(np.int32, copy=False) retained["channel"] = np.int8(channel) retained["active"] = np.int8(active) retained["tie_priority"] = np.int8(tie_priority) retained["organic_like"] = np.int8( family == "likes" ) * retained.is_organic.astype(np.int8) parts.append(retained.drop(columns="is_organic")) if not parts: return {} events = pd.concat(parts, ignore_index=True) events = events.sort_values( ["uid", "timestamp", "tie_priority"], kind="mergesort" ).reset_index(drop=True) result: dict[int, ExplicitEvents] = {} for uid, rows in events.groupby("uid", sort=False): result[int(uid)] = ExplicitEvents( timestamps=rows.timestamp.to_numpy(dtype=np.int64, copy=True), items=rows.item.to_numpy(dtype=np.int32, copy=True), channel=rows.channel.to_numpy(dtype=np.int8, copy=True), active=rows.active.to_numpy(dtype=np.int8, copy=True), organic_like=rows.organic_like.to_numpy(dtype=np.int8, copy=True), ) log(f"loaded explicit events: rows={len(events):,} users={len(result):,}") return result def empty_explicit_events() -> ExplicitEvents: return ExplicitEvents( timestamps=np.empty(0, dtype=np.int64), items=np.empty(0, dtype=np.int32), channel=np.empty(0, dtype=np.int8), active=np.empty(0, dtype=np.int8), organic_like=np.empty(0, dtype=np.int8), ) def pack_active_items( active: dict[int, int], width: int, padding_item: int ) -> np.ndarray: packed = np.full(width, padding_item, dtype=np.uint16) if not active: return packed ordered = sorted(active.items(), key=lambda pair: (-pair[1], pair[0]))[:width] packed[: len(ordered)] = np.fromiter( (item for item, _ in ordered), dtype=np.uint16, count=len(ordered) ) return packed def load_inference_state( path: Path, item_index: pd.Index, uids: np.ndarray, n_items: int, ) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]: """Pack the task-provided active explicit state.""" state = pd.read_parquet(path, columns=["uid", "item_id", "state"]) mapped = item_index.get_indexer(state.item_id.to_numpy(dtype=np.int64)) keep = mapped >= 0 state = state.loc[keep, ["uid", "state"]].copy() state["item"] = mapped[keep].astype(np.int32, copy=False) user_rows = pd.Index(uids) liked = np.full((len(uids), LIKE_ITEMS), n_items, dtype=np.uint16) disliked = np.full((len(uids), DISLIKE_ITEMS), n_items, dtype=np.uint16) like_counts = np.zeros(len(uids), dtype=np.int32) dislike_counts = np.zeros(len(uids), dtype=np.int32) for value, width, destination, counts in ( ("liked", LIKE_ITEMS, liked, like_counts), ("disliked", DISLIKE_ITEMS, disliked, dislike_counts), ): selected = state[state.state == value] for uid, rows in selected.groupby("uid", sort=False): row = int(user_rows.get_indexer([int(uid)])[0]) if row < 0: continue items = np.sort(rows.item.unique()) counts[row] = len(items) retained = items[:width] destination[row, : len(retained)] = retained.astype(np.uint16) return liked, disliked, like_counts, dislike_counts class SequentialTwoTower(nn.Module): """Order-sensitive user tower and independent catalog-aware item tower.""" def __init__(self, item_features: np.ndarray, config: TowerConfig) -> None: super().__init__() n_items, n_item_features = item_features.shape dim = config.embedding_dim self.n_items = n_items self.context_embedding = nn.Embedding( n_items + 1, dim, padding_idx=n_items ) self.event_projection = nn.Sequential( nn.Linear(dim + 3, config.hidden_dim), nn.GELU(), ) self.sequence_encoder = nn.GRU( input_size=config.hidden_dim, hidden_size=dim, num_layers=1, batch_first=True, ) self.user_tower = nn.Sequential( nn.Linear(dim * 3 + 6, config.user_hidden_1), nn.GELU(), nn.Dropout(config.dropout), nn.Linear(config.user_hidden_1, config.user_hidden_2), nn.GELU(), nn.Dropout(config.dropout), nn.Linear(config.user_hidden_2, dim), ) self.candidate_embedding = nn.Embedding(n_items, dim) self.register_buffer( "item_features", torch.from_numpy(item_features), persistent=True ) self.item_tower = nn.Sequential( nn.Linear(dim + n_item_features, config.item_hidden_1), nn.GELU(), ### Harness context { "type": "token_usage_record", "thread_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "session_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "root_turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "response_id": "resp_08539f19231b9e9f016ab5a8dbea1887d2be0c4cfbb0e9787f", "usage": { "input_tokens": 14900, "cached_input_tokens": 11136, "cache_write_input_tokens": 0, "output_tokens": 288, "reasoning_output_tokens": 84, "total_tokens": 15188 }, "turn_token_usage": { "input_tokens": 14900, "cached_input_tokens": 11136, "cache_write_input_tokens": 0, "output_tokens": 288, "reasoning_output_tokens": 84, "total_tokens": 15188 }, "thread_token_usage": { "input_tokens": 14900, "cached_input_tokens": 11136, "cache_write_input_tokens": 0, "output_tokens": 288, "reasoning_output_tokens": 84, "total_tokens": 15188 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-b8174fcd-bee3-4503-9117-6754ccb7cc12", "process_id": "50258", "command": [ "/bin/sh", "-lc", "mkdir -p /app/solution && find /task/current_model -maxdepth 2 -type f -printf '%p %k KB\\n' && find /task/data -maxdepth 1 -type f -printf '%f %k KB\\n' | sort && sed -n '1,260p' /task/current_model/train_current_model.py && sed -n '1,320p' /task/current_model/two_tower_model.py" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "mkdir -p /app/solution && find /task/current_model -maxdepth 2 -type f -printf '%p %k KB\\n' && find /task/data -maxdepth 1 -type f -printf '%f %k KB\\n' | sort && sed -n '1,260p' /task/current_model/train_current_model.py && sed -n '1,320p' /task/current_model/two_tower_model.py" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "/task/current_model/train_current_model.py 80 KB\n/task/current_model/two_tower_model.py 16 KB\ncandidate_catalog.parquet 344 KB\ntarget_users.parquet 84 KB\ntest_eligible_candidates.npz 7556 KB\ntest_preference_state.parquet 1068 KB\ntrain_dislikes.parquet 840 KB\ntrain_likes.parquet 5252 KB\ntrain_listens.parquet 311704 KB\ntrain_undislikes.parquet 156 KB\ntrain_unlikes.parquet 1816 KB\nval_dislikes.parquet 36 KB\nval_likes.parquet 292 KB\nval_listens.parquet 9716 KB\nval_undislikes.parquet 8 KB\nval_unlikes.parquet 72 KB\nvalidation_eligible_candidates.npz 7400 KB\nvalidation_preference_state.parquet 1040 KB\n#!/usr/bin/env python3\n\"\"\"Train the Yambda windowed two-tower baseline.\n\nEvery catalog listen with at least 50 percent completion is a sequence token.\nWithin each selected chronological window, every adjacent target contributes a\nloss. The fast default retains each user's most recent 128 training targets and\n16 held-out validation targets; passing zero for either cap restores the\nuncapped behavior for that side of the split. A 100-step truncated-BPTT block\ncomputes all of its timestep losses and carries the detached GRU hidden state\ninto the next block.\n\nThe model is a pure two-tower retriever: no user-ID embedding, no output\nnormalization, raw dot-product retrieval, sample-weighted binary cross entropy,\nand 24 uniform plus eight propensity-corrected unique in-batch negatives. Every\nselected adjacent target retains a nonzero weight. Only files under the supplied\npublic directory are read.\n\"\"\"\nfrom __future__ import annotations\n\nimport argparse\nimport json\nimport random\nimport sys\nimport time\nfrom dataclasses import asdict, dataclass\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\nimport torch\nfrom torch import nn\nfrom torch.nn import functional as F\n\nimport two_tower_model as base\n\n\nCACHE_VERSION = 2\n\n\n@dataclass(frozen=True)\nclass Config:\n bptt_steps: int = 100\n user_batch_size: int = 64\n embedding_dim: int = 192\n hidden_dim: int = 192\n user_hidden_1: int = 512\n user_hidden_2: int = 256\n item_hidden_1: int = 512\n item_hidden_2: int = 256\n dropout: float = 0.05\n epochs: int = 1\n learning_rate: float = 1.0e-3\n weight_decay: float = 1.0e-5\n gradient_clip: float = 2.0\n loss_microbatch_size: int = 65_536\n uniform_negatives: int = 24\n inbatch_negatives: int = 8\n inbatch_correction_power: float = 0.5\n inbatch_correction_min: float = 0.25\n inbatch_correction_max: float = 4.0\n negative_filter_scope: str = \"full-training-history\"\n target_weighting: str = \"organic-strong\"\n ordinary_target_weight: float = 0.25\n organic_strong_target_weight: float = 1.0\n organic_like_target_weight: float = 3.0\n discovery_horizon_days: int = 7\n validation_fraction: float = 0.10\n max_train_targets_per_user: int = 128\n max_validation_targets_per_user: int = 16\n early_stopping_patience: int = 1\n early_stopping_min_delta: float = 1.0e-4\n seed: int = 29\n\n\n@dataclass(frozen=True)\nclass CandidateSet:\n uids: np.ndarray\n item_ids: np.ndarray\n packed: np.ndarray\n\n @classmethod\n def read(cls, path: Path) -> \"CandidateSet\":\n with np.load(path, allow_pickle=False) as artifact:\n result = cls(\n uids=artifact[\"uids\"].astype(np.int64, copy=True),\n item_ids=artifact[\"item_ids\"].astype(np.int64, copy=True),\n packed=artifact[\"packed_eligible\"].astype(np.uint8, copy=True),\n )\n expected = (len(result.uids), (len(result.item_ids) + 7) // 8)\n if result.packed.shape != expected:\n raise ValueError(\n f\"invalid candidate artifact shape {result.packed.shape}; \"\n f\"expected {expected}\"\n )\n return result\n\n\n@dataclass(frozen=True)\nclass SequenceData:\n item_ids: np.ndarray\n item_features: np.ndarray\n user_ids: np.ndarray\n offsets: np.ndarray\n sequence_timestamps: np.ndarray\n sequence_items: np.ndarray\n event_features: np.ndarray\n activity_features: np.ndarray\n state_ids: np.ndarray\n liked_states: np.ndarray\n disliked_states: np.ndarray\n state_counts: np.ndarray\n inference_user_rows: np.ndarray\n inference_liked_items: np.ndarray\n inference_disliked_items: np.ndarray\n inference_user_features: np.ndarray\n\n @property\n def lengths(self) -> np.ndarray:\n return np.diff(self.offsets)\n\n @property\n def target_count(self) -> int:\n return int(np.maximum(self.lengths - 1, 0).sum())\n\n\n@dataclass(frozen=True)\nclass TargetPartition:\n \"\"\"A contiguous recent target window and its chronological BCE split.\"\"\"\n\n window_starts: np.ndarray\n training_counts: np.ndarray\n validation_counts: np.ndarray\n history_training_counts: np.ndarray\n\n @property\n def selected_count(self) -> int:\n return int(self.training_counts.sum() + self.validation_counts.sum())\n\n\ndef log(message: str) -> None:\n print(message, file=sys.stderr, flush=True)\n\n\ndef seed_everything(seed: int) -> None:\n random.seed(seed)\n np.random.seed(seed)\n torch.manual_seed(seed)\n\n\ndef model_config(config: Config) -> base.TowerConfig:\n \"\"\"Map the dense trainer configuration onto the unchanged tower.\"\"\"\n return base.TowerConfig(\n sequence_length=config.bptt_steps,\n embedding_dim=config.embedding_dim,\n hidden_dim=config.hidden_dim,\n user_hidden_1=config.user_hidden_1,\n user_hidden_2=config.user_hidden_2,\n item_hidden_1=config.item_hidden_1,\n item_hidden_2=config.item_hidden_2,\n dropout=config.dropout,\n epochs=config.epochs,\n learning_rate=config.learning_rate,\n weight_decay=config.weight_decay,\n gradient_clip=config.gradient_clip,\n uniform_negatives=config.uniform_negatives,\n inbatch_negatives=config.inbatch_negatives,\n inbatch_correction_power=config.inbatch_correction_power,\n inbatch_correction_min=config.inbatch_correction_min,\n inbatch_correction_max=config.inbatch_correction_max,\n seed=config.seed,\n )\n\n\ndef append_state_snapshot(\n active_likes: dict[int, int],\n active_dislikes: dict[int, int],\n n_items: int,\n liked_states: list[np.ndarray],\n disliked_states: list[np.ndarray],\n state_counts: list[tuple[int, int]],\n) -> int:\n liked_states.append(base.pack_active_items(active_likes, base.LIKE_ITEMS, n_items))\n disliked_states.append(\n base.pack_active_items(active_dislikes, base.DISLIKE_ITEMS, n_items)\n )\n state_counts.append((len(active_likes), len(active_dislikes)))\n return len(liked_states) - 1\n\n\ndef causal_activity_features(\n timestamps: np.ndarray,\n items: np.ndarray,\n organic: np.ndarray,\n completion: np.ndarray,\n) -> np.ndarray:\n \"\"\"Compute the first four reviewed user features at every event cutoff.\"\"\"\n length = len(items)\n result = np.zeros((length, 4), dtype=np.float16)\n if not length:\n return result\n left_rows = np.searchsorted(\n timestamps,\n timestamps - base.PROFILE_LOOKBACK_DAYS * base.DAY,\n side=\"left\",\n )\n rows = np.arange(length, dtype=np.int64)\n counts = rows - left_rows + 1\n organic_sum = np.cumsum(organic.astype(np.float64))\n completion_sum = np.cumsum(np.minimum(completion, 100).astype(np.float64))\n before_organic = np.where(\n left_rows > 0, organic_sum[np.maximum(left_rows - 1, 0)], 0.0\n )\n before_completion = np.where(\n left_rows > 0, completion_sum[np.maximum(left_rows - 1, 0)], 0.0\n )\n\n unique_counts = np.empty(length, dtype=np.int32)\n frequencies: dict[int, int] = {}\n left = 0\n for right, raw_item in enumerate(items):\n item = int(raw_item)\n frequencies[item] = frequencies.get(item, 0) + 1\n required_left = int(left_rows[right])\n while left < required_left:\n old_item = int(items[left])\n remaining = frequencies[old_item] - 1\n if remaining:\n frequencies[old_item] = remaining\n else:\n del frequencies[old_item]\n left += 1\n unique_counts[right] = len(frequencies)\n\n result[:, 0] = (np.log1p(counts) / 8.0).astype(np.float16)\n result[:, 1] = (np.log1p(unique_counts) / 6.0).astype(np.float16)\n result[:, 2] = (\n (organic_sum - before_organic) / np.maximum(counts, 1)\n ).astype(np.float16)\n result[:, 3] = (\n (completion_sum - before_completion)\n / np.maximum(counts, 1)\n / 100.0\n ).astype(np.float16)\n return result\n\n\ndef build_sequence_parts(\n listens: base.ListenEvents,\n explicit: base.ExplicitEvents,\n n_items: int,\n liked_states: list[np.ndarray],\n disliked_states: list[np.ndarray],\n state_counts: list[tuple[int, int]],\n) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray] | None:\n qualifying = listens.completion_pct >= base.LISTEN_POSITIVE_THRESHOLD\n timestamps = listens.timestamps[qualifying].astype(np.int64, copy=False)\n items = listens.items[qualifying].astype(np.uint16, copy=False)\n organic = listens.organic[qualifying].astype(np.float32, copy=False)\n completion = listens.completion_pct[qualifying].astype(np.float32, copy=False)\n if not len(items):\n\"\"\"Shared public-data primitives for the Yambda two-tower baseline.\"\"\"\nfrom __future__ import annotations\n\nimport sys\nfrom dataclasses import dataclass\nfrom pathlib import Path\nfrom typing import Iterator\n\nimport numpy as np\nimport pandas as pd\nimport pyarrow.parquet as pq\nimport torch\nfrom torch import nn\n\n\nDAY = 86_400\nTRAIN_END = 24_786_800\nVALIDATION_END = 25_393_400\nLISTEN_POSITIVE_THRESHOLD = 50\nPROFILE_LOOKBACK_DAYS = 120\nLIKE_ITEMS = 48\nDISLIKE_ITEMS = 24\nTOP_N = 100\n\n\n@dataclass(frozen=True)\nclass TowerConfig:\n sequence_length: int = 100\n embedding_dim: int = 192\n hidden_dim: int = 192\n user_hidden_1: int = 512\n user_hidden_2: int = 256\n item_hidden_1: int = 512\n item_hidden_2: int = 256\n dropout: float = 0.05\n epochs: int = 1\n learning_rate: float = 1.0e-3\n weight_decay: float = 1.0e-5\n gradient_clip: float = 2.0\n uniform_negatives: int = 24\n inbatch_negatives: int = 8\n inbatch_correction_power: float = 0.5\n inbatch_correction_min: float = 0.25\n inbatch_correction_max: float = 4.0\n seed: int = 29\n\n\n@dataclass(frozen=True)\nclass ListenEvents:\n timestamps: np.ndarray\n items: np.ndarray\n organic: np.ndarray\n completion_pct: np.ndarray\n\n\n@dataclass(frozen=True)\nclass ExplicitEvents:\n timestamps: np.ndarray\n items: np.ndarray\n channel: np.ndarray\n active: np.ndarray\n organic_like: np.ndarray\n\n\ndef log(message: str) -> None:\n print(message, file=sys.stderr, flush=True)\n\n\ndef build_item_features(catalog: pd.DataFrame) -> np.ndarray:\n \"\"\"Return four standardized features built only from the public catalog.\"\"\"\n positives = catalog.positive_listens.to_numpy(dtype=np.float64, copy=False)\n organic = catalog.organic_positive_listens.to_numpy(dtype=np.float64, copy=False)\n popularity_rank = catalog.popularity_rank.to_numpy(dtype=np.float64, copy=False)\n features = np.column_stack(\n [\n np.log1p(positives),\n np.log1p(organic),\n organic / np.maximum(positives, 1.0),\n 1.0 - (popularity_rank - 1.0) / max(len(catalog) - 1, 1),\n ]\n )\n means = features.mean(axis=0, keepdims=True)\n scales = np.maximum(features.std(axis=0, keepdims=True), 1.0e-6)\n standardized = ((features - means) / scales).astype(np.float32)\n if not np.isfinite(standardized).all():\n raise RuntimeError(\"catalog features contain non-finite values\")\n return standardized\n\n\ndef _combine_listen_chunks(parts: list[ListenEvents]) -> ListenEvents:\n if len(parts) == 1:\n return parts[0]\n timestamps = np.concatenate([part.timestamps for part in parts])\n items = np.concatenate([part.items for part in parts])\n organic = np.concatenate([part.organic for part in parts])\n completion = np.concatenate([part.completion_pct for part in parts])\n order = np.argsort(timestamps, kind=\"stable\")\n return ListenEvents(\n timestamps=timestamps[order],\n items=items[order],\n organic=organic[order],\n completion_pct=completion[order],\n )\n\n\ndef iter_listen_users(\n path: Path, item_index: pd.Index\n) -> Iterator[tuple[int, ListenEvents]]:\n \"\"\"Stream one uid-sorted public listen file one user at a time.\"\"\"\n parquet = pq.ParquetFile(path)\n pending_uid: int | None = None\n pending: list[ListenEvents] = []\n previous_uid = -1\n for batch in parquet.iter_batches(\n batch_size=750_000,\n columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"],\n ):\n frame = batch.to_pandas()\n mapped = item_index.get_indexer(frame.item_id.to_numpy(dtype=np.int64))\n keep = mapped >= 0\n if not np.any(keep):\n continue\n uids = frame.uid.to_numpy(dtype=np.int64, copy=False)[keep]\n timestamps = frame.timestamp.to_numpy(dtype=np.int64, copy=False)[keep]\n items = mapped[keep].astype(np.int32, copy=False)\n organic = frame.is_organic.to_numpy(dtype=np.uint8, copy=False)[keep]\n completion = frame.played_ratio_pct.to_numpy(dtype=np.uint16, copy=False)[keep]\n if np.any(uids[1:] < uids[:-1]):\n raise RuntimeError(f\"{path.name} is not sorted by uid\")\n boundaries = np.r_[\n 0,\n np.flatnonzero(uids[1:] != uids[:-1]) + 1,\n len(uids),\n ]\n for start, end in zip(boundaries[:-1], boundaries[1:]):\n uid = int(uids[start])\n part = ListenEvents(\n timestamps[start:end].copy(),\n items[start:end].copy(),\n organic[start:end].copy(),\n completion[start:end].copy(),\n )\n if pending_uid is None:\n pending_uid = uid\n elif uid != pending_uid:\n if pending_uid < previous_uid:\n raise RuntimeError(f\"{path.name} user order regressed\")\n yield pending_uid, _combine_listen_chunks(pending)\n previous_uid = pending_uid\n pending_uid = uid\n pending = []\n pending.append(part)\n if pending_uid is not None:\n yield pending_uid, _combine_listen_chunks(pending)\n\n\ndef merge_listen_streams(\n paths: list[Path], item_index: pd.Index\n) -> Iterator[tuple[int, ListenEvents]]:\n \"\"\"Merge uid-sorted public listen files without materializing them.\"\"\"\n streams = [iter(iter_listen_users(path, item_index)) for path in paths]\n heads: list[tuple[int, ListenEvents] | None] = [\n next(stream, None) for stream in streams\n ]\n while any(head is not None for head in heads):\n uid = min(head[0] for head in heads if head is not None)\n parts: list[ListenEvents] = []\n for position, head in enumerate(heads):\n if head is not None and head[0] == uid:\n parts.append(head[1])\n heads[position] = next(streams[position], None)\n yield uid, _combine_listen_chunks(parts)\n\n\ndef load_explicit_events(\n data: Path,\n prefixes: tuple[str, ...],\n item_index: pd.Index,\n) -> dict[int, ExplicitEvents]:\n \"\"\"Load public explicit actions as chronological state changes.\"\"\"\n specifications = (\n (\"likes\", 0, 1, 0),\n (\"unlikes\", 0, 0, 1),\n (\"dislikes\", 1, 1, 0),\n (\"undislikes\", 1, 0, 1),\n )\n parts: list[pd.DataFrame] = []\n for family, channel, active, tie_priority in specifications:\n for prefix in prefixes:\n frame = pd.read_parquet(\n data / f\"{prefix}_{family}.parquet\",\n columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\"],\n )\n mapped = item_index.get_indexer(\n frame.item_id.to_numpy(dtype=np.int64, copy=False)\n )\n keep = mapped >= 0\n if not np.any(keep):\n continue\n retained = frame.loc[keep, [\"uid\", \"timestamp\", \"is_organic\"]].copy()\n retained[\"item\"] = mapped[keep].astype(np.int32, copy=False)\n retained[\"channel\"] = np.int8(channel)\n retained[\"active\"] = np.int8(active)\n retained[\"tie_priority\"] = np.int8(tie_priority)\n retained[\"organic_like\"] = np.int8(\n family == \"likes\"\n ) * retained.is_organic.astype(np.int8)\n parts.append(retained.drop(columns=\"is_organic\"))\n if not parts:\n return {}\n events = pd.concat(parts, ignore_index=True)\n events = events.sort_values(\n [\"uid\", \"timestamp\", \"tie_priority\"], kind=\"mergesort\"\n ).reset_index(drop=True)\n result: dict[int, ExplicitEvents] = {}\n for uid, rows in events.groupby(\"uid\", sort=False):\n result[int(uid)] = ExplicitEvents(\n timestamps=rows.timestamp.to_numpy(dtype=np.int64, copy=True),\n items=rows.item.to_numpy(dtype=np.int32, copy=True),\n channel=rows.channel.to_numpy(dtype=np.int8, copy=True),\n active=rows.active.to_numpy(dtype=np.int8, copy=True),\n organic_like=rows.organic_like.to_numpy(dtype=np.int8, copy=True),\n )\n log(f\"loaded explicit events: rows={len(events):,} users={len(result):,}\")\n return result\n\n\ndef empty_explicit_events() -> ExplicitEvents:\n return ExplicitEvents(\n timestamps=np.empty(0, dtype=np.int64),\n items=np.empty(0, dtype=np.int32),\n channel=np.empty(0, dtype=np.int8),\n active=np.empty(0, dtype=np.int8),\n organic_like=np.empty(0, dtype=np.int8),\n )\n\n\ndef pack_active_items(\n active: dict[int, int], width: int, padding_item: int\n) -> np.ndarray:\n packed = np.full(width, padding_item, dtype=np.uint16)\n if not active:\n return packed\n ordered = sorted(active.items(), key=lambda pair: (-pair[1], pair[0]))[:width]\n packed[: len(ordered)] = np.fromiter(\n (item for item, _ in ordered), dtype=np.uint16, count=len(ordered)\n )\n return packed\n\n\ndef load_inference_state(\n path: Path,\n item_index: pd.Index,\n uids: np.ndarray,\n n_items: int,\n) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:\n \"\"\"Pack the task-provided active explicit state.\"\"\"\n state = pd.read_parquet(path, columns=[\"uid\", \"item_id\", \"state\"])\n mapped = item_index.get_indexer(state.item_id.to_numpy(dtype=np.int64))\n keep = mapped >= 0\n state = state.loc[keep, [\"uid\", \"state\"]].copy()\n state[\"item\"] = mapped[keep].astype(np.int32, copy=False)\n user_rows = pd.Index(uids)\n liked = np.full((len(uids), LIKE_ITEMS), n_items, dtype=np.uint16)\n disliked = np.full((len(uids), DISLIKE_ITEMS), n_items, dtype=np.uint16)\n like_counts = np.zeros(len(uids), dtype=np.int32)\n dislike_counts = np.zeros(len(uids), dtype=np.int32)\n for value, width, destination, counts in (\n (\"liked\", LIKE_ITEMS, liked, like_counts),\n (\"disliked\", DISLIKE_ITEMS, disliked, dislike_counts),\n ):\n selected = state[state.state == value]\n for uid, rows in selected.groupby(\"uid\", sort=False):\n row = int(user_rows.get_indexer([int(uid)])[0])\n if row < 0:\n continue\n items = np.sort(rows.item.unique())\n counts[row] = len(items)\n retained = items[:width]\n destination[row, : len(retained)] = retained.astype(np.uint16)\n return liked, disliked, like_counts, dislike_counts\n\n\nclass SequentialTwoTower(nn.Module):\n \"\"\"Order-sensitive user tower and independent catalog-aware item tower.\"\"\"\n\n def __init__(self, item_features: np.ndarray, config: TowerConfig) -> None:\n super().__init__()\n n_items, n_item_features = item_features.shape\n dim = config.embedding_dim\n self.n_items = n_items\n self.context_embedding = nn.Embedding(\n n_items + 1, dim, padding_idx=n_items\n )\n self.event_projection = nn.Sequential(\n nn.Linear(dim + 3, config.hidden_dim),\n nn.GELU(),\n )\n self.sequence_encoder = nn.GRU(\n input_size=config.hidden_dim,\n hidden_size=dim,\n num_layers=1,\n batch_first=True,\n )\n self.user_tower = nn.Sequential(\n nn.Linear(dim * 3 + 6, config.user_hidden_1),\n nn.GELU(),\n nn.Dropout(config.dropout),\n nn.Linear(config.user_hidden_1, config.user_hidden_2),\n nn.GELU(),\n nn.Dropout(config.dropout),\n nn.Linear(config.user_hidden_2, dim),\n )\n self.candidate_embedding = nn.Embedding(n_items, dim)\n self.register_buffer(\n \"item_features\", torch.from_numpy(item_features), persistent=True\n )\n self.item_tower = nn.Sequential(\n nn.Linear(dim + n_item_features, config.item_hidden_1),\n nn.GELU(),\n", "stderr": "", "aggregated_output": "/task/current_model/train_current_model.py 80 KB\n/task/current_model/two_tower_model.py 16 KB\ncandidate_catalog.parquet 344 KB\ntarget_users.parquet 84 KB\ntest_eligible_candidates.npz 7556 KB\ntest_preference_state.parquet 1068 KB\ntrain_dislikes.parquet 840 KB\ntrain_likes.parquet 5252 KB\ntrain_listens.parquet 311704 KB\ntrain_undislikes.parquet 156 KB\ntrain_unlikes.parquet 1816 KB\nval_dislikes.parquet 36 KB\nval_likes.parquet 292 KB\nval_listens.parquet 9716 KB\nval_undislikes.parquet 8 KB\nval_unlikes.parquet 72 KB\nvalidation_eligible_candidates.npz 7400 KB\nvalidation_preference_state.parquet 1040 KB\n#!/usr/bin/env python3\n\"\"\"Train the Yambda windowed two-tower baseline.\n\nEvery catalog listen with at least 50 percent completion is a sequence token.\nWithin each selected chronological window, every adjacent target contributes a\nloss. The fast default retains each user's most recent 128 training targets and\n16 held-out validation targets; passing zero for either cap restores the\nuncapped behavior for that side of the split. A 100-step truncated-BPTT block\ncomputes all of its timestep losses and carries the detached GRU hidden state\ninto the next block.\n\nThe model is a pure two-tower retriever: no user-ID embedding, no output\nnormalization, raw dot-product retrieval, sample-weighted binary cross entropy,\nand 24 uniform plus eight propensity-corrected unique in-batch negatives. Every\nselected adjacent target retains a nonzero weight. Only files under the supplied\npublic directory are read.\n\"\"\"\nfrom __future__ import annotations\n\nimport argparse\nimport json\nimport random\nimport sys\nimport time\nfrom dataclasses import asdict, dataclass\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\nimport torch\nfrom torch import nn\nfrom torch.nn import functional as F\n\nimport two_tower_model as base\n\n\nCACHE_VERSION = 2\n\n\n@dataclass(frozen=True)\nclass Config:\n bptt_steps: int = 100\n user_batch_size: int = 64\n embedding_dim: int = 192\n hidden_dim: int = 192\n user_hidden_1: int = 512\n user_hidden_2: int = 256\n item_hidden_1: int = 512\n item_hidden_2: int = 256\n dropout: float = 0.05\n epochs: int = 1\n learning_rate: float = 1.0e-3\n weight_decay: float = 1.0e-5\n gradient_clip: float = 2.0\n loss_microbatch_size: int = 65_536\n uniform_negatives: int = 24\n inbatch_negatives: int = 8\n inbatch_correction_power: float = 0.5\n inbatch_correction_min: float = 0.25\n inbatch_correction_max: float = 4.0\n negative_filter_scope: str = \"full-training-history\"\n target_weighting: str = \"organic-strong\"\n ordinary_target_weight: float = 0.25\n organic_strong_target_weight: float = 1.0\n organic_like_target_weight: float = 3.0\n discovery_horizon_days: int = 7\n validation_fraction: float = 0.10\n max_train_targets_per_user: int = 128\n max_validation_targets_per_user: int = 16\n early_stopping_patience: int = 1\n early_stopping_min_delta: float = 1.0e-4\n seed: int = 29\n\n\n@dataclass(frozen=True)\nclass CandidateSet:\n uids: np.ndarray\n item_ids: np.ndarray\n packed: np.ndarray\n\n @classmethod\n def read(cls, path: Path) -> \"CandidateSet\":\n with np.load(path, allow_pickle=False) as artifact:\n result = cls(\n uids=artifact[\"uids\"].astype(np.int64, copy=True),\n item_ids=artifact[\"item_ids\"].astype(np.int64, copy=True),\n packed=artifact[\"packed_eligible\"].astype(np.uint8, copy=True),\n )\n expected = (len(result.uids), (len(result.item_ids) + 7) // 8)\n if result.packed.shape != expected:\n raise ValueError(\n f\"invalid candidate artifact shape {result.packed.shape}; \"\n f\"expected {expected}\"\n )\n return result\n\n\n@dataclass(frozen=True)\nclass SequenceData:\n item_ids: np.ndarray\n item_features: np.ndarray\n user_ids: np.ndarray\n offsets: np.ndarray\n sequence_timestamps: np.ndarray\n sequence_items: np.ndarray\n event_features: np.ndarray\n activity_features: np.ndarray\n state_ids: np.ndarray\n liked_states: np.ndarray\n disliked_states: np.ndarray\n state_counts: np.ndarray\n inference_user_rows: np.ndarray\n inference_liked_items: np.ndarray\n inference_disliked_items: np.ndarray\n inference_user_features: np.ndarray\n\n @property\n def lengths(self) -> np.ndarray:\n return np.diff(self.offsets)\n\n @property\n def target_count(self) -> int:\n return int(np.maximum(self.lengths - 1, 0).sum())\n\n\n@dataclass(frozen=True)\nclass TargetPartition:\n \"\"\"A contiguous recent target window and its chronological BCE split.\"\"\"\n\n window_starts: np.ndarray\n training_counts: np.ndarray\n validation_counts: np.ndarray\n history_training_counts: np.ndarray\n\n @property\n def selected_count(self) -> int:\n return int(self.training_counts.sum() + self.validation_counts.sum())\n\n\ndef log(message: str) -> None:\n print(message, file=sys.stderr, flush=True)\n\n\ndef seed_everything(seed: int) -> None:\n random.seed(seed)\n np.random.seed(seed)\n torch.manual_seed(seed)\n\n\ndef model_config(config: Config) -> base.TowerConfig:\n \"\"\"Map the dense trainer configuration onto the unchanged tower.\"\"\"\n return base.TowerConfig(\n sequence_length=config.bptt_steps,\n embedding_dim=config.embedding_dim,\n hidden_dim=config.hidden_dim,\n user_hidden_1=config.user_hidden_1,\n user_hidden_2=config.user_hidden_2,\n item_hidden_1=config.item_hidden_1,\n item_hidden_2=config.item_hidden_2,\n dropout=config.dropout,\n epochs=config.epochs,\n learning_rate=config.learning_rate,\n weight_decay=config.weight_decay,\n gradient_clip=config.gradient_clip,\n uniform_negatives=config.uniform_negatives,\n inbatch_negatives=config.inbatch_negatives,\n inbatch_correction_power=config.inbatch_correction_power,\n inbatch_correction_min=config.inbatch_correction_min,\n inbatch_correction_max=config.inbatch_correction_max,\n seed=config.seed,\n )\n\n\ndef append_state_snapshot(\n active_likes: dict[int, int],\n active_dislikes: dict[int, int],\n n_items: int,\n liked_states: list[np.ndarray],\n disliked_states: list[np.ndarray],\n state_counts: list[tuple[int, int]],\n) -> int:\n liked_states.append(base.pack_active_items(active_likes, base.LIKE_ITEMS, n_items))\n disliked_states.append(\n base.pack_active_items(active_dislikes, base.DISLIKE_ITEMS, n_items)\n )\n state_counts.append((len(active_likes), len(active_dislikes)))\n return len(liked_states) - 1\n\n\ndef causal_activity_features(\n timestamps: np.ndarray,\n items: np.ndarray,\n organic: np.ndarray,\n completion: np.ndarray,\n) -> np.ndarray:\n \"\"\"Compute the first four reviewed user features at every event cutoff.\"\"\"\n length = len(items)\n result = np.zeros((length, 4), dtype=np.float16)\n if not length:\n return result\n left_rows = np.searchsorted(\n timestamps,\n timestamps - base.PROFILE_LOOKBACK_DAYS * base.DAY,\n side=\"left\",\n )\n rows = np.arange(length, dtype=np.int64)\n counts = rows - left_rows + 1\n organic_sum = np.cumsum(organic.astype(np.float64))\n completion_sum = np.cumsum(np.minimum(completion, 100).astype(np.float64))\n before_organic = np.where(\n left_rows > 0, organic_sum[np.maximum(left_rows - 1, 0)], 0.0\n )\n before_completion = np.where(\n left_rows > 0, completion_sum[np.maximum(left_rows - 1, 0)], 0.0\n )\n\n unique_counts = np.empty(length, dtype=np.int32)\n frequencies: dict[int, int] = {}\n left = 0\n for right, raw_item in enumerate(items):\n item = int(raw_item)\n frequencies[item] = frequencies.get(item, 0) + 1\n required_left = int(left_rows[right])\n while left < required_left:\n old_item = int(items[left])\n remaining = frequencies[old_item] - 1\n if remaining:\n frequencies[old_item] = remaining\n else:\n del frequencies[old_item]\n left += 1\n unique_counts[right] = len(frequencies)\n\n result[:, 0] = (np.log1p(counts) / 8.0).astype(np.float16)\n result[:, 1] = (np.log1p(unique_counts) / 6.0).astype(np.float16)\n result[:, 2] = (\n (organic_sum - before_organic) / np.maximum(counts, 1)\n ).astype(np.float16)\n result[:, 3] = (\n (completion_sum - before_completion)\n / np.maximum(counts, 1)\n / 100.0\n ).astype(np.float16)\n return result\n\n\ndef build_sequence_parts(\n listens: base.ListenEvents,\n explicit: base.ExplicitEvents,\n n_items: int,\n liked_states: list[np.ndarray],\n disliked_states: list[np.ndarray],\n state_counts: list[tuple[int, int]],\n) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray] | None:\n qualifying = listens.completion_pct >= base.LISTEN_POSITIVE_THRESHOLD\n timestamps = listens.timestamps[qualifying].astype(np.int64, copy=False)\n items = listens.items[qualifying].astype(np.uint16, copy=False)\n organic = listens.organic[qualifying].astype(np.float32, copy=False)\n completion = listens.completion_pct[qualifying].astype(np.float32, copy=False)\n if not len(items):\n\"\"\"Shared public-data primitives for the Yambda two-tower baseline.\"\"\"\nfrom __future__ import annotations\n\nimport sys\nfrom dataclasses import dataclass\nfrom pathlib import Path\nfrom typing import Iterator\n\nimport numpy as np\nimport pandas as pd\nimport pyarrow.parquet as pq\nimport torch\nfrom torch import nn\n\n\nDAY = 86_400\nTRAIN_END = 24_786_800\nVALIDATION_END = 25_393_400\nLISTEN_POSITIVE_THRESHOLD = 50\nPROFILE_LOOKBACK_DAYS = 120\nLIKE_ITEMS = 48\nDISLIKE_ITEMS = 24\nTOP_N = 100\n\n\n@dataclass(frozen=True)\nclass TowerConfig:\n sequence_length: int = 100\n embedding_dim: int = 192\n hidden_dim: int = 192\n user_hidden_1: int = 512\n user_hidden_2: int = 256\n item_hidden_1: int = 512\n item_hidden_2: int = 256\n dropout: float = 0.05\n epochs: int = 1\n learning_rate: float = 1.0e-3\n weight_decay: float = 1.0e-5\n gradient_clip: float = 2.0\n uniform_negatives: int = 24\n inbatch_negatives: int = 8\n inbatch_correction_power: float = 0.5\n inbatch_correction_min: float = 0.25\n inbatch_correction_max: float = 4.0\n seed: int = 29\n\n\n@dataclass(frozen=True)\nclass ListenEvents:\n timestamps: np.ndarray\n items: np.ndarray\n organic: np.ndarray\n completion_pct: np.ndarray\n\n\n@dataclass(frozen=True)\nclass ExplicitEvents:\n timestamps: np.ndarray\n items: np.ndarray\n channel: np.ndarray\n active: np.ndarray\n organic_like: np.ndarray\n\n\ndef log(message: str) -> None:\n print(message, file=sys.stderr, flush=True)\n\n\ndef build_item_features(catalog: pd.DataFrame) -> np.ndarray:\n \"\"\"Return four standardized features built only from the public catalog.\"\"\"\n positives = catalog.positive_listens.to_numpy(dtype=np.float64, copy=False)\n organic = catalog.organic_positive_listens.to_numpy(dtype=np.float64, copy=False)\n popularity_rank = catalog.popularity_rank.to_numpy(dtype=np.float64, copy=False)\n features = np.column_stack(\n [\n np.log1p(positives),\n np.log1p(organic),\n organic / np.maximum(positives, 1.0),\n 1.0 - (popularity_rank - 1.0) / max(len(catalog) - 1, 1),\n ]\n )\n means = features.mean(axis=0, keepdims=True)\n scales = np.maximum(features.std(axis=0, keepdims=True), 1.0e-6)\n standardized = ((features - means) / scales).astype(np.float32)\n if not np.isfinite(standardized).all():\n raise RuntimeError(\"catalog features contain non-finite values\")\n return standardized\n\n\ndef _combine_listen_chunks(parts: list[ListenEvents]) -> ListenEvents:\n if len(parts) == 1:\n return parts[0]\n timestamps = np.concatenate([part.timestamps for part in parts])\n items = np.concatenate([part.items for part in parts])\n organic = np.concatenate([part.organic for part in parts])\n completion = np.concatenate([part.completion_pct for part in parts])\n order = np.argsort(timestamps, kind=\"stable\")\n return ListenEvents(\n timestamps=timestamps[order],\n items=items[order],\n organic=organic[order],\n completion_pct=completion[order],\n )\n\n\ndef iter_listen_users(\n path: Path, item_index: pd.Index\n) -> Iterator[tuple[int, ListenEvents]]:\n \"\"\"Stream one uid-sorted public listen file one user at a time.\"\"\"\n parquet = pq.ParquetFile(path)\n pending_uid: int | None = None\n pending: list[ListenEvents] = []\n previous_uid = -1\n for batch in parquet.iter_batches(\n batch_size=750_000,\n columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"],\n ):\n frame = batch.to_pandas()\n mapped = item_index.get_indexer(frame.item_id.to_numpy(dtype=np.int64))\n keep = mapped >= 0\n if not np.any(keep):\n continue\n uids = frame.uid.to_numpy(dtype=np.int64, copy=False)[keep]\n timestamps = frame.timestamp.to_numpy(dtype=np.int64, copy=False)[keep]\n items = mapped[keep].astype(np.int32, copy=False)\n organic = frame.is_organic.to_numpy(dtype=np.uint8, copy=False)[keep]\n completion = frame.played_ratio_pct.to_numpy(dtype=np.uint16, copy=False)[keep]\n if np.any(uids[1:] < uids[:-1]):\n raise RuntimeError(f\"{path.name} is not sorted by uid\")\n boundaries = np.r_[\n 0,\n np.flatnonzero(uids[1:] != uids[:-1]) + 1,\n len(uids),\n ]\n for start, end in zip(boundaries[:-1], boundaries[1:]):\n uid = int(uids[start])\n part = ListenEvents(\n timestamps[start:end].copy(),\n items[start:end].copy(),\n organic[start:end].copy(),\n completion[start:end].copy(),\n )\n if pending_uid is None:\n pending_uid = uid\n elif uid != pending_uid:\n if pending_uid < previous_uid:\n raise RuntimeError(f\"{path.name} user order regressed\")\n yield pending_uid, _combine_listen_chunks(pending)\n previous_uid = pending_uid\n pending_uid = uid\n pending = []\n pending.append(part)\n if pending_uid is not None:\n yield pending_uid, _combine_listen_chunks(pending)\n\n\ndef merge_listen_streams(\n paths: list[Path], item_index: pd.Index\n) -> Iterator[tuple[int, ListenEvents]]:\n \"\"\"Merge uid-sorted public listen files without materializing them.\"\"\"\n streams = [iter(iter_listen_users(path, item_index)) for path in paths]\n heads: list[tuple[int, ListenEvents] | None] = [\n next(stream, None) for stream in streams\n ]\n while any(head is not None for head in heads):\n uid = min(head[0] for head in heads if head is not None)\n parts: list[ListenEvents] = []\n for position, head in enumerate(heads):\n if head is not None and head[0] == uid:\n parts.append(head[1])\n heads[position] = next(streams[position], None)\n yield uid, _combine_listen_chunks(parts)\n\n\ndef load_explicit_events(\n data: Path,\n prefixes: tuple[str, ...],\n item_index: pd.Index,\n) -> dict[int, ExplicitEvents]:\n \"\"\"Load public explicit actions as chronological state changes.\"\"\"\n specifications = (\n (\"likes\", 0, 1, 0),\n (\"unlikes\", 0, 0, 1),\n (\"dislikes\", 1, 1, 0),\n (\"undislikes\", 1, 0, 1),\n )\n parts: list[pd.DataFrame] = []\n for family, channel, active, tie_priority in specifications:\n for prefix in prefixes:\n frame = pd.read_parquet(\n data / f\"{prefix}_{family}.parquet\",\n columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\"],\n )\n mapped = item_index.get_indexer(\n frame.item_id.to_numpy(dtype=np.int64, copy=False)\n )\n keep = mapped >= 0\n if not np.any(keep):\n continue\n retained = frame.loc[keep, [\"uid\", \"timestamp\", \"is_organic\"]].copy()\n retained[\"item\"] = mapped[keep].astype(np.int32, copy=False)\n retained[\"channel\"] = np.int8(channel)\n retained[\"active\"] = np.int8(active)\n retained[\"tie_priority\"] = np.int8(tie_priority)\n retained[\"organic_like\"] = np.int8(\n family == \"likes\"\n ) * retained.is_organic.astype(np.int8)\n parts.append(retained.drop(columns=\"is_organic\"))\n if not parts:\n return {}\n events = pd.concat(parts, ignore_index=True)\n events = events.sort_values(\n [\"uid\", \"timestamp\", \"tie_priority\"], kind=\"mergesort\"\n ).reset_index(drop=True)\n result: dict[int, ExplicitEvents] = {}\n for uid, rows in events.groupby(\"uid\", sort=False):\n result[int(uid)] = ExplicitEvents(\n timestamps=rows.timestamp.to_numpy(dtype=np.int64, copy=True),\n items=rows.item.to_numpy(dtype=np.int32, copy=True),\n channel=rows.channel.to_numpy(dtype=np.int8, copy=True),\n active=rows.active.to_numpy(dtype=np.int8, copy=True),\n organic_like=rows.organic_like.to_numpy(dtype=np.int8, copy=True),\n )\n log(f\"loaded explicit events: rows={len(events):,} users={len(result):,}\")\n return result\n\n\ndef empty_explicit_events() -> ExplicitEvents:\n return ExplicitEvents(\n timestamps=np.empty(0, dtype=np.int64),\n items=np.empty(0, dtype=np.int32),\n channel=np.empty(0, dtype=np.int8),\n active=np.empty(0, dtype=np.int8),\n organic_like=np.empty(0, dtype=np.int8),\n )\n\n\ndef pack_active_items(\n active: dict[int, int], width: int, padding_item: int\n) -> np.ndarray:\n packed = np.full(width, padding_item, dtype=np.uint16)\n if not active:\n return packed\n ordered = sorted(active.items(), key=lambda pair: (-pair[1], pair[0]))[:width]\n packed[: len(ordered)] = np.fromiter(\n (item for item, _ in ordered), dtype=np.uint16, count=len(ordered)\n )\n return packed\n\n\ndef load_inference_state(\n path: Path,\n item_index: pd.Index,\n uids: np.ndarray,\n n_items: int,\n) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:\n \"\"\"Pack the task-provided active explicit state.\"\"\"\n state = pd.read_parquet(path, columns=[\"uid\", \"item_id\", \"state\"])\n mapped = item_index.get_indexer(state.item_id.to_numpy(dtype=np.int64))\n keep = mapped >= 0\n state = state.loc[keep, [\"uid\", \"state\"]].copy()\n state[\"item\"] = mapped[keep].astype(np.int32, copy=False)\n user_rows = pd.Index(uids)\n liked = np.full((len(uids), LIKE_ITEMS), n_items, dtype=np.uint16)\n disliked = np.full((len(uids), DISLIKE_ITEMS), n_items, dtype=np.uint16)\n like_counts = np.zeros(len(uids), dtype=np.int32)\n dislike_counts = np.zeros(len(uids), dtype=np.int32)\n for value, width, destination, counts in (\n (\"liked\", LIKE_ITEMS, liked, like_counts),\n (\"disliked\", DISLIKE_ITEMS, disliked, dislike_counts),\n ):\n selected = state[state.state == value]\n for uid, rows in selected.groupby(\"uid\", sort=False):\n row = int(user_rows.get_indexer([int(uid)])[0])\n if row < 0:\n continue\n items = np.sort(rows.item.unique())\n counts[row] = len(items)\n retained = items[:width]\n destination[row, : len(retained)] = retained.astype(np.uint16)\n return liked, disliked, like_counts, dislike_counts\n\n\nclass SequentialTwoTower(nn.Module):\n \"\"\"Order-sensitive user tower and independent catalog-aware item tower.\"\"\"\n\n def __init__(self, item_features: np.ndarray, config: TowerConfig) -> None:\n super().__init__()\n n_items, n_item_features = item_features.shape\n dim = config.embedding_dim\n self.n_items = n_items\n self.context_embedding = nn.Embedding(\n n_items + 1, dim, padding_idx=n_items\n )\n self.event_projection = nn.Sequential(\n nn.Linear(dim + 3, config.hidden_dim),\n nn.GELU(),\n )\n self.sequence_encoder = nn.GRU(\n input_size=config.hidden_dim,\n hidden_size=dim,\n num_layers=1,\n batch_first=True,\n )\n self.user_tower = nn.Sequential(\n nn.Linear(dim * 3 + 6, config.user_hidden_1),\n nn.GELU(),\n nn.Dropout(config.dropout),\n nn.Linear(config.user_hidden_1, config.user_hidden_2),\n nn.GELU(),\n nn.Dropout(config.dropout),\n nn.Linear(config.user_hidden_2, dim),\n )\n self.candidate_embedding = nn.Embedding(n_items, dim)\n self.register_buffer(\n \"item_features\", torch.from_numpy(item_features), persistent=True\n )\n self.item_tower = nn.Sequential(\n nn.Linear(dim + n_item_features, config.item_hidden_1),\n nn.GELU(),\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4820 }, "formatted_output": "/task/current_model/train_current_model.py 80 KB\n/task/current_model/two_tower_model.py 16 KB\ncandidate_catalog.parquet 344 KB\ntarget_users.parquet 84 KB\ntest_eligible_candidates.npz 7556 KB\ntest_preference_state.parquet 1068 KB\ntrain_dislikes.parquet 840 KB\ntrain_likes.parquet 5252 KB\ntrain_listens.parquet 311704 KB\ntrain_undislikes.parquet 156 KB\ntrain_unlikes.parquet 1816 KB\nval_dislikes.parquet 36 KB\nval_likes.parquet 292 KB\nval_listens.parquet 9716 KB\nval_undislikes.parquet 8 KB\nval_unlikes.parquet 72 KB\nvalidation_eligible_candidates.npz 7400 KB\nvalidation_preference_state.parquet 1040 KB\n#!/usr/bin/env python3\n\"\"\"Train the Yambda windowed two-tower baseline.\n\nEvery catalog listen with at least 50 percent completion is a sequence token.\nWithin each selected chronological window, every adjacent target contributes a\nloss. The fast default retains each user's most recent 128 training targets and\n16 held-out validation targets; passing zero for either cap restores the\nuncapped behavior for that side of the split. A 100-step truncated-BPTT block\ncomputes all of its timestep losses and carries the detached GRU hidden state\ninto the next block.\n\nThe model is a pure two-tower retriever: no user-ID embedding, no output\nnormalization, raw dot-product retrieval, sample-weighted binary cross entropy,\nand 24 uniform plus eight propensity-corrected unique in-batch negatives. Every\nselected adjacent target retains a nonzero weight. Only files under the supplied\npublic directory are read.\n\"\"\"\nfrom __future__ import annotations\n\nimport argparse\nimport json\nimport random\nimport sys\nimport time\nfrom dataclasses import asdict, dataclass\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\nimport torch\nfrom torch import nn\nfrom torch.nn import functional as F\n\nimport two_tower_model as base\n\n\nCACHE_VERSION = 2\n\n\n@dataclass(frozen=True)\nclass Config:\n bptt_steps: int = 100\n user_batch_size: int = 64\n embedding_dim: int = 192\n hidden_dim: int = 192\n user_hidden_1: int = 512\n user_hidden_2: int = 256\n item_hidden_1: int = 512\n item_hidden_2: int = 256\n dropout: float = 0.05\n epochs: int = 1\n learning_rate: float = 1.0e-3\n weight_decay: float = 1.0e-5\n gradient_clip: float = 2.0\n loss_microbatch_size: int = 65_536\n uniform_negatives: int = 24\n inbatch_negatives: int = 8\n inbatch_correction_power: float = 0.5\n inbatch_correction_min: float = 0.25\n inbatch_correction_max: float = 4.0\n negative_filter_scope: str = \"full-training-history\"\n target_weighting: str = \"organic-strong\"\n ordinary_target_weight: float = 0.25\n organic_strong_target_weight: float = 1.0\n organic_like_target_weight: float = 3.0\n discovery_horizon_days: int = 7\n validation_fraction: float = 0.10\n max_train_targets_per_user: int = 128\n max_validation_targets_per_user: int = 16\n early_stopping_patience: int = 1\n early_stopping_min_delta: float = 1.0e-4\n seed: int = 29\n\n\n@dataclass(frozen=True)\nclass CandidateSet:\n uids: np.ndarray\n item_ids: np.ndarray\n packed: np.ndarray\n\n @classmethod\n def read(cls, path: Path) -> \"CandidateSet\":\n with np.load(path, allow_pickle=False) as artifact:\n result = cls(\n uids=artifact[\"uids\"].astype(np.int64, copy=True),\n item_ids=artifact[\"item_ids\"].astype(np.int64, copy=True),\n packed=artifact[\"packed_eligible\"].astype(np.uint8, copy=True),\n )\n expected = (len(result.uids), (len(result.item_ids) + 7) // 8)\n if result.packed.shape != expected:\n raise ValueError(\n f\"invalid candidate artifact shape {result.packed.shape}; \"\n f\"expected {expected}\"\n )\n return result\n\n\n@dataclass(frozen=True)\nclass SequenceData:\n item_ids: np.ndarray\n item_features: np.ndarray\n user_ids: np.ndarray\n offsets: np.ndarray\n sequence_timestamps: np.ndarray\n sequence_items: np.ndarray\n event_features: np.ndarray\n activity_features: np.ndarray\n state_ids: np.ndarray\n liked_states: np.ndarray\n disliked_states: np.ndarray\n state_counts: np.ndarray\n inference_user_rows: np.ndarray\n inference_liked_items: np.ndarray\n inference_disliked_items: np.ndarray\n inference_user_features: np.ndarray\n\n @property\n def lengths(self) -> np.ndarray:\n return np.diff(self.offsets)\n\n @property\n def target_count(self) -> int:\n return int(np.maximum(self.lengths - 1, 0).sum())\n\n\n@dataclass(frozen=True)\nclass TargetPartition:\n \"\"\"A contiguous recent target window and its chronological BCE split.\"\"\"\n\n window_starts: np.ndarray\n training_counts: np.ndarray\n validation_counts: np.ndarray\n history_training_counts: np.ndarray\n\n @property\n def selected_count(self) -> int:\n return int(self.training_counts.sum() + self.validation_counts.sum())\n\n\ndef log(message: str) -> None:\n print(message, file=sys.stderr, flush=True)\n\n\ndef seed_everything(seed: int) -> None:\n random.seed(seed)\n np.random.seed(seed)\n torch.manual_seed(seed)\n\n\ndef model_config(config: Config) -> base.TowerConfig:\n \"\"\"Map the dense trainer configuration onto the unchanged tower.\"\"\"\n return base.TowerConfig(\n sequence_length=config.bptt_steps,\n embedding_dim=config.embedding_dim,\n hidden_dim=config.hidden_dim,\n user_hidden_1=config.user_hidden_1,\n user_hidden_2=config.user_hidden_2,\n item_hidden_1=config.item_hidden_1,\n item_hidden_2=config.item_hidden_2,\n dropout=config.dropout,\n epochs=config.epochs,\n learning_rate=config.learning_rate,\n weight_decay=config.weight_decay,\n gradient_clip=config.gradient_clip,\n uniform_negatives=config.uniform_negatives,\n inbatch_negatives=config.inbatch_negatives,\n inbatch_correction_power=config.inbatch_correction_power,\n inbatch_correction_min=config.inbatch_correction_min,\n inbatch_correction_max=config.inbatch_correction_max,\n seed=config.seed,\n )\n\n\ndef append_state_snapshot(\n active_likes: dict[int, int],\n active_dislikes: dict[int, int],\n n_items: int,\n liked_states: list[np.ndarray],\n disliked_states: list[np.ndarray],\n state_counts: list[tuple[int, int]],\n) -> int:\n liked_states.append(base.pack_active_items(active_likes, base.LIKE_ITEMS, n_items))\n disliked_states.append(\n base.pack_active_items(active_dislikes, base.DISLIKE_ITEMS, n_items)\n )\n state_counts.append((len(active_likes), len(active_dislikes)))\n return len(liked_states) - 1\n\n\ndef causal_activity_features(\n timestamps: np.ndarray,\n items: np.ndarray,\n organic: np.ndarray,\n completion: np.ndarray,\n) -> np.ndarray:\n \"\"\"Compute the first four reviewed user features at every event cutoff.\"\"\"\n length = len(items)\n result = np.zeros((length, 4), dtype=np.float16)\n if not length:\n return result\n left_rows = np.searchsorted(\n timestamps,\n timestamps - base.PROFILE_LOOKBACK_DAYS * base.DAY,\n side=\"left\",\n )\n rows = np.arange(length, dtype=np.int64)\n counts = rows - left_rows + 1\n organic_sum = np.cumsum(organic.astype(np.float64))\n completion_sum = np.cumsum(np.minimum(completion, 100).astype(np.float64))\n before_organic = np.where(\n left_rows > 0, organic_sum[np.maximum(left_rows - 1, 0)], 0.0\n )\n before_completion = np.where(\n left_rows > 0, completion_sum[np.maximum(left_rows - 1, 0)], 0.0\n )\n\n unique_counts = np.empty(length, dtype=np.int32)\n frequencies: dict[int, int] = {}\n left = 0\n for right, raw_item in enumerate(items):\n item = int(raw_item)\n frequencies[item] = frequencies.get(item, 0) + 1\n required_left = int(left_rows[right])\n while left < required_left:\n old_item = int(items[left])\n remaining = frequencies[old_item] - 1\n if remaining:\n frequencies[old_item] = remaining\n else:\n del frequencies[old_item]\n left += 1\n unique_counts[right] = len(frequencies)\n\n result[:, 0] = (np.log1p(counts) / 8.0).astype(np.float16)\n result[:, 1] = (np.log1p(unique_counts) / 6.0).astype(np.float16)\n result[:, 2] = (\n (organic_sum - before_organic) / np.maximum(counts, 1)\n ).astype(np.float16)\n result[:, 3] = (\n (completion_sum - before_completion)\n / np.maximum(counts, 1)\n / 100.0\n ).astype(np.float16)\n return result\n\n\ndef build_sequence_parts(\n listens: base.ListenEvents,\n explicit: base.ExplicitEvents,\n n_items: int,\n liked_states: list[np.ndarray],\n disliked_states: list[np.ndarray],\n state_counts: list[tuple[int, int]],\n) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray] | None:\n qualifying = listens.completion_pct >= base.LISTEN_POSITIVE_THRESHOLD\n timestamps = listens.timestamps[qualifying].astype(np.int64, copy=False)\n items = listens.items[qualifying].astype(np.uint16, copy=False)\n organic = listens.organic[qualifying].astype(np.float32, copy=False)\n completion = listens.completion_pct[qualifying].astype(np.float32, copy=False)\n if not len(items):\n\"\"\"Shared public-data primitives for the Yambda two-tower baseline.\"\"\"\nfrom __future__ import annotations\n\nimport sys\nfrom dataclasses import dataclass\nfrom pathlib import Path\nfrom typing import Iterator\n\nimport numpy as np\nimport pandas as pd\nimport pyarrow.parquet as pq\nimport torch\nfrom torch import nn\n\n\nDAY = 86_400\nTRAIN_END = 24_786_800\nVALIDATION_END = 25_393_400\nLISTEN_POSITIVE_THRESHOLD = 50\nPROFILE_LOOKBACK_DAYS = 120\nLIKE_ITEMS = 48\nDISLIKE_ITEMS = 24\nTOP_N = 100\n\n\n@dataclass(frozen=True)\nclass TowerConfig:\n sequence_length: int = 100\n embedding_dim: int = 192\n hidden_dim: int = 192\n user_hidden_1: int = 512\n user_hidden_2: int = 256\n item_hidden_1: int = 512\n item_hidden_2: int = 256\n dropout: float = 0.05\n epochs: int = 1\n learning_rate: float = 1.0e-3\n weight_decay: float = 1.0e-5\n gradient_clip: float = 2.0\n uniform_negatives: int = 24\n inbatch_negatives: int = 8\n inbatch_correction_power: float = 0.5\n inbatch_correction_min: float = 0.25\n inbatch_correction_max: float = 4.0\n seed: int = 29\n\n\n@dataclass(frozen=True)\nclass ListenEvents:\n timestamps: np.ndarray\n items: np.ndarray\n organic: np.ndarray\n completion_pct: np.ndarray\n\n\n@dataclass(frozen=True)\nclass ExplicitEvents:\n timestamps: np.ndarray\n items: np.ndarray\n channel: np.ndarray\n active: np.ndarray\n organic_like: np.ndarray\n\n\ndef log(message: str) -> None:\n print(message, file=sys.stderr, flush=True)\n\n\ndef build_item_features(catalog: pd.DataFrame) -> np.ndarray:\n \"\"\"Return four standardized features built only from the public catalog.\"\"\"\n positives = catalog.positive_listens.to_numpy(dtype=np.float64, copy=False)\n organic = catalog.organic_positive_listens.to_numpy(dtype=np.float64, copy=False)\n popularity_rank = catalog.popularity_rank.to_numpy(dtype=np.float64, copy=False)\n features = np.column_stack(\n [\n np.log1p(positives),\n np.log1p(organic),\n organic / np.maximum(positives, 1.0),\n 1.0 - (popularity_rank - 1.0) / max(len(catalog) - 1, 1),\n ]\n )\n means = features.mean(axis=0, keepdims=True)\n scales = np.maximum(features.std(axis=0, keepdims=True), 1.0e-6)\n standardized = ((features - means) / scales).astype(np.float32)\n if not np.isfinite(standardized).all():\n raise RuntimeError(\"catalog features contain non-finite values\")\n return standardized\n\n\ndef _combine_listen_chunks(parts: list[ListenEvents]) -> ListenEvents:\n if len(parts) == 1:\n return parts[0]\n timestamps = np.concatenate([part.timestamps for part in parts])\n items = np.concatenate([part.items for part in parts])\n organic = np.concatenate([part.organic for part in parts])\n completion = np.concatenate([part.completion_pct for part in parts])\n order = np.argsort(timestamps, kind=\"stable\")\n return ListenEvents(\n timestamps=timestamps[order],\n items=items[order],\n organic=organic[order],\n completion_pct=completion[order],\n )\n\n\ndef iter_listen_users(\n path: Path, item_index: pd.Index\n) -> Iterator[tuple[int, ListenEvents]]:\n \"\"\"Stream one uid-sorted public listen file one user at a time.\"\"\"\n parquet = pq.ParquetFile(path)\n pending_uid: int | None = None\n pending: list[ListenEvents] = []\n previous_uid = -1\n for batch in parquet.iter_batches(\n batch_size=750_000,\n columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"],\n ):\n frame = batch.to_pandas()\n mapped = item_index.get_indexer(frame.item_id.to_numpy(dtype=np.int64))\n keep = mapped >= 0\n if not np.any(keep):\n continue\n uids = frame.uid.to_numpy(dtype=np.int64, copy=False)[keep]\n timestamps = frame.timestamp.to_numpy(dtype=np.int64, copy=False)[keep]\n items = mapped[keep].astype(np.int32, copy=False)\n organic = frame.is_organic.to_numpy(dtype=np.uint8, copy=False)[keep]\n completion = frame.played_ratio_pct.to_numpy(dtype=np.uint16, copy=False)[keep]\n if np.any(uids[1:] < uids[:-1]):\n raise RuntimeError(f\"{path.name} is not sorted by uid\")\n boundaries = np.r_[\n 0,\n np.flatnonzero(uids[1:] != uids[:-1]) + 1,\n len(uids),\n ]\n for start, end in zip(boundaries[:-1], boundaries[1:]):\n uid = int(uids[start])\n part = ListenEvents(\n timestamps[start:end].copy(),\n items[start:end].copy(),\n organic[start:end].copy(),\n completion[start:end].copy(),\n )\n if pending_uid is None:\n pending_uid = uid\n elif uid != pending_uid:\n if pending_uid < previous_uid:\n raise RuntimeError(f\"{path.name} user order regressed\")\n yield pending_uid, _combine_listen_chunks(pending)\n previous_uid = pending_uid\n pending_uid = uid\n pending = []\n pending.append(part)\n if pending_uid is not None:\n yield pending_uid, _combine_listen_chunks(pending)\n\n\ndef merge_listen_streams(\n paths: list[Path], item_index: pd.Index\n) -> Iterator[tuple[int, ListenEvents]]:\n \"\"\"Merge uid-sorted public listen files without materializing them.\"\"\"\n streams = [iter(iter_listen_users(path, item_index)) for path in paths]\n heads: list[tuple[int, ListenEvents] | None] = [\n next(stream, None) for stream in streams\n ]\n while any(head is not None for head in heads):\n uid = min(head[0] for head in heads if head is not None)\n parts: list[ListenEvents] = []\n for position, head in enumerate(heads):\n if head is not None and head[0] == uid:\n parts.append(head[1])\n heads[position] = next(streams[position], None)\n yield uid, _combine_listen_chunks(parts)\n\n\ndef load_explicit_events(\n data: Path,\n prefixes: tuple[str, ...],\n item_index: pd.Index,\n) -> dict[int, ExplicitEvents]:\n \"\"\"Load public explicit actions as chronological state changes.\"\"\"\n specifications = (\n (\"likes\", 0, 1, 0),\n (\"unlikes\", 0, 0, 1),\n (\"dislikes\", 1, 1, 0),\n (\"undislikes\", 1, 0, 1),\n )\n parts: list[pd.DataFrame] = []\n for family, channel, active, tie_priority in specifications:\n for prefix in prefixes:\n frame = pd.read_parquet(\n data / f\"{prefix}_{family}.parquet\",\n columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\"],\n )\n mapped = item_index.get_indexer(\n frame.item_id.to_numpy(dtype=np.int64, copy=False)\n )\n keep = mapped >= 0\n if not np.any(keep):\n continue\n retained = frame.loc[keep, [\"uid\", \"timestamp\", \"is_organic\"]].copy()\n retained[\"item\"] = mapped[keep].astype(np.int32, copy=False)\n retained[\"channel\"] = np.int8(channel)\n retained[\"active\"] = np.int8(active)\n retained[\"tie_priority\"] = np.int8(tie_priority)\n retained[\"organic_like\"] = np.int8(\n family == \"likes\"\n ) * retained.is_organic.astype(np.int8)\n parts.append(retained.drop(columns=\"is_organic\"))\n if not parts:\n return {}\n events = pd.concat(parts, ignore_index=True)\n events = events.sort_values(\n [\"uid\", \"timestamp\", \"tie_priority\"], kind=\"mergesort\"\n ).reset_index(drop=True)\n result: dict[int, ExplicitEvents] = {}\n for uid, rows in events.groupby(\"uid\", sort=False):\n result[int(uid)] = ExplicitEvents(\n timestamps=rows.timestamp.to_numpy(dtype=np.int64, copy=True),\n items=rows.item.to_numpy(dtype=np.int32, copy=True),\n channel=rows.channel.to_numpy(dtype=np.int8, copy=True),\n active=rows.active.to_numpy(dtype=np.int8, copy=True),\n organic_like=rows.organic_like.to_numpy(dtype=np.int8, copy=True),\n )\n log(f\"loaded explicit events: rows={len(events):,} users={len(result):,}\")\n return result\n\n\ndef empty_explicit_events() -> ExplicitEvents:\n return ExplicitEvents(\n timestamps=np.empty(0, dtype=np.int64),\n items=np.empty(0, dtype=np.int32),\n channel=np.empty(0, dtype=np.int8),\n active=np.empty(0, dtype=np.int8),\n organic_like=np.empty(0, dtype=np.int8),\n )\n\n\ndef pack_active_items(\n active: dict[int, int], width: int, padding_item: int\n) -> np.ndarray:\n packed = np.full(width, padding_item, dtype=np.uint16)\n if not active:\n return packed\n ordered = sorted(active.items(), key=lambda pair: (-pair[1], pair[0]))[:width]\n packed[: len(ordered)] = np.fromiter(\n (item for item, _ in ordered), dtype=np.uint16, count=len(ordered)\n )\n return packed\n\n\ndef load_inference_state(\n path: Path,\n item_index: pd.Index,\n uids: np.ndarray,\n n_items: int,\n) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:\n \"\"\"Pack the task-provided active explicit state.\"\"\"\n state = pd.read_parquet(path, columns=[\"uid\", \"item_id\", \"state\"])\n mapped = item_index.get_indexer(state.item_id.to_numpy(dtype=np.int64))\n keep = mapped >= 0\n state = state.loc[keep, [\"uid\", \"state\"]].copy()\n state[\"item\"] = mapped[keep].astype(np.int32, copy=False)\n user_rows = pd.Index(uids)\n liked = np.full((len(uids), LIKE_ITEMS), n_items, dtype=np.uint16)\n disliked = np.full((len(uids), DISLIKE_ITEMS), n_items, dtype=np.uint16)\n like_counts = np.zeros(len(uids), dtype=np.int32)\n dislike_counts = np.zeros(len(uids), dtype=np.int32)\n for value, width, destination, counts in (\n (\"liked\", LIKE_ITEMS, liked, like_counts),\n (\"disliked\", DISLIKE_ITEMS, disliked, dislike_counts),\n ):\n selected = state[state.state == value]\n for uid, rows in selected.groupby(\"uid\", sort=False):\n row = int(user_rows.get_indexer([int(uid)])[0])\n if row < 0:\n continue\n items = np.sort(rows.item.unique())\n counts[row] = len(items)\n retained = items[:width]\n destination[row, : len(retained)] = retained.astype(np.uint16)\n return liked, disliked, like_counts, dislike_counts\n\n\nclass SequentialTwoTower(nn.Module):\n \"\"\"Order-sensitive user tower and independent catalog-aware item tower.\"\"\"\n\n def __init__(self, item_features: np.ndarray, config: TowerConfig) -> None:\n super().__init__()\n n_items, n_item_features = item_features.shape\n dim = config.embedding_dim\n self.n_items = n_items\n self.context_embedding = nn.Embedding(\n n_items + 1, dim, padding_idx=n_items\n )\n self.event_projection = nn.Sequential(\n nn.Linear(dim + 3, config.hidden_dim),\n nn.GELU(),\n )\n self.sequence_encoder = nn.GRU(\n input_size=config.hidden_dim,\n hidden_size=dim,\n num_layers=1,\n batch_first=True,\n )\n self.user_tower = nn.Sequential(\n nn.Linear(dim * 3 + 6, config.user_hidden_1),\n nn.GELU(),\n nn.Dropout(config.dropout),\n nn.Linear(config.user_hidden_1, config.user_hidden_2),\n nn.GELU(),\n nn.Dropout(config.dropout),\n nn.Linear(config.user_hidden_2, dim),\n )\n self.candidate_embedding = nn.Embedding(n_items, dim)\n self.register_buffer(\n \"item_features\", torch.from_numpy(item_features), persistent=True\n )\n self.item_tower = nn.Sequential(\n nn.Linear(dim + n_item_features, config.item_hidden_1),\n nn.GELU(),\n" } } ## Step 8 · agent · 2026-09-24T22:49:08.795Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T22:49:11.616Z { "code": "const r = await tools.exec_command({\"cmd\":\"cp /task/current_model/train_current_model.py /app/solution/train_current_model.py\\ncp /task/current_model/two_tower_model.py /app/solution/two_tower_model.py\\nrg -n \\\"^(def|class) |negative|cache|argparse|output|stage|validation\\\" /app/solution/train_current_model.py | sed -n '1,260p'\\nsed -n '260,760p' /app/solution/train_current_model.py\\nsed -n '300,700p' /app/solution/two_tower_model.py\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":50000}); text(r.output);" } ### Tool output · 2026-09-24T22:49:11.677Z Script completed Wall time 0.0 seconds Output: 7:16 held-out validation targets; passing zero for either cap restores the 12:The model is a pure two-tower retriever: no user-ID embedding, no output 14:and 24 uniform plus eight propensity-corrected unique in-batch negatives. Every 20:import argparse 41:class Config: 56: uniform_negatives: int = 24 57: inbatch_negatives: int = 8 61: negative_filter_scope: str = "full-training-history" 67: validation_fraction: float = 0.10 69: max_validation_targets_per_user: int = 16 76:class CandidateSet: 99:class SequenceData: 127:class TargetPartition: 132: validation_counts: np.ndarray 137: return int(self.training_counts.sum() + self.validation_counts.sum()) 140:def log(message: str) -> None: 144:def seed_everything(seed: int) -> None: 150:def model_config(config: Config) -> base.TowerConfig: 165: uniform_negatives=config.uniform_negatives, 166: inbatch_negatives=config.inbatch_negatives, 174:def append_state_snapshot( 190:def causal_activity_features( 247:def build_sequence_parts( 314:def final_user_features( 341:def prepare_data( 342: public: Path, stage: str, config: Config 354: if stage == "validation": 357: candidate_name = "validation_eligible_candidates.npz" 358: state_name = "validation_preference_state.parquet" 463: f"prepared stage={stage} sequence_users={len(prepared.user_ids):,} " 471:def cache_metadata(stage: str, config: Config) -> dict[str, object]: 473: "cache_version": CACHE_VERSION, 474: "stage": stage, 477: "event_time_feature": "exp-negative-gap-over-60-days", 481:def save_cache( 482: path: Path, prepared: SequenceData, stage: str, config: Config 487: metadata=np.asarray(json.dumps(cache_metadata(stage, config), sort_keys=True)), 490: log(f"saved public-derived full-sequence cache: {path}") 493:def load_cache(path: Path, stage: str, config: Config) -> SequenceData: 496: expected = cache_metadata(stage, config) 498: raise RuntimeError(f"sequence cache differs: {actual} != {expected}") 505: f"loaded full-sequence cache: tokens={len(prepared.sequence_items):,} " 511:def load_or_prepare( 513: stage: str, 515: cache: Path | None, 518: "validation_eligible_candidates.npz" 519: if stage == "validation" 523: if cache is not None and cache.is_file(): 524: prepared = load_cache(cache, stage, config) 526: raise RuntimeError("cached catalog differs from candidate artifact") 528: prepared, built_eligibility = prepare_data(public, stage, config) 529: if cache is not None: 530: save_cache(cache, prepared, stage, config) 534:def build_item_correction( 578:def build_known_positive_filter( 603: f"full-history false-negative filter: users={len(prepared.user_ids):,} " 609:def invalid_full_history_negatives( 610: negatives: np.ndarray, 616: """Return row-level invalid negatives using the complete training prefix.""" 617: byte_columns = negatives >> 3 618: bit_columns = negatives & 7 621: invalid |= negatives == targets[:, None] 622: invalid |= (negatives[:, :, None] == liked_items[:, None, :]).any(axis=2) 623: for column in range(1, negatives.shape[1]): 625: negatives[:, column, None] == negatives[:, :column] 630:def draw_full_history_filtered_negatives( 641: """Draw mixed negatives and reject all known public-training positives.""" 647: (batch_size, config.uniform_negatives), 653: if config.inbatch_negatives: 656: config.inbatch_negatives, max(len(unique_targets) - 1, 0) 672: if corrected_count < config.inbatch_negatives: 675: (batch_size, config.inbatch_negatives - corrected_count), 681: negatives = torch.cat([uniform, inbatch], dim=1).numpy() 682: correction = np.ones_like(negatives, dtype=np.float32) 685: :, config.uniform_negatives : config.uniform_negatives + corrected_count 688: invalid = invalid_full_history_negatives( 689: negatives, 697: torch.from_numpy(negatives).to(device), 706: negatives[invalid] = replacements 709: invalid = invalid_full_history_negatives( 710: negatives, 717: raise RuntimeError("full-history negative resampling failed") 719: torch.from_numpy(negatives).to(device), 724:def draw_training_negatives( 736: if config.negative_filter_scope == "full-training-history": 738: raise RuntimeError("full-history negative filter was not built") 739: return draw_full_history_filtered_negatives( 750: return base.draw_filtered_mixed_negatives( 755: config.uniform_negatives, 756: config.inbatch_negatives, 763:def event_sequence( 774:def pool_explicit_states( 798:def user_queries( 813:def candidate_vectors( 816: negatives: torch.Tensor, 818: all_items = torch.cat([positives, negatives.reshape(-1)]) 822: negative_vectors = encoded[inverse[len(positives) :]].reshape( 823: len(positives), negatives.shape[1], -1 825: return positive_vectors, negative_vectors 828:def history_windows( 845:def corrected_negative_loss( 851: total = config.uniform_negatives + config.inbatch_negatives 853: if config.uniform_negatives: 854: result += config.uniform_negatives * raw[ 855: :, : config.uniform_negatives 857: if config.inbatch_negatives: 858: selected = slice(config.uniform_negatives, total) 860: result += config.inbatch_negatives * ( 867:def retained_target_window_starts( 869: validation_counts: np.ndarray, 873: training_region_ends = total_targets - validation_counts 878: or np.any(validation_counts < 0) 880: raise RuntimeError("chronological target partition produced negative counts") 882: window_starts + training_counts + validation_counts, total_targets 888:def chronological_target_partition( 890: validation_fraction: float, 892: max_validation_targets_per_user: int, 896: validation = np.floor(totals * validation_fraction).astype(np.int64) 897: validation[(totals >= 2) & (validation == 0)] = 1 898: if max_validation_targets_per_user: 899: validation = np.minimum(validation, max_validation_targets_per_user) 901: history_training = totals - validation 905: window_starts = retained_target_window_starts(totals, validation, training) 909: validation_counts=validation, 914:def load_organic_like_outcomes( 915: public: Path, stage: str, item_ids: np.ndarray 918: prefixes = ("train",) if stage == "validation" else ("train", "val") 955:def build_target_weights( 957: stage: str, 978: public, stage, prepared.item_ids 981: base.TRAIN_END if stage == "validation" else base.VALIDATION_END 991: first_validation_target = last_training_target + 1 993: int(prepared.sequence_timestamps[first_validation_target]) 994: if first_validation_target < end 1035:def build_dense_block( 1111:def score_target_microbatches( 1119: negatives: torch.Tensor, 1120: negative_weights: torch.Tensor, 1134: "negative_sum": 0.0, 1162: positive_vectors, negative_vectors = candidate_vectors( 1165: negatives[begin:end], 1168: negative_logits = torch.einsum( 1169: "bd,bkd->bk", query_vectors, negative_vectors 1172: negative_loss = corrected_negative_loss( 1173: negative_logits, negative_weights[begin:end], config 1179: selected_weights * (positive_loss + negative_loss) / 2.0 1190: statistics["negative_sum"] += float(negative_logits.detach().sum().cpu()) 1192: (positive_logits.detach()[:, None] > negative_logits.detach()).sum().cpu() 1198:def release_accelerator_cache(device: torch.device) -> None: 1200: torch.mps.empty_cache() 1203:def run_training_epoch( 1227: negative_count = config.uniform_negatives + config.inbatch_negatives 1232: total_negative = 0.0 1257: sequence_outputs, _ = event_sequence( 1265: sequence_outputs[row, count - 1] 1275: sequence_vectors = sequence_outputs.reshape( 1293: negatives, negative_weights = draw_training_negatives( 1314: negatives, 1315: negative_weights, 1331: total_negative += statistics["negative_sum"] 1335: release_accelerator_cache(device) 1353: "negative_logit": total_negative / max(total_targets * negative_count, 1), 1355: / max(total_targets * negative_count, 1), 1364:def evaluate_validation_bce( 1371: validation_counts: np.ndarray, 1378: totals = training_counts + validation_counts 1379: users_with_validation = np.flatnonzero(validation_counts > 0) 1380: ordered = users_with_validation[ 1381: np.argsort(totals[users_with_validation], kind="stable") 1413: sequence_outputs, _ = event_sequence( 1421: sequence_outputs[row, count - 1] 1431: sequence_vectors = sequence_outputs.reshape( 1449: negatives, negative_weights = draw_training_negatives( 1469: negatives, 1470: negative_weights, 1479: release_accelerator_cache(device) 1481: "validation_loss": total_loss / max(total_weight, 1.0e-12), 1482: "validation_targets": float(total_targets), 1483: "validation_target_weight_sum": total_weight, 1484: "validation_seconds": time.monotonic() - started, 1488:def snapshot_model(model: nn.Module) -> dict[str, torch.Tensor]: 1495:def save_epoch_checkpoint( 1521:def train_model( 1523: stage: str, 1532: config.validation_fraction, 1534: config.max_validation_targets_per_user, 1537: validation_counts = partition.validation_counts 1540: f"validation={int(validation_counts.sum()):,} " 1560: stage, 1567: if config.negative_filter_scope == "full-training-history" 1601: "negative_filter_scope", "last-100" 1619: stored_config.get("validation_fraction", 0.10), 1621: stored_config.get("max_validation_targets_per_user", 0), 1624: config.validation_fraction, 1626: config.max_validation_targets_per_user, 1629: stored_filter_scope != config.negative_filter_scope 1657: prior.pop("validation_loss", None) 1660: f"{stored_filter_scope}->{config.negative_filter_scope}, " 1664: "recomputing the resume checkpoint's validation BCE" 1666: if "validation_loss" not in prior: 1668: evaluate_validation_bce( 1675: validation_counts, 1682: best_loss = float(prior["validation_loss"]) 1686: f"resumed validation-BCE training: epoch={start_epoch} " 1705: validation = evaluate_validation_bce( 1712: validation_counts, 1717: diagnostics.update(validation) 1731: diagnostics["validation_loss"] 1735: best_loss = diagnostics["validation_loss"] 1755: f"validation_loss={diagnostics['validation_loss']:.5f} " 1758: f"validation_targets={int(diagnostics['validation_targets']):,} " 1760: f"validation_seconds={diagnostics['validation_seconds']:.1f}" 1762: release_accelerator_cache(device) 1766: f"best_epoch={best_epoch} best_validation_loss={best_loss:.6f}" 1775: f"restored best validation-BCE checkpoint: epoch={best_epoch} " 1782:def inference_queries( 1797: output_rows = ordered[group_start : group_start + config.user_batch_size] 1798: rows = sequence_rows[output_rows] 1799: group_lengths = lengths[output_rows] 1801: 1, len(output_rows), config.embedding_dim, device=device 1809: (len(output_rows), steps), 1813: features = np.zeros((len(output_rows), steps, 3), dtype=np.float16) 1814: counts = np.zeros(len(output_rows), dtype=np.int32) 1826: outputs, _ = event_sequence( 1835: outputs[local_row, count - 1] 1842: prepared.inference_liked_items[output_rows].astype(np.int64) 1845: prepared.inference_disliked_items[output_rows].astype(np.int64) 1853: prepared.inference_user_features[output_rows].astype(np.float32) 1856: result[torch.from_numpy(output_rows.astype(np.int64))] = queries.cpu() 1861:def rank_candidates( 1912:def write_ranking(frame: pd.DataFrame, output: Path) -> None: 1913: output.parent.mkdir(parents=True, exist_ok=True) 1914: if output.suffix == ".parquet": 1915: frame.to_parquet(output, index=False, compression="zstd") 1916: elif output.suffix == ".csv": 1917: frame.to_csv(output, index=False) 1919: raise ValueError("--output must end in .parquet or .csv") 1922:def main() -> int: 1923: parser = argparse.ArgumentParser(description=__doc__) 1925: "--stage", choices=("validation", "submission"), default="validation" 1927: parser.add_argument("--output", type=Path, required=True) 1929: parser.add_argument("--prepared-cache", type=Path) 1944: "--negative-filter-scope", 1946: default=Config.negative_filter_scope, 1974: "--validation-fraction", 1976: default=Config.validation_fraction, 1982: help="retain this many recent pre-validation targets per user; 0 is uncapped", 1985: "--max-validation-targets-per-user", 1987: default=Config.max_validation_targets_per_user, 1988: help="retain this many final validation targets per user; 0 is uncapped", 2020: or args.max_validation_targets_per_user < 0 2023: if not 0.0 < args.validation_fraction < 1.0: 2024: parser.error("validation fraction must be strictly between zero and one") 2034: negative_filter_scope=args.negative_filter_scope, 2040: validation_fraction=args.validation_fraction, 2042: max_validation_targets_per_user=args.max_validation_targets_per_user, 2057: args.stage, 2059: args.prepared_cache.resolve() if args.prepared_cache else None, 2063: config.validation_fraction, 2065: config.max_validation_targets_per_user, if not len(items): return None event_features = np.zeros((len(items), 3), dtype=np.float16) event_features[:, 0] = organic.astype(np.float16) event_features[:, 1] = ( np.minimum(completion, 100.0) / 100.0 ).astype(np.float16) if len(items) > 1: gaps = np.maximum(0, timestamps[1:] - timestamps[:-1]) event_features[1:, 2] = np.exp( -gaps.astype(np.float64) / (60.0 * base.DAY) ).astype(np.float16) activity = causal_activity_features( timestamps, items, organic, completion ) states = np.zeros(len(items), dtype=np.uint32) active_likes: dict[int, int] = {} active_dislikes: dict[int, int] = {} explicit_position = 0 current_state = 0 for row, timestamp in enumerate(timestamps): changed = False while ( explicit_position < len(explicit.timestamps) and int(explicit.timestamps[explicit_position]) < int(timestamp) ): item = int(explicit.items[explicit_position]) event_timestamp = int(explicit.timestamps[explicit_position]) destination = ( active_likes if int(explicit.channel[explicit_position]) == 0 else active_dislikes ) if int(explicit.active[explicit_position]) == 1: destination[item] = event_timestamp else: destination.pop(item, None) explicit_position += 1 changed = True if changed: current_state = append_state_snapshot( active_likes, active_dislikes, n_items, liked_states, disliked_states, state_counts, ) states[row] = np.uint32(current_state) return timestamps, items.copy(), event_features, activity, states def final_user_features( timestamps: np.ndarray, items: np.ndarray, event_features: np.ndarray, history_end: int, like_count: int, dislike_count: int, ) -> np.ndarray: start = int( np.searchsorted( timestamps, history_end - base.PROFILE_LOOKBACK_DAYS * base.DAY, side="left", ) ) result = np.zeros(6, dtype=np.float32) recent_items = items[start:] if len(recent_items): result[0] = np.log1p(len(recent_items)) / 8.0 result[1] = np.log1p(len(np.unique(recent_items))) / 6.0 result[2] = float(np.mean(event_features[start:, 0])) result[3] = float(np.mean(event_features[start:, 1])) result[4] = np.log1p(like_count) / 6.0 result[5] = np.log1p(dislike_count) / 4.0 return result def prepare_data( public: Path, stage: str, config: Config ) -> tuple[SequenceData, CandidateSet]: data = public / "data" catalog = pd.read_parquet(data / "candidate_catalog.parquet").sort_values( "popularity_rank" ) item_ids = catalog.item_id.to_numpy(dtype=np.int64, copy=True) item_index = pd.Index(item_ids) n_items = len(item_ids) if n_items >= np.iinfo(np.uint16).max: raise RuntimeError("uint16 item storage is too small for this catalog") if stage == "validation": prefixes = ("train",) history_end = base.TRAIN_END candidate_name = "validation_eligible_candidates.npz" state_name = "validation_preference_state.parquet" else: prefixes = ("train", "val") history_end = base.VALIDATION_END candidate_name = "test_eligible_candidates.npz" state_name = "test_preference_state.parquet" eligibility = CandidateSet.read(data / candidate_name) if not np.array_equal(eligibility.item_ids, item_ids): raise RuntimeError("catalog and candidate artifact item order differ") explicit_by_user = base.load_explicit_events(data, prefixes, item_index) inference_likes, inference_dislikes, like_counts, dislike_counts = ( base.load_inference_state( data / state_name, item_index, eligibility.uids, n_items, ) ) inference_rows = {int(uid): row for row, uid in enumerate(eligibility.uids)} inference_user_features = np.zeros((len(eligibility.uids), 6), dtype=np.float32) inference_sequence_rows = np.full(len(eligibility.uids), -1, dtype=np.int32) # Snapshot zero is the shared empty explicit state. liked_states = [np.full(base.LIKE_ITEMS, n_items, dtype=np.uint16)] disliked_states = [ np.full(base.DISLIKE_ITEMS, n_items, dtype=np.uint16) ] state_counts: list[tuple[int, int]] = [(0, 0)] user_ids: list[int] = [] offsets = [0] timestamp_parts: list[np.ndarray] = [] item_parts: list[np.ndarray] = [] event_parts: list[np.ndarray] = [] activity_parts: list[np.ndarray] = [] state_parts: list[np.ndarray] = [] listen_paths = [data / f"{prefix}_listens.parquet" for prefix in prefixes] started = time.monotonic() for seen_users, (uid, listens) in enumerate( base.merge_listen_streams(listen_paths, item_index), start=1 ): parts = build_sequence_parts( listens, explicit_by_user.get(uid, base.empty_explicit_events()), n_items, liked_states, disliked_states, state_counts, ) if parts is None: continue sequence_row = len(user_ids) user_ids.append(uid) timestamp_parts.append(parts[0].astype(np.uint32, copy=False)) item_parts.append(parts[1]) event_parts.append(parts[2]) activity_parts.append(parts[3]) state_parts.append(parts[4]) offsets.append(offsets[-1] + len(parts[1])) inference_row = inference_rows.get(uid) if inference_row is not None: inference_sequence_rows[inference_row] = sequence_row inference_user_features[inference_row] = final_user_features( parts[0], parts[1], parts[2], history_end, int(like_counts[inference_row]), int(dislike_counts[inference_row]), ) if seen_users % 1_000 == 0: log( f"prepared users={seen_users:,} tokens={offsets[-1]:,} " f"states={len(liked_states):,} " f"elapsed={time.monotonic() - started:.1f}s" ) missing = np.flatnonzero(inference_sequence_rows < 0) if len(missing): inference_user_features[missing, 4] = np.log1p(like_counts[missing]) / 6.0 inference_user_features[missing, 5] = ( np.log1p(dislike_counts[missing]) / 4.0 ) log(f"target users without qualifying catalog sequence={len(missing):,}") prepared = SequenceData( item_ids=item_ids, item_features=base.build_item_features(catalog), user_ids=np.asarray(user_ids, dtype=np.int64), offsets=np.asarray(offsets, dtype=np.int64), sequence_timestamps=np.concatenate(timestamp_parts), sequence_items=np.concatenate(item_parts), event_features=np.concatenate(event_parts), activity_features=np.concatenate(activity_parts), state_ids=np.concatenate(state_parts), liked_states=np.stack(liked_states), disliked_states=np.stack(disliked_states), state_counts=np.asarray(state_counts, dtype=np.int32), inference_user_rows=inference_sequence_rows, inference_liked_items=inference_likes, inference_disliked_items=inference_dislikes, inference_user_features=inference_user_features, ) log( f"prepared stage={stage} sequence_users={len(prepared.user_ids):,} " f"tokens={len(prepared.sequence_items):,} " f"next_item_targets={prepared.target_count:,} " f"states={len(prepared.liked_states):,}" ) return prepared, eligibility def cache_metadata(stage: str, config: Config) -> dict[str, object]: return { "cache_version": CACHE_VERSION, "stage": stage, "positive_listen_threshold": base.LISTEN_POSITIVE_THRESHOLD, "profile_lookback_days": base.PROFILE_LOOKBACK_DAYS, "event_time_feature": "exp-negative-gap-over-60-days", } def save_cache( path: Path, prepared: SequenceData, stage: str, config: Config ) -> None: path.parent.mkdir(parents=True, exist_ok=True) np.savez( path, metadata=np.asarray(json.dumps(cache_metadata(stage, config), sort_keys=True)), **prepared.__dict__, ) log(f"saved public-derived full-sequence cache: {path}") def load_cache(path: Path, stage: str, config: Config) -> SequenceData: with np.load(path, allow_pickle=False) as artifact: actual = json.loads(str(artifact["metadata"].item())) expected = cache_metadata(stage, config) if actual != expected: raise RuntimeError(f"sequence cache differs: {actual} != {expected}") values = { field: artifact[field].copy() for field in SequenceData.__dataclass_fields__ } prepared = SequenceData(**values) log( f"loaded full-sequence cache: tokens={len(prepared.sequence_items):,} " f"targets={prepared.target_count:,}" ) return prepared def load_or_prepare( public: Path, stage: str, config: Config, cache: Path | None, ) -> tuple[SequenceData, CandidateSet]: candidate_name = ( "validation_eligible_candidates.npz" if stage == "validation" else "test_eligible_candidates.npz" ) eligibility = CandidateSet.read(public / "data" / candidate_name) if cache is not None and cache.is_file(): prepared = load_cache(cache, stage, config) if not np.array_equal(prepared.item_ids, eligibility.item_ids): raise RuntimeError("cached catalog differs from candidate artifact") return prepared, eligibility prepared, built_eligibility = prepare_data(public, stage, config) if cache is not None: save_cache(cache, prepared, stage, config) return prepared, built_eligibility def build_item_correction( prepared: SequenceData, config: Config, target_starts: np.ndarray, target_counts: np.ndarray, ) -> torch.Tensor: """Estimate in-batch propensities from the selected training targets.""" counts = np.zeros(len(prepared.item_ids), dtype=np.float64) for user_row, count in enumerate(target_counts): if not count: continue offset = int(prepared.offsets[user_row]) target_start = int(target_starts[user_row]) counts += np.bincount( prepared.sequence_items[ offset + target_start + 1 : offset + target_start + int(count) + 1 ].astype(np.int64), minlength=len(prepared.item_ids), ) total = float(counts.sum()) probabilities = (counts + 1.0) / (total + len(counts)) raw = np.power( (1.0 / len(counts)) / probabilities, config.inbatch_correction_power, ) normalization = float(np.sum(counts * raw) / max(total, 1.0)) correction = np.clip( raw / max(normalization, 1.0e-12), config.inbatch_correction_min, config.inbatch_correction_max, ).astype(np.float32) mean_on_targets = float(np.sum(counts * correction) / max(total, 1.0)) log( f"inbatch correction: min={correction.min():.3f} " f"mean_on_targets={mean_on_targets:.3f} max={correction.max():.3f}" ) return torch.from_numpy(correction) def build_known_positive_filter( prepared: SequenceData, training_counts: np.ndarray ) -> np.ndarray: """Pack each user's complete public-training positive-item set.""" width = (len(prepared.item_ids) + 7) // 8 packed = np.zeros((len(prepared.user_ids), width), dtype=np.uint8) retained = 0 for user_row, target_count in enumerate(training_counts): if not target_count: continue offset = int(prepared.offsets[user_row]) # T adjacent targets consume sequence positions 0 through T. The # chronological BCE suffix starts at T + 1 and is intentionally absent. items = np.unique( prepared.sequence_items[ offset : offset + int(target_count) + 1 ].astype(np.int64) ) byte_columns = items >> 3 bit_values = np.left_shift( np.uint8(1), (items & 7).astype(np.uint8) ) np.bitwise_or.at(packed[user_row], byte_columns, bit_values) retained += len(items) log( f"full-history false-negative filter: users={len(prepared.user_ids):,} " f"unique_user_items={retained:,} bytes={packed.nbytes:,}" ) return packed def invalid_full_history_negatives( negatives: np.ndarray, targets: np.ndarray, user_rows: np.ndarray, known_positive_packed: np.ndarray, liked_items: np.ndarray, ) -> np.ndarray: """Return row-level invalid negatives using the complete training prefix.""" byte_columns = negatives >> 3 bit_columns = negatives & 7 known = known_positive_packed[user_rows[:, None], byte_columns] invalid = ((known >> bit_columns) & 1).astype(bool) invalid |= negatives == targets[:, None] invalid |= (negatives[:, :, None] == liked_items[:, None, :]).any(axis=2) for column in range(1, negatives.shape[1]): invalid[:, column] |= ( negatives[:, column, None] == negatives[:, :column] ).any(axis=1) return invalid def draw_full_history_filtered_negatives( targets: torch.Tensor, user_rows: np.ndarray, liked_items: torch.Tensor, known_positive_packed: np.ndarray, n_items: int, config: Config, item_correction: torch.Tensor, generator: torch.Generator, device: torch.device, ) -> tuple[torch.Tensor, torch.Tensor]: """Draw mixed negatives and reject all known public-training positives.""" targets_cpu = targets.detach().cpu().numpy().astype(np.int64, copy=False) liked_cpu = liked_items.detach().cpu().numpy().astype(np.int64, copy=False) batch_size = len(targets_cpu) uniform = torch.randint( n_items, (batch_size, config.uniform_negatives), generator=generator, device="cpu", ) inbatch = torch.empty((batch_size, 0), dtype=torch.long) corrected_count = 0 if config.inbatch_negatives: unique_targets = torch.unique(targets.detach().cpu()) corrected_count = min( config.inbatch_negatives, max(len(unique_targets) - 1, 0) ) if corrected_count: priorities = torch.rand( (batch_size, len(unique_targets)), generator=generator ) priorities.masked_fill_( unique_targets[None, :].eq( torch.from_numpy(targets_cpu)[:, None] ), -1.0, ) columns = torch.topk( priorities, k=corrected_count, dim=1 ).indices inbatch = unique_targets[columns] if corrected_count < config.inbatch_negatives: filler = torch.randint( n_items, (batch_size, config.inbatch_negatives - corrected_count), generator=generator, device="cpu", ) inbatch = torch.cat([inbatch, filler], dim=1) negatives = torch.cat([uniform, inbatch], dim=1).numpy() correction = np.ones_like(negatives, dtype=np.float32) if corrected_count: correction[ :, config.uniform_negatives : config.uniform_negatives + corrected_count ] = item_correction[inbatch[:, :corrected_count]].numpy() for _ in range(32): invalid = invalid_full_history_negatives( negatives, targets_cpu, user_rows, known_positive_packed, liked_cpu, ) if not np.any(invalid): return ( torch.from_numpy(negatives).to(device), torch.from_numpy(correction).to(device), ) replacements = torch.randint( n_items, (int(invalid.sum()),), generator=generator, device="cpu", ).numpy() negatives[invalid] = replacements # Rejected in-batch items become ordinary uniform draws. correction[invalid] = 1.0 invalid = invalid_full_history_negatives( negatives, targets_cpu, user_rows, known_positive_packed, liked_cpu, ) if np.any(invalid): raise RuntimeError("full-history negative resampling failed") return ( torch.from_numpy(negatives).to(device), torch.from_numpy(correction).to(device), ) def draw_training_negatives( positive_items: torch.Tensor, known_history: torch.Tensor, liked_items: torch.Tensor, user_rows: np.ndarray, known_positive_packed: np.ndarray | None, prepared: SequenceData, config: Config, item_correction: torch.Tensor, generator: torch.Generator, device: torch.device, ) -> tuple[torch.Tensor, torch.Tensor]: if config.negative_filter_scope == "full-training-history": if known_positive_packed is None: raise RuntimeError("full-history negative filter was not built") return draw_full_history_filtered_negatives( positive_items, user_rows, liked_items, known_positive_packed, len(prepared.item_ids), config, item_correction, generator, device, ) return base.draw_filtered_mixed_negatives( positive_items, known_history, liked_items, len(prepared.item_ids), config.uniform_negatives, config.inbatch_negatives, item_correction, generator, device, ) input_size=config.hidden_dim, hidden_size=dim, num_layers=1, batch_first=True, ) self.user_tower = nn.Sequential( nn.Linear(dim * 3 + 6, config.user_hidden_1), nn.GELU(), nn.Dropout(config.dropout), nn.Linear(config.user_hidden_1, config.user_hidden_2), nn.GELU(), nn.Dropout(config.dropout), nn.Linear(config.user_hidden_2, dim), ) self.candidate_embedding = nn.Embedding(n_items, dim) self.register_buffer( "item_features", torch.from_numpy(item_features), persistent=True ) self.item_tower = nn.Sequential( nn.Linear(dim + n_item_features, config.item_hidden_1), nn.GELU(), nn.Dropout(config.dropout), nn.Linear(config.item_hidden_1, config.item_hidden_2), nn.GELU(), nn.Dropout(config.dropout), nn.Linear(config.item_hidden_2, dim), ) nn.init.normal_(self.context_embedding.weight, std=0.02) nn.init.normal_(self.candidate_embedding.weight, std=0.02) with torch.no_grad(): self.context_embedding.weight[n_items].zero_() def _mean_context_channel(self, items: torch.Tensor) -> torch.Tensor: mask = items.ne(self.n_items) vectors = self.context_embedding(items) return (vectors * mask.unsqueeze(-1)).sum(dim=1) / mask.sum( dim=1, keepdim=True ).clamp_min(1) def encode_items(self, items: torch.Tensor) -> torch.Tensor: inputs = torch.cat( [self.candidate_embedding(items), self.item_features[items]], dim=1 ) return self.item_tower(inputs) def invalid_negative_mask( negatives: torch.Tensor, targets: torch.Tensor, context_items: torch.Tensor, liked_items: torch.Tensor, ) -> torch.Tensor: """Identify targets, known positives, active likes, and row duplicates.""" invalid = negatives.eq(targets[:, None]) invalid |= negatives[:, :, None].eq(context_items[:, None, :]).any(dim=2) invalid |= negatives[:, :, None].eq(liked_items[:, None, :]).any(dim=2) for column in range(1, negatives.shape[1]): invalid[:, column] |= negatives[:, column, None].eq( negatives[:, :column] ).any(dim=1) return invalid def draw_filtered_mixed_negatives( targets: torch.Tensor, context_items: torch.Tensor, liked_items: torch.Tensor, n_items: int, uniform_count: int, inbatch_count: int, item_correction: torch.Tensor, generator: torch.Generator, device: torch.device, ) -> tuple[torch.Tensor, torch.Tensor]: """Draw uniform and unique-target in-batch negatives.""" batch_size = len(targets) uniform = torch.randint( n_items, (batch_size, uniform_count), generator=generator, device="cpu", ) inbatch = torch.empty((batch_size, 0), dtype=torch.long, device="cpu") corrected_count = 0 if inbatch_count: unique_targets = torch.unique(targets.detach().cpu()) corrected_count = min(inbatch_count, max(len(unique_targets) - 1, 0)) if corrected_count: priorities = torch.rand( (batch_size, len(unique_targets)), generator=generator ) priorities.masked_fill_( unique_targets[None, :].eq(targets.detach().cpu()[:, None]), -1.0, ) columns = torch.topk(priorities, k=corrected_count, dim=1).indices inbatch = unique_targets[columns] if corrected_count < inbatch_count: filler = torch.randint( n_items, (batch_size, inbatch_count - corrected_count), generator=generator, device="cpu", ) inbatch = torch.cat([inbatch, filler], dim=1) negatives_cpu = torch.cat([uniform, inbatch], dim=1) correction_cpu = torch.ones_like(negatives_cpu, dtype=torch.float32) if corrected_count: correction_cpu[ :, uniform_count : uniform_count + corrected_count ] = item_correction[inbatch[:, :corrected_count]] negatives = negatives_cpu.to(device) correction = correction_cpu.to(device) for _ in range(32): invalid = invalid_negative_mask( negatives, targets, context_items, liked_items ) if not bool(invalid.any()): return negatives, correction replacements = torch.randint( n_items, (int(invalid.sum().detach().cpu()),), generator=generator, device="cpu", ).to(device) negatives[invalid] = replacements correction[invalid] = 1.0 invalid = invalid_negative_mask( negatives, targets, context_items, liked_items ) if bool(invalid.any()): raise RuntimeError("mixed negative resampling failed false-negative filter") return negatives, correction ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-204759cd-8971-415a-ab09-9632d43cf7ca", "process_id": "1699", "command": [ "/bin/sh", "-lc", "cp /task/current_model/train_current_model.py /app/solution/train_current_model.py\ncp /task/current_model/two_tower_model.py /app/solution/two_tower_model.py\nrg -n \"^(def|class) |negative|cache|argparse|output|stage|validation\" /app/solution/train_current_model.py | sed -n '1,260p'\nsed -n '260,760p' /app/solution/train_current_model.py\nsed -n '300,700p' /app/solution/two_tower_model.py" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cp /task/current_model/train_current_model.py /app/solution/train_current_model.py\ncp /task/current_model/two_tower_model.py /app/solution/two_tower_model.py\nrg -n \"^(def|class) |negative|cache|argparse|output|stage|validation\" /app/solution/train_current_model.py | sed -n '1,260p'\nsed -n '260,760p' /app/solution/train_current_model.py\nsed -n '300,700p' /app/solution/two_tower_model.py" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "7:16 held-out validation targets; passing zero for either cap restores the\n12:The model is a pure two-tower retriever: no user-ID embedding, no output\n14:and 24 uniform plus eight propensity-corrected unique in-batch negatives. Every\n20:import argparse\n41:class Config:\n56: uniform_negatives: int = 24\n57: inbatch_negatives: int = 8\n61: negative_filter_scope: str = \"full-training-history\"\n67: validation_fraction: float = 0.10\n69: max_validation_targets_per_user: int = 16\n76:class CandidateSet:\n99:class SequenceData:\n127:class TargetPartition:\n132: validation_counts: np.ndarray\n137: return int(self.training_counts.sum() + self.validation_counts.sum())\n140:def log(message: str) -> None:\n144:def seed_everything(seed: int) -> None:\n150:def model_config(config: Config) -> base.TowerConfig:\n165: uniform_negatives=config.uniform_negatives,\n166: inbatch_negatives=config.inbatch_negatives,\n174:def append_state_snapshot(\n190:def causal_activity_features(\n247:def build_sequence_parts(\n314:def final_user_features(\n341:def prepare_data(\n342: public: Path, stage: str, config: Config\n354: if stage == \"validation\":\n357: candidate_name = \"validation_eligible_candidates.npz\"\n358: state_name = \"validation_preference_state.parquet\"\n463: f\"prepared stage={stage} sequence_users={len(prepared.user_ids):,} \"\n471:def cache_metadata(stage: str, config: Config) -> dict[str, object]:\n473: \"cache_version\": CACHE_VERSION,\n474: \"stage\": stage,\n477: \"event_time_feature\": \"exp-negative-gap-over-60-days\",\n481:def save_cache(\n482: path: Path, prepared: SequenceData, stage: str, config: Config\n487: metadata=np.asarray(json.dumps(cache_metadata(stage, config), sort_keys=True)),\n490: log(f\"saved public-derived full-sequence cache: {path}\")\n493:def load_cache(path: Path, stage: str, config: Config) -> SequenceData:\n496: expected = cache_metadata(stage, config)\n498: raise RuntimeError(f\"sequence cache differs: {actual} != {expected}\")\n505: f\"loaded full-sequence cache: tokens={len(prepared.sequence_items):,} \"\n511:def load_or_prepare(\n513: stage: str,\n515: cache: Path | None,\n518: \"validation_eligible_candidates.npz\"\n519: if stage == \"validation\"\n523: if cache is not None and cache.is_file():\n524: prepared = load_cache(cache, stage, config)\n526: raise RuntimeError(\"cached catalog differs from candidate artifact\")\n528: prepared, built_eligibility = prepare_data(public, stage, config)\n529: if cache is not None:\n530: save_cache(cache, prepared, stage, config)\n534:def build_item_correction(\n578:def build_known_positive_filter(\n603: f\"full-history false-negative filter: users={len(prepared.user_ids):,} \"\n609:def invalid_full_history_negatives(\n610: negatives: np.ndarray,\n616: \"\"\"Return row-level invalid negatives using the complete training prefix.\"\"\"\n617: byte_columns = negatives >> 3\n618: bit_columns = negatives & 7\n621: invalid |= negatives == targets[:, None]\n622: invalid |= (negatives[:, :, None] == liked_items[:, None, :]).any(axis=2)\n623: for column in range(1, negatives.shape[1]):\n625: negatives[:, column, None] == negatives[:, :column]\n630:def draw_full_history_filtered_negatives(\n641: \"\"\"Draw mixed negatives and reject all known public-training positives.\"\"\"\n647: (batch_size, config.uniform_negatives),\n653: if config.inbatch_negatives:\n656: config.inbatch_negatives, max(len(unique_targets) - 1, 0)\n672: if corrected_count < config.inbatch_negatives:\n675: (batch_size, config.inbatch_negatives - corrected_count),\n681: negatives = torch.cat([uniform, inbatch], dim=1).numpy()\n682: correction = np.ones_like(negatives, dtype=np.float32)\n685: :, config.uniform_negatives : config.uniform_negatives + corrected_count\n688: invalid = invalid_full_history_negatives(\n689: negatives,\n697: torch.from_numpy(negatives).to(device),\n706: negatives[invalid] = replacements\n709: invalid = invalid_full_history_negatives(\n710: negatives,\n717: raise RuntimeError(\"full-history negative resampling failed\")\n719: torch.from_numpy(negatives).to(device),\n724:def draw_training_negatives(\n736: if config.negative_filter_scope == \"full-training-history\":\n738: raise RuntimeError(\"full-history negative filter was not built\")\n739: return draw_full_history_filtered_negatives(\n750: return base.draw_filtered_mixed_negatives(\n755: config.uniform_negatives,\n756: config.inbatch_negatives,\n763:def event_sequence(\n774:def pool_explicit_states(\n798:def user_queries(\n813:def candidate_vectors(\n816: negatives: torch.Tensor,\n818: all_items = torch.cat([positives, negatives.reshape(-1)])\n822: negative_vectors = encoded[inverse[len(positives) :]].reshape(\n823: len(positives), negatives.shape[1], -1\n825: return positive_vectors, negative_vectors\n828:def history_windows(\n845:def corrected_negative_loss(\n851: total = config.uniform_negatives + config.inbatch_negatives\n853: if config.uniform_negatives:\n854: result += config.uniform_negatives * raw[\n855: :, : config.uniform_negatives\n857: if config.inbatch_negatives:\n858: selected = slice(config.uniform_negatives, total)\n860: result += config.inbatch_negatives * (\n867:def retained_target_window_starts(\n869: validation_counts: np.ndarray,\n873: training_region_ends = total_targets - validation_counts\n878: or np.any(validation_counts < 0)\n880: raise RuntimeError(\"chronological target partition produced negative counts\")\n882: window_starts + training_counts + validation_counts, total_targets\n888:def chronological_target_partition(\n890: validation_fraction: float,\n892: max_validation_targets_per_user: int,\n896: validation = np.floor(totals * validation_fraction).astype(np.int64)\n897: validation[(totals >= 2) & (validation == 0)] = 1\n898: if max_validation_targets_per_user:\n899: validation = np.minimum(validation, max_validation_targets_per_user)\n901: history_training = totals - validation\n905: window_starts = retained_target_window_starts(totals, validation, training)\n909: validation_counts=validation,\n914:def load_organic_like_outcomes(\n915: public: Path, stage: str, item_ids: np.ndarray\n918: prefixes = (\"train\",) if stage == \"validation\" else (\"train\", \"val\")\n955:def build_target_weights(\n957: stage: str,\n978: public, stage, prepared.item_ids\n981: base.TRAIN_END if stage == \"validation\" else base.VALIDATION_END\n991: first_validation_target = last_training_target + 1\n993: int(prepared.sequence_timestamps[first_validation_target])\n994: if first_validation_target < end\n1035:def build_dense_block(\n1111:def score_target_microbatches(\n1119: negatives: torch.Tensor,\n1120: negative_weights: torch.Tensor,\n1134: \"negative_sum\": 0.0,\n1162: positive_vectors, negative_vectors = candidate_vectors(\n1165: negatives[begin:end],\n1168: negative_logits = torch.einsum(\n1169: \"bd,bkd->bk\", query_vectors, negative_vectors\n1172: negative_loss = corrected_negative_loss(\n1173: negative_logits, negative_weights[begin:end], config\n1179: selected_weights * (positive_loss + negative_loss) / 2.0\n1190: statistics[\"negative_sum\"] += float(negative_logits.detach().sum().cpu())\n1192: (positive_logits.detach()[:, None] > negative_logits.detach()).sum().cpu()\n1198:def release_accelerator_cache(device: torch.device) -> None:\n1200: torch.mps.empty_cache()\n1203:def run_training_epoch(\n1227: negative_count = config.uniform_negatives + config.inbatch_negatives\n1232: total_negative = 0.0\n1257: sequence_outputs, _ = event_sequence(\n1265: sequence_outputs[row, count - 1]\n1275: sequence_vectors = sequence_outputs.reshape(\n1293: negatives, negative_weights = draw_training_negatives(\n1314: negatives,\n1315: negative_weights,\n1331: total_negative += statistics[\"negative_sum\"]\n1335: release_accelerator_cache(device)\n1353: \"negative_logit\": total_negative / max(total_targets * negative_count, 1),\n1355: / max(total_targets * negative_count, 1),\n1364:def evaluate_validation_bce(\n1371: validation_counts: np.ndarray,\n1378: totals = training_counts + validation_counts\n1379: users_with_validation = np.flatnonzero(validation_counts > 0)\n1380: ordered = users_with_validation[\n1381: np.argsort(totals[users_with_validation], kind=\"stable\")\n1413: sequence_outputs, _ = event_sequence(\n1421: sequence_outputs[row, count - 1]\n1431: sequence_vectors = sequence_outputs.reshape(\n1449: negatives, negative_weights = draw_training_negatives(\n1469: negatives,\n1470: negative_weights,\n1479: release_accelerator_cache(device)\n1481: \"validation_loss\": total_loss / max(total_weight, 1.0e-12),\n1482: \"validation_targets\": float(total_targets),\n1483: \"validation_target_weight_sum\": total_weight,\n1484: \"validation_seconds\": time.monotonic() - started,\n1488:def snapshot_model(model: nn.Module) -> dict[str, torch.Tensor]:\n1495:def save_epoch_checkpoint(\n1521:def train_model(\n1523: stage: str,\n1532: config.validation_fraction,\n1534: config.max_validation_targets_per_user,\n1537: validation_counts = partition.validation_counts\n1540: f\"validation={int(validation_counts.sum()):,} \"\n1560: stage,\n1567: if config.negative_filter_scope == \"full-training-history\"\n1601: \"negative_filter_scope\", \"last-100\"\n1619: stored_config.get(\"validation_fraction\", 0.10),\n1621: stored_config.get(\"max_validation_targets_per_user\", 0),\n1624: config.validation_fraction,\n1626: config.max_validation_targets_per_user,\n1629: stored_filter_scope != config.negative_filter_scope\n1657: prior.pop(\"validation_loss\", None)\n1660: f\"{stored_filter_scope}->{config.negative_filter_scope}, \"\n1664: \"recomputing the resume checkpoint's validation BCE\"\n1666: if \"validation_loss\" not in prior:\n1668: evaluate_validation_bce(\n1675: validation_counts,\n1682: best_loss = float(prior[\"validation_loss\"])\n1686: f\"resumed validation-BCE training: epoch={start_epoch} \"\n1705: validation = evaluate_validation_bce(\n1712: validation_counts,\n1717: diagnostics.update(validation)\n1731: diagnostics[\"validation_loss\"]\n1735: best_loss = diagnostics[\"validation_loss\"]\n1755: f\"validation_loss={diagnostics['validation_loss']:.5f} \"\n1758: f\"validation_targets={int(diagnostics['validation_targets']):,} \"\n1760: f\"validation_seconds={diagnostics['validation_seconds']:.1f}\"\n1762: release_accelerator_cache(device)\n1766: f\"best_epoch={best_epoch} best_validation_loss={best_loss:.6f}\"\n1775: f\"restored best validation-BCE checkpoint: epoch={best_epoch} \"\n1782:def inference_queries(\n1797: output_rows = ordered[group_start : group_start + config.user_batch_size]\n1798: rows = sequence_rows[output_rows]\n1799: group_lengths = lengths[output_rows]\n1801: 1, len(output_rows), config.embedding_dim, device=device\n1809: (len(output_rows), steps),\n1813: features = np.zeros((len(output_rows), steps, 3), dtype=np.float16)\n1814: counts = np.zeros(len(output_rows), dtype=np.int32)\n1826: outputs, _ = event_sequence(\n1835: outputs[local_row, count - 1]\n1842: prepared.inference_liked_items[output_rows].astype(np.int64)\n1845: prepared.inference_disliked_items[output_rows].astype(np.int64)\n1853: prepared.inference_user_features[output_rows].astype(np.float32)\n1856: result[torch.from_numpy(output_rows.astype(np.int64))] = queries.cpu()\n1861:def rank_candidates(\n1912:def write_ranking(frame: pd.DataFrame, output: Path) -> None:\n1913: output.parent.mkdir(parents=True, exist_ok=True)\n1914: if output.suffix == \".parquet\":\n1915: frame.to_parquet(output, index=False, compression=\"zstd\")\n1916: elif output.suffix == \".csv\":\n1917: frame.to_csv(output, index=False)\n1919: raise ValueError(\"--output must end in .parquet or .csv\")\n1922:def main() -> int:\n1923: parser = argparse.ArgumentParser(description=__doc__)\n1925: \"--stage\", choices=(\"validation\", \"submission\"), default=\"validation\"\n1927: parser.add_argument(\"--output\", type=Path, required=True)\n1929: parser.add_argument(\"--prepared-cache\", type=Path)\n1944: \"--negative-filter-scope\",\n1946: default=Config.negative_filter_scope,\n1974: \"--validation-fraction\",\n1976: default=Config.validation_fraction,\n1982: help=\"retain this many recent pre-validation targets per user; 0 is uncapped\",\n1985: \"--max-validation-targets-per-user\",\n1987: default=Config.max_validation_targets_per_user,\n1988: help=\"retain this many final validation targets per user; 0 is uncapped\",\n2020: or args.max_validation_targets_per_user < 0\n2023: if not 0.0 < args.validation_fraction < 1.0:\n2024: parser.error(\"validation fraction must be strictly between zero and one\")\n2034: negative_filter_scope=args.negative_filter_scope,\n2040: validation_fraction=args.validation_fraction,\n2042: max_validation_targets_per_user=args.max_validation_targets_per_user,\n2057: args.stage,\n2059: args.prepared_cache.resolve() if args.prepared_cache else None,\n2063: config.validation_fraction,\n2065: config.max_validation_targets_per_user,\n if not len(items):\n return None\n\n event_features = np.zeros((len(items), 3), dtype=np.float16)\n event_features[:, 0] = organic.astype(np.float16)\n event_features[:, 1] = (\n np.minimum(completion, 100.0) / 100.0\n ).astype(np.float16)\n if len(items) > 1:\n gaps = np.maximum(0, timestamps[1:] - timestamps[:-1])\n event_features[1:, 2] = np.exp(\n -gaps.astype(np.float64) / (60.0 * base.DAY)\n ).astype(np.float16)\n\n activity = causal_activity_features(\n timestamps, items, organic, completion\n )\n states = np.zeros(len(items), dtype=np.uint32)\n active_likes: dict[int, int] = {}\n active_dislikes: dict[int, int] = {}\n explicit_position = 0\n current_state = 0\n for row, timestamp in enumerate(timestamps):\n changed = False\n while (\n explicit_position < len(explicit.timestamps)\n and int(explicit.timestamps[explicit_position]) < int(timestamp)\n ):\n item = int(explicit.items[explicit_position])\n event_timestamp = int(explicit.timestamps[explicit_position])\n destination = (\n active_likes\n if int(explicit.channel[explicit_position]) == 0\n else active_dislikes\n )\n if int(explicit.active[explicit_position]) == 1:\n destination[item] = event_timestamp\n else:\n destination.pop(item, None)\n explicit_position += 1\n changed = True\n if changed:\n current_state = append_state_snapshot(\n active_likes,\n active_dislikes,\n n_items,\n liked_states,\n disliked_states,\n state_counts,\n )\n states[row] = np.uint32(current_state)\n return timestamps, items.copy(), event_features, activity, states\n\n\ndef final_user_features(\n timestamps: np.ndarray,\n items: np.ndarray,\n event_features: np.ndarray,\n history_end: int,\n like_count: int,\n dislike_count: int,\n) -> np.ndarray:\n start = int(\n np.searchsorted(\n timestamps,\n history_end - base.PROFILE_LOOKBACK_DAYS * base.DAY,\n side=\"left\",\n )\n )\n result = np.zeros(6, dtype=np.float32)\n recent_items = items[start:]\n if len(recent_items):\n result[0] = np.log1p(len(recent_items)) / 8.0\n result[1] = np.log1p(len(np.unique(recent_items))) / 6.0\n result[2] = float(np.mean(event_features[start:, 0]))\n result[3] = float(np.mean(event_features[start:, 1]))\n result[4] = np.log1p(like_count) / 6.0\n result[5] = np.log1p(dislike_count) / 4.0\n return result\n\n\ndef prepare_data(\n public: Path, stage: str, config: Config\n) -> tuple[SequenceData, CandidateSet]:\n data = public / \"data\"\n catalog = pd.read_parquet(data / \"candidate_catalog.parquet\").sort_values(\n \"popularity_rank\"\n )\n item_ids = catalog.item_id.to_numpy(dtype=np.int64, copy=True)\n item_index = pd.Index(item_ids)\n n_items = len(item_ids)\n if n_items >= np.iinfo(np.uint16).max:\n raise RuntimeError(\"uint16 item storage is too small for this catalog\")\n\n if stage == \"validation\":\n prefixes = (\"train\",)\n history_end = base.TRAIN_END\n candidate_name = \"validation_eligible_candidates.npz\"\n state_name = \"validation_preference_state.parquet\"\n else:\n prefixes = (\"train\", \"val\")\n history_end = base.VALIDATION_END\n candidate_name = \"test_eligible_candidates.npz\"\n state_name = \"test_preference_state.parquet\"\n eligibility = CandidateSet.read(data / candidate_name)\n if not np.array_equal(eligibility.item_ids, item_ids):\n raise RuntimeError(\"catalog and candidate artifact item order differ\")\n\n explicit_by_user = base.load_explicit_events(data, prefixes, item_index)\n inference_likes, inference_dislikes, like_counts, dislike_counts = (\n base.load_inference_state(\n data / state_name,\n item_index,\n eligibility.uids,\n n_items,\n )\n )\n inference_rows = {int(uid): row for row, uid in enumerate(eligibility.uids)}\n inference_user_features = np.zeros((len(eligibility.uids), 6), dtype=np.float32)\n inference_sequence_rows = np.full(len(eligibility.uids), -1, dtype=np.int32)\n\n # Snapshot zero is the shared empty explicit state.\n liked_states = [np.full(base.LIKE_ITEMS, n_items, dtype=np.uint16)]\n disliked_states = [\n np.full(base.DISLIKE_ITEMS, n_items, dtype=np.uint16)\n ]\n state_counts: list[tuple[int, int]] = [(0, 0)]\n user_ids: list[int] = []\n offsets = [0]\n timestamp_parts: list[np.ndarray] = []\n item_parts: list[np.ndarray] = []\n event_parts: list[np.ndarray] = []\n activity_parts: list[np.ndarray] = []\n state_parts: list[np.ndarray] = []\n\n listen_paths = [data / f\"{prefix}_listens.parquet\" for prefix in prefixes]\n started = time.monotonic()\n for seen_users, (uid, listens) in enumerate(\n base.merge_listen_streams(listen_paths, item_index), start=1\n ):\n parts = build_sequence_parts(\n listens,\n explicit_by_user.get(uid, base.empty_explicit_events()),\n n_items,\n liked_states,\n disliked_states,\n state_counts,\n )\n if parts is None:\n continue\n sequence_row = len(user_ids)\n user_ids.append(uid)\n timestamp_parts.append(parts[0].astype(np.uint32, copy=False))\n item_parts.append(parts[1])\n event_parts.append(parts[2])\n activity_parts.append(parts[3])\n state_parts.append(parts[4])\n offsets.append(offsets[-1] + len(parts[1]))\n inference_row = inference_rows.get(uid)\n if inference_row is not None:\n inference_sequence_rows[inference_row] = sequence_row\n inference_user_features[inference_row] = final_user_features(\n parts[0],\n parts[1],\n parts[2],\n history_end,\n int(like_counts[inference_row]),\n int(dislike_counts[inference_row]),\n )\n if seen_users % 1_000 == 0:\n log(\n f\"prepared users={seen_users:,} tokens={offsets[-1]:,} \"\n f\"states={len(liked_states):,} \"\n f\"elapsed={time.monotonic() - started:.1f}s\"\n )\n\n missing = np.flatnonzero(inference_sequence_rows < 0)\n if len(missing):\n inference_user_features[missing, 4] = np.log1p(like_counts[missing]) / 6.0\n inference_user_features[missing, 5] = (\n np.log1p(dislike_counts[missing]) / 4.0\n )\n log(f\"target users without qualifying catalog sequence={len(missing):,}\")\n\n prepared = SequenceData(\n item_ids=item_ids,\n item_features=base.build_item_features(catalog),\n user_ids=np.asarray(user_ids, dtype=np.int64),\n offsets=np.asarray(offsets, dtype=np.int64),\n sequence_timestamps=np.concatenate(timestamp_parts),\n sequence_items=np.concatenate(item_parts),\n event_features=np.concatenate(event_parts),\n activity_features=np.concatenate(activity_parts),\n state_ids=np.concatenate(state_parts),\n liked_states=np.stack(liked_states),\n disliked_states=np.stack(disliked_states),\n state_counts=np.asarray(state_counts, dtype=np.int32),\n inference_user_rows=inference_sequence_rows,\n inference_liked_items=inference_likes,\n inference_disliked_items=inference_dislikes,\n inference_user_features=inference_user_features,\n )\n log(\n f\"prepared stage={stage} sequence_users={len(prepared.user_ids):,} \"\n f\"tokens={len(prepared.sequence_items):,} \"\n f\"next_item_targets={prepared.target_count:,} \"\n f\"states={len(prepared.liked_states):,}\"\n )\n return prepared, eligibility\n\n\ndef cache_metadata(stage: str, config: Config) -> dict[str, object]:\n return {\n \"cache_version\": CACHE_VERSION,\n \"stage\": stage,\n \"positive_listen_threshold\": base.LISTEN_POSITIVE_THRESHOLD,\n \"profile_lookback_days\": base.PROFILE_LOOKBACK_DAYS,\n \"event_time_feature\": \"exp-negative-gap-over-60-days\",\n }\n\n\ndef save_cache(\n path: Path, prepared: SequenceData, stage: str, config: Config\n) -> None:\n path.parent.mkdir(parents=True, exist_ok=True)\n np.savez(\n path,\n metadata=np.asarray(json.dumps(cache_metadata(stage, config), sort_keys=True)),\n **prepared.__dict__,\n )\n log(f\"saved public-derived full-sequence cache: {path}\")\n\n\ndef load_cache(path: Path, stage: str, config: Config) -> SequenceData:\n with np.load(path, allow_pickle=False) as artifact:\n actual = json.loads(str(artifact[\"metadata\"].item()))\n expected = cache_metadata(stage, config)\n if actual != expected:\n raise RuntimeError(f\"sequence cache differs: {actual} != {expected}\")\n values = {\n field: artifact[field].copy()\n for field in SequenceData.__dataclass_fields__\n }\n prepared = SequenceData(**values)\n log(\n f\"loaded full-sequence cache: tokens={len(prepared.sequence_items):,} \"\n f\"targets={prepared.target_count:,}\"\n )\n return prepared\n\n\ndef load_or_prepare(\n public: Path,\n stage: str,\n config: Config,\n cache: Path | None,\n) -> tuple[SequenceData, CandidateSet]:\n candidate_name = (\n \"validation_eligible_candidates.npz\"\n if stage == \"validation\"\n else \"test_eligible_candidates.npz\"\n )\n eligibility = CandidateSet.read(public / \"data\" / candidate_name)\n if cache is not None and cache.is_file():\n prepared = load_cache(cache, stage, config)\n if not np.array_equal(prepared.item_ids, eligibility.item_ids):\n raise RuntimeError(\"cached catalog differs from candidate artifact\")\n return prepared, eligibility\n prepared, built_eligibility = prepare_data(public, stage, config)\n if cache is not None:\n save_cache(cache, prepared, stage, config)\n return prepared, built_eligibility\n\n\ndef build_item_correction(\n prepared: SequenceData,\n config: Config,\n target_starts: np.ndarray,\n target_counts: np.ndarray,\n) -> torch.Tensor:\n \"\"\"Estimate in-batch propensities from the selected training targets.\"\"\"\n counts = np.zeros(len(prepared.item_ids), dtype=np.float64)\n for user_row, count in enumerate(target_counts):\n if not count:\n continue\n offset = int(prepared.offsets[user_row])\n target_start = int(target_starts[user_row])\n counts += np.bincount(\n prepared.sequence_items[\n offset\n + target_start\n + 1 : offset\n + target_start\n + int(count)\n + 1\n ].astype(np.int64),\n minlength=len(prepared.item_ids),\n )\n total = float(counts.sum())\n probabilities = (counts + 1.0) / (total + len(counts))\n raw = np.power(\n (1.0 / len(counts)) / probabilities,\n config.inbatch_correction_power,\n )\n normalization = float(np.sum(counts * raw) / max(total, 1.0))\n correction = np.clip(\n raw / max(normalization, 1.0e-12),\n config.inbatch_correction_min,\n config.inbatch_correction_max,\n ).astype(np.float32)\n mean_on_targets = float(np.sum(counts * correction) / max(total, 1.0))\n log(\n f\"inbatch correction: min={correction.min():.3f} \"\n f\"mean_on_targets={mean_on_targets:.3f} max={correction.max():.3f}\"\n )\n return torch.from_numpy(correction)\n\n\ndef build_known_positive_filter(\n prepared: SequenceData, training_counts: np.ndarray\n) -> np.ndarray:\n \"\"\"Pack each user's complete public-training positive-item set.\"\"\"\n width = (len(prepared.item_ids) + 7) // 8\n packed = np.zeros((len(prepared.user_ids), width), dtype=np.uint8)\n retained = 0\n for user_row, target_count in enumerate(training_counts):\n if not target_count:\n continue\n offset = int(prepared.offsets[user_row])\n # T adjacent targets consume sequence positions 0 through T. The\n # chronological BCE suffix starts at T + 1 and is intentionally absent.\n items = np.unique(\n prepared.sequence_items[\n offset : offset + int(target_count) + 1\n ].astype(np.int64)\n )\n byte_columns = items >> 3\n bit_values = np.left_shift(\n np.uint8(1), (items & 7).astype(np.uint8)\n )\n np.bitwise_or.at(packed[user_row], byte_columns, bit_values)\n retained += len(items)\n log(\n f\"full-history false-negative filter: users={len(prepared.user_ids):,} \"\n f\"unique_user_items={retained:,} bytes={packed.nbytes:,}\"\n )\n return packed\n\n\ndef invalid_full_history_negatives(\n negatives: np.ndarray,\n targets: np.ndarray,\n user_rows: np.ndarray,\n known_positive_packed: np.ndarray,\n liked_items: np.ndarray,\n) -> np.ndarray:\n \"\"\"Return row-level invalid negatives using the complete training prefix.\"\"\"\n byte_columns = negatives >> 3\n bit_columns = negatives & 7\n known = known_positive_packed[user_rows[:, None], byte_columns]\n invalid = ((known >> bit_columns) & 1).astype(bool)\n invalid |= negatives == targets[:, None]\n invalid |= (negatives[:, :, None] == liked_items[:, None, :]).any(axis=2)\n for column in range(1, negatives.shape[1]):\n invalid[:, column] |= (\n negatives[:, column, None] == negatives[:, :column]\n ).any(axis=1)\n return invalid\n\n\ndef draw_full_history_filtered_negatives(\n targets: torch.Tensor,\n user_rows: np.ndarray,\n liked_items: torch.Tensor,\n known_positive_packed: np.ndarray,\n n_items: int,\n config: Config,\n item_correction: torch.Tensor,\n generator: torch.Generator,\n device: torch.device,\n) -> tuple[torch.Tensor, torch.Tensor]:\n \"\"\"Draw mixed negatives and reject all known public-training positives.\"\"\"\n targets_cpu = targets.detach().cpu().numpy().astype(np.int64, copy=False)\n liked_cpu = liked_items.detach().cpu().numpy().astype(np.int64, copy=False)\n batch_size = len(targets_cpu)\n uniform = torch.randint(\n n_items,\n (batch_size, config.uniform_negatives),\n generator=generator,\n device=\"cpu\",\n )\n inbatch = torch.empty((batch_size, 0), dtype=torch.long)\n corrected_count = 0\n if config.inbatch_negatives:\n unique_targets = torch.unique(targets.detach().cpu())\n corrected_count = min(\n config.inbatch_negatives, max(len(unique_targets) - 1, 0)\n )\n if corrected_count:\n priorities = torch.rand(\n (batch_size, len(unique_targets)), generator=generator\n )\n priorities.masked_fill_(\n unique_targets[None, :].eq(\n torch.from_numpy(targets_cpu)[:, None]\n ),\n -1.0,\n )\n columns = torch.topk(\n priorities, k=corrected_count, dim=1\n ).indices\n inbatch = unique_targets[columns]\n if corrected_count < config.inbatch_negatives:\n filler = torch.randint(\n n_items,\n (batch_size, config.inbatch_negatives - corrected_count),\n generator=generator,\n device=\"cpu\",\n )\n inbatch = torch.cat([inbatch, filler], dim=1)\n\n negatives = torch.cat([uniform, inbatch], dim=1).numpy()\n correction = np.ones_like(negatives, dtype=np.float32)\n if corrected_count:\n correction[\n :, config.uniform_negatives : config.uniform_negatives + corrected_count\n ] = item_correction[inbatch[:, :corrected_count]].numpy()\n for _ in range(32):\n invalid = invalid_full_history_negatives(\n negatives,\n targets_cpu,\n user_rows,\n known_positive_packed,\n liked_cpu,\n )\n if not np.any(invalid):\n return (\n torch.from_numpy(negatives).to(device),\n torch.from_numpy(correction).to(device),\n )\n replacements = torch.randint(\n n_items,\n (int(invalid.sum()),),\n generator=generator,\n device=\"cpu\",\n ).numpy()\n negatives[invalid] = replacements\n # Rejected in-batch items become ordinary uniform draws.\n correction[invalid] = 1.0\n invalid = invalid_full_history_negatives(\n negatives,\n targets_cpu,\n user_rows,\n known_positive_packed,\n liked_cpu,\n )\n if np.any(invalid):\n raise RuntimeError(\"full-history negative resampling failed\")\n return (\n torch.from_numpy(negatives).to(device),\n torch.from_numpy(correction).to(device),\n )\n\n\ndef draw_training_negatives(\n positive_items: torch.Tensor,\n known_history: torch.Tensor,\n liked_items: torch.Tensor,\n user_rows: np.ndarray,\n known_positive_packed: np.ndarray | None,\n prepared: SequenceData,\n config: Config,\n item_correction: torch.Tensor,\n generator: torch.Generator,\n device: torch.device,\n) -> tuple[torch.Tensor, torch.Tensor]:\n if config.negative_filter_scope == \"full-training-history\":\n if known_positive_packed is None:\n raise RuntimeError(\"full-history negative filter was not built\")\n return draw_full_history_filtered_negatives(\n positive_items,\n user_rows,\n liked_items,\n known_positive_packed,\n len(prepared.item_ids),\n config,\n item_correction,\n generator,\n device,\n )\n return base.draw_filtered_mixed_negatives(\n positive_items,\n known_history,\n liked_items,\n len(prepared.item_ids),\n config.uniform_negatives,\n config.inbatch_negatives,\n item_correction,\n generator,\n device,\n )\n input_size=config.hidden_dim,\n hidden_size=dim,\n num_layers=1,\n batch_first=True,\n )\n self.user_tower = nn.Sequential(\n nn.Linear(dim * 3 + 6, config.user_hidden_1),\n nn.GELU(),\n nn.Dropout(config.dropout),\n nn.Linear(config.user_hidden_1, config.user_hidden_2),\n nn.GELU(),\n nn.Dropout(config.dropout),\n nn.Linear(config.user_hidden_2, dim),\n )\n self.candidate_embedding = nn.Embedding(n_items, dim)\n self.register_buffer(\n \"item_features\", torch.from_numpy(item_features), persistent=True\n )\n self.item_tower = nn.Sequential(\n nn.Linear(dim + n_item_features, config.item_hidden_1),\n nn.GELU(),\n nn.Dropout(config.dropout),\n nn.Linear(config.item_hidden_1, config.item_hidden_2),\n nn.GELU(),\n nn.Dropout(config.dropout),\n nn.Linear(config.item_hidden_2, dim),\n )\n nn.init.normal_(self.context_embedding.weight, std=0.02)\n nn.init.normal_(self.candidate_embedding.weight, std=0.02)\n with torch.no_grad():\n self.context_embedding.weight[n_items].zero_()\n\n def _mean_context_channel(self, items: torch.Tensor) -> torch.Tensor:\n mask = items.ne(self.n_items)\n vectors = self.context_embedding(items)\n return (vectors * mask.unsqueeze(-1)).sum(dim=1) / mask.sum(\n dim=1, keepdim=True\n ).clamp_min(1)\n\n def encode_items(self, items: torch.Tensor) -> torch.Tensor:\n inputs = torch.cat(\n [self.candidate_embedding(items), self.item_features[items]], dim=1\n )\n return self.item_tower(inputs)\n\n\ndef invalid_negative_mask(\n negatives: torch.Tensor,\n targets: torch.Tensor,\n context_items: torch.Tensor,\n liked_items: torch.Tensor,\n) -> torch.Tensor:\n \"\"\"Identify targets, known positives, active likes, and row duplicates.\"\"\"\n invalid = negatives.eq(targets[:, None])\n invalid |= negatives[:, :, None].eq(context_items[:, None, :]).any(dim=2)\n invalid |= negatives[:, :, None].eq(liked_items[:, None, :]).any(dim=2)\n for column in range(1, negatives.shape[1]):\n invalid[:, column] |= negatives[:, column, None].eq(\n negatives[:, :column]\n ).any(dim=1)\n return invalid\n\n\ndef draw_filtered_mixed_negatives(\n targets: torch.Tensor,\n context_items: torch.Tensor,\n liked_items: torch.Tensor,\n n_items: int,\n uniform_count: int,\n inbatch_count: int,\n item_correction: torch.Tensor,\n generator: torch.Generator,\n device: torch.device,\n) -> tuple[torch.Tensor, torch.Tensor]:\n \"\"\"Draw uniform and unique-target in-batch negatives.\"\"\"\n batch_size = len(targets)\n uniform = torch.randint(\n n_items,\n (batch_size, uniform_count),\n generator=generator,\n device=\"cpu\",\n )\n inbatch = torch.empty((batch_size, 0), dtype=torch.long, device=\"cpu\")\n corrected_count = 0\n if inbatch_count:\n unique_targets = torch.unique(targets.detach().cpu())\n corrected_count = min(inbatch_count, max(len(unique_targets) - 1, 0))\n if corrected_count:\n priorities = torch.rand(\n (batch_size, len(unique_targets)), generator=generator\n )\n priorities.masked_fill_(\n unique_targets[None, :].eq(targets.detach().cpu()[:, None]),\n -1.0,\n )\n columns = torch.topk(priorities, k=corrected_count, dim=1).indices\n inbatch = unique_targets[columns]\n if corrected_count < inbatch_count:\n filler = torch.randint(\n n_items,\n (batch_size, inbatch_count - corrected_count),\n generator=generator,\n device=\"cpu\",\n )\n inbatch = torch.cat([inbatch, filler], dim=1)\n\n negatives_cpu = torch.cat([uniform, inbatch], dim=1)\n correction_cpu = torch.ones_like(negatives_cpu, dtype=torch.float32)\n if corrected_count:\n correction_cpu[\n :, uniform_count : uniform_count + corrected_count\n ] = item_correction[inbatch[:, :corrected_count]]\n negatives = negatives_cpu.to(device)\n correction = correction_cpu.to(device)\n for _ in range(32):\n invalid = invalid_negative_mask(\n negatives, targets, context_items, liked_items\n )\n if not bool(invalid.any()):\n return negatives, correction\n replacements = torch.randint(\n n_items,\n (int(invalid.sum().detach().cpu()),),\n generator=generator,\n device=\"cpu\",\n ).to(device)\n negatives[invalid] = replacements\n correction[invalid] = 1.0\n invalid = invalid_negative_mask(\n negatives, targets, context_items, liked_items\n )\n if bool(invalid.any()):\n raise RuntimeError(\"mixed negative resampling failed false-negative filter\")\n return negatives, correction\n", "stderr": "", "aggregated_output": "7:16 held-out validation targets; passing zero for either cap restores the\n12:The model is a pure two-tower retriever: no user-ID embedding, no output\n14:and 24 uniform plus eight propensity-corrected unique in-batch negatives. Every\n20:import argparse\n41:class Config:\n56: uniform_negatives: int = 24\n57: inbatch_negatives: int = 8\n61: negative_filter_scope: str = \"full-training-history\"\n67: validation_fraction: float = 0.10\n69: max_validation_targets_per_user: int = 16\n76:class CandidateSet:\n99:class SequenceData:\n127:class TargetPartition:\n132: validation_counts: np.ndarray\n137: return int(self.training_counts.sum() + self.validation_counts.sum())\n140:def log(message: str) -> None:\n144:def seed_everything(seed: int) -> None:\n150:def model_config(config: Config) -> base.TowerConfig:\n165: uniform_negatives=config.uniform_negatives,\n166: inbatch_negatives=config.inbatch_negatives,\n174:def append_state_snapshot(\n190:def causal_activity_features(\n247:def build_sequence_parts(\n314:def final_user_features(\n341:def prepare_data(\n342: public: Path, stage: str, config: Config\n354: if stage == \"validation\":\n357: candidate_name = \"validation_eligible_candidates.npz\"\n358: state_name = \"validation_preference_state.parquet\"\n463: f\"prepared stage={stage} sequence_users={len(prepared.user_ids):,} \"\n471:def cache_metadata(stage: str, config: Config) -> dict[str, object]:\n473: \"cache_version\": CACHE_VERSION,\n474: \"stage\": stage,\n477: \"event_time_feature\": \"exp-negative-gap-over-60-days\",\n481:def save_cache(\n482: path: Path, prepared: SequenceData, stage: str, config: Config\n487: metadata=np.asarray(json.dumps(cache_metadata(stage, config), sort_keys=True)),\n490: log(f\"saved public-derived full-sequence cache: {path}\")\n493:def load_cache(path: Path, stage: str, config: Config) -> SequenceData:\n496: expected = cache_metadata(stage, config)\n498: raise RuntimeError(f\"sequence cache differs: {actual} != {expected}\")\n505: f\"loaded full-sequence cache: tokens={len(prepared.sequence_items):,} \"\n511:def load_or_prepare(\n513: stage: str,\n515: cache: Path | None,\n518: \"validation_eligible_candidates.npz\"\n519: if stage == \"validation\"\n523: if cache is not None and cache.is_file():\n524: prepared = load_cache(cache, stage, config)\n526: raise RuntimeError(\"cached catalog differs from candidate artifact\")\n528: prepared, built_eligibility = prepare_data(public, stage, config)\n529: if cache is not None:\n530: save_cache(cache, prepared, stage, config)\n534:def build_item_correction(\n578:def build_known_positive_filter(\n603: f\"full-history false-negative filter: users={len(prepared.user_ids):,} \"\n609:def invalid_full_history_negatives(\n610: negatives: np.ndarray,\n616: \"\"\"Return row-level invalid negatives using the complete training prefix.\"\"\"\n617: byte_columns = negatives >> 3\n618: bit_columns = negatives & 7\n621: invalid |= negatives == targets[:, None]\n622: invalid |= (negatives[:, :, None] == liked_items[:, None, :]).any(axis=2)\n623: for column in range(1, negatives.shape[1]):\n625: negatives[:, column, None] == negatives[:, :column]\n630:def draw_full_history_filtered_negatives(\n641: \"\"\"Draw mixed negatives and reject all known public-training positives.\"\"\"\n647: (batch_size, config.uniform_negatives),\n653: if config.inbatch_negatives:\n656: config.inbatch_negatives, max(len(unique_targets) - 1, 0)\n672: if corrected_count < config.inbatch_negatives:\n675: (batch_size, config.inbatch_negatives - corrected_count),\n681: negatives = torch.cat([uniform, inbatch], dim=1).numpy()\n682: correction = np.ones_like(negatives, dtype=np.float32)\n685: :, config.uniform_negatives : config.uniform_negatives + corrected_count\n688: invalid = invalid_full_history_negatives(\n689: negatives,\n697: torch.from_numpy(negatives).to(device),\n706: negatives[invalid] = replacements\n709: invalid = invalid_full_history_negatives(\n710: negatives,\n717: raise RuntimeError(\"full-history negative resampling failed\")\n719: torch.from_numpy(negatives).to(device),\n724:def draw_training_negatives(\n736: if config.negative_filter_scope == \"full-training-history\":\n738: raise RuntimeError(\"full-history negative filter was not built\")\n739: return draw_full_history_filtered_negatives(\n750: return base.draw_filtered_mixed_negatives(\n755: config.uniform_negatives,\n756: config.inbatch_negatives,\n763:def event_sequence(\n774:def pool_explicit_states(\n798:def user_queries(\n813:def candidate_vectors(\n816: negatives: torch.Tensor,\n818: all_items = torch.cat([positives, negatives.reshape(-1)])\n822: negative_vectors = encoded[inverse[len(positives) :]].reshape(\n823: len(positives), negatives.shape[1], -1\n825: return positive_vectors, negative_vectors\n828:def history_windows(\n845:def corrected_negative_loss(\n851: total = config.uniform_negatives + config.inbatch_negatives\n853: if config.uniform_negatives:\n854: result += config.uniform_negatives * raw[\n855: :, : config.uniform_negatives\n857: if config.inbatch_negatives:\n858: selected = slice(config.uniform_negatives, total)\n860: result += config.inbatch_negatives * (\n867:def retained_target_window_starts(\n869: validation_counts: np.ndarray,\n873: training_region_ends = total_targets - validation_counts\n878: or np.any(validation_counts < 0)\n880: raise RuntimeError(\"chronological target partition produced negative counts\")\n882: window_starts + training_counts + validation_counts, total_targets\n888:def chronological_target_partition(\n890: validation_fraction: float,\n892: max_validation_targets_per_user: int,\n896: validation = np.floor(totals * validation_fraction).astype(np.int64)\n897: validation[(totals >= 2) & (validation == 0)] = 1\n898: if max_validation_targets_per_user:\n899: validation = np.minimum(validation, max_validation_targets_per_user)\n901: history_training = totals - validation\n905: window_starts = retained_target_window_starts(totals, validation, training)\n909: validation_counts=validation,\n914:def load_organic_like_outcomes(\n915: public: Path, stage: str, item_ids: np.ndarray\n918: prefixes = (\"train\",) if stage == \"validation\" else (\"train\", \"val\")\n955:def build_target_weights(\n957: stage: str,\n978: public, stage, prepared.item_ids\n981: base.TRAIN_END if stage == \"validation\" else base.VALIDATION_END\n991: first_validation_target = last_training_target + 1\n993: int(prepared.sequence_timestamps[first_validation_target])\n994: if first_validation_target < end\n1035:def build_dense_block(\n1111:def score_target_microbatches(\n1119: negatives: torch.Tensor,\n1120: negative_weights: torch.Tensor,\n1134: \"negative_sum\": 0.0,\n1162: positive_vectors, negative_vectors = candidate_vectors(\n1165: negatives[begin:end],\n1168: negative_logits = torch.einsum(\n1169: \"bd,bkd->bk\", query_vectors, negative_vectors\n1172: negative_loss = corrected_negative_loss(\n1173: negative_logits, negative_weights[begin:end], config\n1179: selected_weights * (positive_loss + negative_loss) / 2.0\n1190: statistics[\"negative_sum\"] += float(negative_logits.detach().sum().cpu())\n1192: (positive_logits.detach()[:, None] > negative_logits.detach()).sum().cpu()\n1198:def release_accelerator_cache(device: torch.device) -> None:\n1200: torch.mps.empty_cache()\n1203:def run_training_epoch(\n1227: negative_count = config.uniform_negatives + config.inbatch_negatives\n1232: total_negative = 0.0\n1257: sequence_outputs, _ = event_sequence(\n1265: sequence_outputs[row, count - 1]\n1275: sequence_vectors = sequence_outputs.reshape(\n1293: negatives, negative_weights = draw_training_negatives(\n1314: negatives,\n1315: negative_weights,\n1331: total_negative += statistics[\"negative_sum\"]\n1335: release_accelerator_cache(device)\n1353: \"negative_logit\": total_negative / max(total_targets * negative_count, 1),\n1355: / max(total_targets * negative_count, 1),\n1364:def evaluate_validation_bce(\n1371: validation_counts: np.ndarray,\n1378: totals = training_counts + validation_counts\n1379: users_with_validation = np.flatnonzero(validation_counts > 0)\n1380: ordered = users_with_validation[\n1381: np.argsort(totals[users_with_validation], kind=\"stable\")\n1413: sequence_outputs, _ = event_sequence(\n1421: sequence_outputs[row, count - 1]\n1431: sequence_vectors = sequence_outputs.reshape(\n1449: negatives, negative_weights = draw_training_negatives(\n1469: negatives,\n1470: negative_weights,\n1479: release_accelerator_cache(device)\n1481: \"validation_loss\": total_loss / max(total_weight, 1.0e-12),\n1482: \"validation_targets\": float(total_targets),\n1483: \"validation_target_weight_sum\": total_weight,\n1484: \"validation_seconds\": time.monotonic() - started,\n1488:def snapshot_model(model: nn.Module) -> dict[str, torch.Tensor]:\n1495:def save_epoch_checkpoint(\n1521:def train_model(\n1523: stage: str,\n1532: config.validation_fraction,\n1534: config.max_validation_targets_per_user,\n1537: validation_counts = partition.validation_counts\n1540: f\"validation={int(validation_counts.sum()):,} \"\n1560: stage,\n1567: if config.negative_filter_scope == \"full-training-history\"\n1601: \"negative_filter_scope\", \"last-100\"\n1619: stored_config.get(\"validation_fraction\", 0.10),\n1621: stored_config.get(\"max_validation_targets_per_user\", 0),\n1624: config.validation_fraction,\n1626: config.max_validation_targets_per_user,\n1629: stored_filter_scope != config.negative_filter_scope\n1657: prior.pop(\"validation_loss\", None)\n1660: f\"{stored_filter_scope}->{config.negative_filter_scope}, \"\n1664: \"recomputing the resume checkpoint's validation BCE\"\n1666: if \"validation_loss\" not in prior:\n1668: evaluate_validation_bce(\n1675: validation_counts,\n1682: best_loss = float(prior[\"validation_loss\"])\n1686: f\"resumed validation-BCE training: epoch={start_epoch} \"\n1705: validation = evaluate_validation_bce(\n1712: validation_counts,\n1717: diagnostics.update(validation)\n1731: diagnostics[\"validation_loss\"]\n1735: best_loss = diagnostics[\"validation_loss\"]\n1755: f\"validation_loss={diagnostics['validation_loss']:.5f} \"\n1758: f\"validation_targets={int(diagnostics['validation_targets']):,} \"\n1760: f\"validation_seconds={diagnostics['validation_seconds']:.1f}\"\n1762: release_accelerator_cache(device)\n1766: f\"best_epoch={best_epoch} best_validation_loss={best_loss:.6f}\"\n1775: f\"restored best validation-BCE checkpoint: epoch={best_epoch} \"\n1782:def inference_queries(\n1797: output_rows = ordered[group_start : group_start + config.user_batch_size]\n1798: rows = sequence_rows[output_rows]\n1799: group_lengths = lengths[output_rows]\n1801: 1, len(output_rows), config.embedding_dim, device=device\n1809: (len(output_rows), steps),\n1813: features = np.zeros((len(output_rows), steps, 3), dtype=np.float16)\n1814: counts = np.zeros(len(output_rows), dtype=np.int32)\n1826: outputs, _ = event_sequence(\n1835: outputs[local_row, count - 1]\n1842: prepared.inference_liked_items[output_rows].astype(np.int64)\n1845: prepared.inference_disliked_items[output_rows].astype(np.int64)\n1853: prepared.inference_user_features[output_rows].astype(np.float32)\n1856: result[torch.from_numpy(output_rows.astype(np.int64))] = queries.cpu()\n1861:def rank_candidates(\n1912:def write_ranking(frame: pd.DataFrame, output: Path) -> None:\n1913: output.parent.mkdir(parents=True, exist_ok=True)\n1914: if output.suffix == \".parquet\":\n1915: frame.to_parquet(output, index=False, compression=\"zstd\")\n1916: elif output.suffix == \".csv\":\n1917: frame.to_csv(output, index=False)\n1919: raise ValueError(\"--output must end in .parquet or .csv\")\n1922:def main() -> int:\n1923: parser = argparse.ArgumentParser(description=__doc__)\n1925: \"--stage\", choices=(\"validation\", \"submission\"), default=\"validation\"\n1927: parser.add_argument(\"--output\", type=Path, required=True)\n1929: parser.add_argument(\"--prepared-cache\", type=Path)\n1944: \"--negative-filter-scope\",\n1946: default=Config.negative_filter_scope,\n1974: \"--validation-fraction\",\n1976: default=Config.validation_fraction,\n1982: help=\"retain this many recent pre-validation targets per user; 0 is uncapped\",\n1985: \"--max-validation-targets-per-user\",\n1987: default=Config.max_validation_targets_per_user,\n1988: help=\"retain this many final validation targets per user; 0 is uncapped\",\n2020: or args.max_validation_targets_per_user < 0\n2023: if not 0.0 < args.validation_fraction < 1.0:\n2024: parser.error(\"validation fraction must be strictly between zero and one\")\n2034: negative_filter_scope=args.negative_filter_scope,\n2040: validation_fraction=args.validation_fraction,\n2042: max_validation_targets_per_user=args.max_validation_targets_per_user,\n2057: args.stage,\n2059: args.prepared_cache.resolve() if args.prepared_cache else None,\n2063: config.validation_fraction,\n2065: config.max_validation_targets_per_user,\n if not len(items):\n return None\n\n event_features = np.zeros((len(items), 3), dtype=np.float16)\n event_features[:, 0] = organic.astype(np.float16)\n event_features[:, 1] = (\n np.minimum(completion, 100.0) / 100.0\n ).astype(np.float16)\n if len(items) > 1:\n gaps = np.maximum(0, timestamps[1:] - timestamps[:-1])\n event_features[1:, 2] = np.exp(\n -gaps.astype(np.float64) / (60.0 * base.DAY)\n ).astype(np.float16)\n\n activity = causal_activity_features(\n timestamps, items, organic, completion\n )\n states = np.zeros(len(items), dtype=np.uint32)\n active_likes: dict[int, int] = {}\n active_dislikes: dict[int, int] = {}\n explicit_position = 0\n current_state = 0\n for row, timestamp in enumerate(timestamps):\n changed = False\n while (\n explicit_position < len(explicit.timestamps)\n and int(explicit.timestamps[explicit_position]) < int(timestamp)\n ):\n item = int(explicit.items[explicit_position])\n event_timestamp = int(explicit.timestamps[explicit_position])\n destination = (\n active_likes\n if int(explicit.channel[explicit_position]) == 0\n else active_dislikes\n )\n if int(explicit.active[explicit_position]) == 1:\n destination[item] = event_timestamp\n else:\n destination.pop(item, None)\n explicit_position += 1\n changed = True\n if changed:\n current_state = append_state_snapshot(\n active_likes,\n active_dislikes,\n n_items,\n liked_states,\n disliked_states,\n state_counts,\n )\n states[row] = np.uint32(current_state)\n return timestamps, items.copy(), event_features, activity, states\n\n\ndef final_user_features(\n timestamps: np.ndarray,\n items: np.ndarray,\n event_features: np.ndarray,\n history_end: int,\n like_count: int,\n dislike_count: int,\n) -> np.ndarray:\n start = int(\n np.searchsorted(\n timestamps,\n history_end - base.PROFILE_LOOKBACK_DAYS * base.DAY,\n side=\"left\",\n )\n )\n result = np.zeros(6, dtype=np.float32)\n recent_items = items[start:]\n if len(recent_items):\n result[0] = np.log1p(len(recent_items)) / 8.0\n result[1] = np.log1p(len(np.unique(recent_items))) / 6.0\n result[2] = float(np.mean(event_features[start:, 0]))\n result[3] = float(np.mean(event_features[start:, 1]))\n result[4] = np.log1p(like_count) / 6.0\n result[5] = np.log1p(dislike_count) / 4.0\n return result\n\n\ndef prepare_data(\n public: Path, stage: str, config: Config\n) -> tuple[SequenceData, CandidateSet]:\n data = public / \"data\"\n catalog = pd.read_parquet(data / \"candidate_catalog.parquet\").sort_values(\n \"popularity_rank\"\n )\n item_ids = catalog.item_id.to_numpy(dtype=np.int64, copy=True)\n item_index = pd.Index(item_ids)\n n_items = len(item_ids)\n if n_items >= np.iinfo(np.uint16).max:\n raise RuntimeError(\"uint16 item storage is too small for this catalog\")\n\n if stage == \"validation\":\n prefixes = (\"train\",)\n history_end = base.TRAIN_END\n candidate_name = \"validation_eligible_candidates.npz\"\n state_name = \"validation_preference_state.parquet\"\n else:\n prefixes = (\"train\", \"val\")\n history_end = base.VALIDATION_END\n candidate_name = \"test_eligible_candidates.npz\"\n state_name = \"test_preference_state.parquet\"\n eligibility = CandidateSet.read(data / candidate_name)\n if not np.array_equal(eligibility.item_ids, item_ids):\n raise RuntimeError(\"catalog and candidate artifact item order differ\")\n\n explicit_by_user = base.load_explicit_events(data, prefixes, item_index)\n inference_likes, inference_dislikes, like_counts, dislike_counts = (\n base.load_inference_state(\n data / state_name,\n item_index,\n eligibility.uids,\n n_items,\n )\n )\n inference_rows = {int(uid): row for row, uid in enumerate(eligibility.uids)}\n inference_user_features = np.zeros((len(eligibility.uids), 6), dtype=np.float32)\n inference_sequence_rows = np.full(len(eligibility.uids), -1, dtype=np.int32)\n\n # Snapshot zero is the shared empty explicit state.\n liked_states = [np.full(base.LIKE_ITEMS, n_items, dtype=np.uint16)]\n disliked_states = [\n np.full(base.DISLIKE_ITEMS, n_items, dtype=np.uint16)\n ]\n state_counts: list[tuple[int, int]] = [(0, 0)]\n user_ids: list[int] = []\n offsets = [0]\n timestamp_parts: list[np.ndarray] = []\n item_parts: list[np.ndarray] = []\n event_parts: list[np.ndarray] = []\n activity_parts: list[np.ndarray] = []\n state_parts: list[np.ndarray] = []\n\n listen_paths = [data / f\"{prefix}_listens.parquet\" for prefix in prefixes]\n started = time.monotonic()\n for seen_users, (uid, listens) in enumerate(\n base.merge_listen_streams(listen_paths, item_index), start=1\n ):\n parts = build_sequence_parts(\n listens,\n explicit_by_user.get(uid, base.empty_explicit_events()),\n n_items,\n liked_states,\n disliked_states,\n state_counts,\n )\n if parts is None:\n continue\n sequence_row = len(user_ids)\n user_ids.append(uid)\n timestamp_parts.append(parts[0].astype(np.uint32, copy=False))\n item_parts.append(parts[1])\n event_parts.append(parts[2])\n activity_parts.append(parts[3])\n state_parts.append(parts[4])\n offsets.append(offsets[-1] + len(parts[1]))\n inference_row = inference_rows.get(uid)\n if inference_row is not None:\n inference_sequence_rows[inference_row] = sequence_row\n inference_user_features[inference_row] = final_user_features(\n parts[0],\n parts[1],\n parts[2],\n history_end,\n int(like_counts[inference_row]),\n int(dislike_counts[inference_row]),\n )\n if seen_users % 1_000 == 0:\n log(\n f\"prepared users={seen_users:,} tokens={offsets[-1]:,} \"\n f\"states={len(liked_states):,} \"\n f\"elapsed={time.monotonic() - started:.1f}s\"\n )\n\n missing = np.flatnonzero(inference_sequence_rows < 0)\n if len(missing):\n inference_user_features[missing, 4] = np.log1p(like_counts[missing]) / 6.0\n inference_user_features[missing, 5] = (\n np.log1p(dislike_counts[missing]) / 4.0\n )\n log(f\"target users without qualifying catalog sequence={len(missing):,}\")\n\n prepared = SequenceData(\n item_ids=item_ids,\n item_features=base.build_item_features(catalog),\n user_ids=np.asarray(user_ids, dtype=np.int64),\n offsets=np.asarray(offsets, dtype=np.int64),\n sequence_timestamps=np.concatenate(timestamp_parts),\n sequence_items=np.concatenate(item_parts),\n event_features=np.concatenate(event_parts),\n activity_features=np.concatenate(activity_parts),\n state_ids=np.concatenate(state_parts),\n liked_states=np.stack(liked_states),\n disliked_states=np.stack(disliked_states),\n state_counts=np.asarray(state_counts, dtype=np.int32),\n inference_user_rows=inference_sequence_rows,\n inference_liked_items=inference_likes,\n inference_disliked_items=inference_dislikes,\n inference_user_features=inference_user_features,\n )\n log(\n f\"prepared stage={stage} sequence_users={len(prepared.user_ids):,} \"\n f\"tokens={len(prepared.sequence_items):,} \"\n f\"next_item_targets={prepared.target_count:,} \"\n f\"states={len(prepared.liked_states):,}\"\n )\n return prepared, eligibility\n\n\ndef cache_metadata(stage: str, config: Config) -> dict[str, object]:\n return {\n \"cache_version\": CACHE_VERSION,\n \"stage\": stage,\n \"positive_listen_threshold\": base.LISTEN_POSITIVE_THRESHOLD,\n \"profile_lookback_days\": base.PROFILE_LOOKBACK_DAYS,\n \"event_time_feature\": \"exp-negative-gap-over-60-days\",\n }\n\n\ndef save_cache(\n path: Path, prepared: SequenceData, stage: str, config: Config\n) -> None:\n path.parent.mkdir(parents=True, exist_ok=True)\n np.savez(\n path,\n metadata=np.asarray(json.dumps(cache_metadata(stage, config), sort_keys=True)),\n **prepared.__dict__,\n )\n log(f\"saved public-derived full-sequence cache: {path}\")\n\n\ndef load_cache(path: Path, stage: str, config: Config) -> SequenceData:\n with np.load(path, allow_pickle=False) as artifact:\n actual = json.loads(str(artifact[\"metadata\"].item()))\n expected = cache_metadata(stage, config)\n if actual != expected:\n raise RuntimeError(f\"sequence cache differs: {actual} != {expected}\")\n values = {\n field: artifact[field].copy()\n for field in SequenceData.__dataclass_fields__\n }\n prepared = SequenceData(**values)\n log(\n f\"loaded full-sequence cache: tokens={len(prepared.sequence_items):,} \"\n f\"targets={prepared.target_count:,}\"\n )\n return prepared\n\n\ndef load_or_prepare(\n public: Path,\n stage: str,\n config: Config,\n cache: Path | None,\n) -> tuple[SequenceData, CandidateSet]:\n candidate_name = (\n \"validation_eligible_candidates.npz\"\n if stage == \"validation\"\n else \"test_eligible_candidates.npz\"\n )\n eligibility = CandidateSet.read(public / \"data\" / candidate_name)\n if cache is not None and cache.is_file():\n prepared = load_cache(cache, stage, config)\n if not np.array_equal(prepared.item_ids, eligibility.item_ids):\n raise RuntimeError(\"cached catalog differs from candidate artifact\")\n return prepared, eligibility\n prepared, built_eligibility = prepare_data(public, stage, config)\n if cache is not None:\n save_cache(cache, prepared, stage, config)\n return prepared, built_eligibility\n\n\ndef build_item_correction(\n prepared: SequenceData,\n config: Config,\n target_starts: np.ndarray,\n target_counts: np.ndarray,\n) -> torch.Tensor:\n \"\"\"Estimate in-batch propensities from the selected training targets.\"\"\"\n counts = np.zeros(len(prepared.item_ids), dtype=np.float64)\n for user_row, count in enumerate(target_counts):\n if not count:\n continue\n offset = int(prepared.offsets[user_row])\n target_start = int(target_starts[user_row])\n counts += np.bincount(\n prepared.sequence_items[\n offset\n + target_start\n + 1 : offset\n + target_start\n + int(count)\n + 1\n ].astype(np.int64),\n minlength=len(prepared.item_ids),\n )\n total = float(counts.sum())\n probabilities = (counts + 1.0) / (total + len(counts))\n raw = np.power(\n (1.0 / len(counts)) / probabilities,\n config.inbatch_correction_power,\n )\n normalization = float(np.sum(counts * raw) / max(total, 1.0))\n correction = np.clip(\n raw / max(normalization, 1.0e-12),\n config.inbatch_correction_min,\n config.inbatch_correction_max,\n ).astype(np.float32)\n mean_on_targets = float(np.sum(counts * correction) / max(total, 1.0))\n log(\n f\"inbatch correction: min={correction.min():.3f} \"\n f\"mean_on_targets={mean_on_targets:.3f} max={correction.max():.3f}\"\n )\n return torch.from_numpy(correction)\n\n\ndef build_known_positive_filter(\n prepared: SequenceData, training_counts: np.ndarray\n) -> np.ndarray:\n \"\"\"Pack each user's complete public-training positive-item set.\"\"\"\n width = (len(prepared.item_ids) + 7) // 8\n packed = np.zeros((len(prepared.user_ids), width), dtype=np.uint8)\n retained = 0\n for user_row, target_count in enumerate(training_counts):\n if not target_count:\n continue\n offset = int(prepared.offsets[user_row])\n # T adjacent targets consume sequence positions 0 through T. The\n # chronological BCE suffix starts at T + 1 and is intentionally absent.\n items = np.unique(\n prepared.sequence_items[\n offset : offset + int(target_count) + 1\n ].astype(np.int64)\n )\n byte_columns = items >> 3\n bit_values = np.left_shift(\n np.uint8(1), (items & 7).astype(np.uint8)\n )\n np.bitwise_or.at(packed[user_row], byte_columns, bit_values)\n retained += len(items)\n log(\n f\"full-history false-negative filter: users={len(prepared.user_ids):,} \"\n f\"unique_user_items={retained:,} bytes={packed.nbytes:,}\"\n )\n return packed\n\n\ndef invalid_full_history_negatives(\n negatives: np.ndarray,\n targets: np.ndarray,\n user_rows: np.ndarray,\n known_positive_packed: np.ndarray,\n liked_items: np.ndarray,\n) -> np.ndarray:\n \"\"\"Return row-level invalid negatives using the complete training prefix.\"\"\"\n byte_columns = negatives >> 3\n bit_columns = negatives & 7\n known = known_positive_packed[user_rows[:, None], byte_columns]\n invalid = ((known >> bit_columns) & 1).astype(bool)\n invalid |= negatives == targets[:, None]\n invalid |= (negatives[:, :, None] == liked_items[:, None, :]).any(axis=2)\n for column in range(1, negatives.shape[1]):\n invalid[:, column] |= (\n negatives[:, column, None] == negatives[:, :column]\n ).any(axis=1)\n return invalid\n\n\ndef draw_full_history_filtered_negatives(\n targets: torch.Tensor,\n user_rows: np.ndarray,\n liked_items: torch.Tensor,\n known_positive_packed: np.ndarray,\n n_items: int,\n config: Config,\n item_correction: torch.Tensor,\n generator: torch.Generator,\n device: torch.device,\n) -> tuple[torch.Tensor, torch.Tensor]:\n \"\"\"Draw mixed negatives and reject all known public-training positives.\"\"\"\n targets_cpu = targets.detach().cpu().numpy().astype(np.int64, copy=False)\n liked_cpu = liked_items.detach().cpu().numpy().astype(np.int64, copy=False)\n batch_size = len(targets_cpu)\n uniform = torch.randint(\n n_items,\n (batch_size, config.uniform_negatives),\n generator=generator,\n device=\"cpu\",\n )\n inbatch = torch.empty((batch_size, 0), dtype=torch.long)\n corrected_count = 0\n if config.inbatch_negatives:\n unique_targets = torch.unique(targets.detach().cpu())\n corrected_count = min(\n config.inbatch_negatives, max(len(unique_targets) - 1, 0)\n )\n if corrected_count:\n priorities = torch.rand(\n (batch_size, len(unique_targets)), generator=generator\n )\n priorities.masked_fill_(\n unique_targets[None, :].eq(\n torch.from_numpy(targets_cpu)[:, None]\n ),\n -1.0,\n )\n columns = torch.topk(\n priorities, k=corrected_count, dim=1\n ).indices\n inbatch = unique_targets[columns]\n if corrected_count < config.inbatch_negatives:\n filler = torch.randint(\n n_items,\n (batch_size, config.inbatch_negatives - corrected_count),\n generator=generator,\n device=\"cpu\",\n )\n inbatch = torch.cat([inbatch, filler], dim=1)\n\n negatives = torch.cat([uniform, inbatch], dim=1).numpy()\n correction = np.ones_like(negatives, dtype=np.float32)\n if corrected_count:\n correction[\n :, config.uniform_negatives : config.uniform_negatives + corrected_count\n ] = item_correction[inbatch[:, :corrected_count]].numpy()\n for _ in range(32):\n invalid = invalid_full_history_negatives(\n negatives,\n targets_cpu,\n user_rows,\n known_positive_packed,\n liked_cpu,\n )\n if not np.any(invalid):\n return (\n torch.from_numpy(negatives).to(device),\n torch.from_numpy(correction).to(device),\n )\n replacements = torch.randint(\n n_items,\n (int(invalid.sum()),),\n generator=generator,\n device=\"cpu\",\n ).numpy()\n negatives[invalid] = replacements\n # Rejected in-batch items become ordinary uniform draws.\n correction[invalid] = 1.0\n invalid = invalid_full_history_negatives(\n negatives,\n targets_cpu,\n user_rows,\n known_positive_packed,\n liked_cpu,\n )\n if np.any(invalid):\n raise RuntimeError(\"full-history negative resampling failed\")\n return (\n torch.from_numpy(negatives).to(device),\n torch.from_numpy(correction).to(device),\n )\n\n\ndef draw_training_negatives(\n positive_items: torch.Tensor,\n known_history: torch.Tensor,\n liked_items: torch.Tensor,\n user_rows: np.ndarray,\n known_positive_packed: np.ndarray | None,\n prepared: SequenceData,\n config: Config,\n item_correction: torch.Tensor,\n generator: torch.Generator,\n device: torch.device,\n) -> tuple[torch.Tensor, torch.Tensor]:\n if config.negative_filter_scope == \"full-training-history\":\n if known_positive_packed is None:\n raise RuntimeError(\"full-history negative filter was not built\")\n return draw_full_history_filtered_negatives(\n positive_items,\n user_rows,\n liked_items,\n known_positive_packed,\n len(prepared.item_ids),\n config,\n item_correction,\n generator,\n device,\n )\n return base.draw_filtered_mixed_negatives(\n positive_items,\n known_history,\n liked_items,\n len(prepared.item_ids),\n config.uniform_negatives,\n config.inbatch_negatives,\n item_correction,\n generator,\n device,\n )\n input_size=config.hidden_dim,\n hidden_size=dim,\n num_layers=1,\n batch_first=True,\n )\n self.user_tower = nn.Sequential(\n nn.Linear(dim * 3 + 6, config.user_hidden_1),\n nn.GELU(),\n nn.Dropout(config.dropout),\n nn.Linear(config.user_hidden_1, config.user_hidden_2),\n nn.GELU(),\n nn.Dropout(config.dropout),\n nn.Linear(config.user_hidden_2, dim),\n )\n self.candidate_embedding = nn.Embedding(n_items, dim)\n self.register_buffer(\n \"item_features\", torch.from_numpy(item_features), persistent=True\n )\n self.item_tower = nn.Sequential(\n nn.Linear(dim + n_item_features, config.item_hidden_1),\n nn.GELU(),\n nn.Dropout(config.dropout),\n nn.Linear(config.item_hidden_1, config.item_hidden_2),\n nn.GELU(),\n nn.Dropout(config.dropout),\n nn.Linear(config.item_hidden_2, dim),\n )\n nn.init.normal_(self.context_embedding.weight, std=0.02)\n nn.init.normal_(self.candidate_embedding.weight, std=0.02)\n with torch.no_grad():\n self.context_embedding.weight[n_items].zero_()\n\n def _mean_context_channel(self, items: torch.Tensor) -> torch.Tensor:\n mask = items.ne(self.n_items)\n vectors = self.context_embedding(items)\n return (vectors * mask.unsqueeze(-1)).sum(dim=1) / mask.sum(\n dim=1, keepdim=True\n ).clamp_min(1)\n\n def encode_items(self, items: torch.Tensor) -> torch.Tensor:\n inputs = torch.cat(\n [self.candidate_embedding(items), self.item_features[items]], dim=1\n )\n return self.item_tower(inputs)\n\n\ndef invalid_negative_mask(\n negatives: torch.Tensor,\n targets: torch.Tensor,\n context_items: torch.Tensor,\n liked_items: torch.Tensor,\n) -> torch.Tensor:\n \"\"\"Identify targets, known positives, active likes, and row duplicates.\"\"\"\n invalid = negatives.eq(targets[:, None])\n invalid |= negatives[:, :, None].eq(context_items[:, None, :]).any(dim=2)\n invalid |= negatives[:, :, None].eq(liked_items[:, None, :]).any(dim=2)\n for column in range(1, negatives.shape[1]):\n invalid[:, column] |= negatives[:, column, None].eq(\n negatives[:, :column]\n ).any(dim=1)\n return invalid\n\n\ndef draw_filtered_mixed_negatives(\n targets: torch.Tensor,\n context_items: torch.Tensor,\n liked_items: torch.Tensor,\n n_items: int,\n uniform_count: int,\n inbatch_count: int,\n item_correction: torch.Tensor,\n generator: torch.Generator,\n device: torch.device,\n) -> tuple[torch.Tensor, torch.Tensor]:\n \"\"\"Draw uniform and unique-target in-batch negatives.\"\"\"\n batch_size = len(targets)\n uniform = torch.randint(\n n_items,\n (batch_size, uniform_count),\n generator=generator,\n device=\"cpu\",\n )\n inbatch = torch.empty((batch_size, 0), dtype=torch.long, device=\"cpu\")\n corrected_count = 0\n if inbatch_count:\n unique_targets = torch.unique(targets.detach().cpu())\n corrected_count = min(inbatch_count, max(len(unique_targets) - 1, 0))\n if corrected_count:\n priorities = torch.rand(\n (batch_size, len(unique_targets)), generator=generator\n )\n priorities.masked_fill_(\n unique_targets[None, :].eq(targets.detach().cpu()[:, None]),\n -1.0,\n )\n columns = torch.topk(priorities, k=corrected_count, dim=1).indices\n inbatch = unique_targets[columns]\n if corrected_count < inbatch_count:\n filler = torch.randint(\n n_items,\n (batch_size, inbatch_count - corrected_count),\n generator=generator,\n device=\"cpu\",\n )\n inbatch = torch.cat([inbatch, filler], dim=1)\n\n negatives_cpu = torch.cat([uniform, inbatch], dim=1)\n correction_cpu = torch.ones_like(negatives_cpu, dtype=torch.float32)\n if corrected_count:\n correction_cpu[\n :, uniform_count : uniform_count + corrected_count\n ] = item_correction[inbatch[:, :corrected_count]]\n negatives = negatives_cpu.to(device)\n correction = correction_cpu.to(device)\n for _ in range(32):\n invalid = invalid_negative_mask(\n negatives, targets, context_items, liked_items\n )\n if not bool(invalid.any()):\n return negatives, correction\n replacements = torch.randint(\n n_items,\n (int(invalid.sum().detach().cpu()),),\n generator=generator,\n device=\"cpu\",\n ).to(device)\n negatives[invalid] = replacements\n correction[invalid] = 1.0\n invalid = invalid_negative_mask(\n negatives, targets, context_items, liked_items\n )\n if bool(invalid.any()):\n raise RuntimeError(\"mixed negative resampling failed false-negative filter\")\n return negatives, correction\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 3570 }, "formatted_output": "7:16 held-out validation targets; passing zero for either cap restores the\n12:The model is a pure two-tower retriever: no user-ID embedding, no output\n14:and 24 uniform plus eight propensity-corrected unique in-batch negatives. Every\n20:import argparse\n41:class Config:\n56: uniform_negatives: int = 24\n57: inbatch_negatives: int = 8\n61: negative_filter_scope: str = \"full-training-history\"\n67: validation_fraction: float = 0.10\n69: max_validation_targets_per_user: int = 16\n76:class CandidateSet:\n99:class SequenceData:\n127:class TargetPartition:\n132: validation_counts: np.ndarray\n137: return int(self.training_counts.sum() + self.validation_counts.sum())\n140:def log(message: str) -> None:\n144:def seed_everything(seed: int) -> None:\n150:def model_config(config: Config) -> base.TowerConfig:\n165: uniform_negatives=config.uniform_negatives,\n166: inbatch_negatives=config.inbatch_negatives,\n174:def append_state_snapshot(\n190:def causal_activity_features(\n247:def build_sequence_parts(\n314:def final_user_features(\n341:def prepare_data(\n342: public: Path, stage: str, config: Config\n354: if stage == \"validation\":\n357: candidate_name = \"validation_eligible_candidates.npz\"\n358: state_name = \"validation_preference_state.parquet\"\n463: f\"prepared stage={stage} sequence_users={len(prepared.user_ids):,} \"\n471:def cache_metadata(stage: str, config: Config) -> dict[str, object]:\n473: \"cache_version\": CACHE_VERSION,\n474: \"stage\": stage,\n477: \"event_time_feature\": \"exp-negative-gap-over-60-days\",\n481:def save_cache(\n482: path: Path, prepared: SequenceData, stage: str, config: Config\n487: metadata=np.asarray(json.dumps(cache_metadata(stage, config), sort_keys=True)),\n490: log(f\"saved public-derived full-sequence cache: {path}\")\n493:def load_cache(path: Path, stage: str, config: Config) -> SequenceData:\n496: expected = cache_metadata(stage, config)\n498: raise RuntimeError(f\"sequence cache differs: {actual} != {expected}\")\n505: f\"loaded full-sequence cache: tokens={len(prepared.sequence_items):,} \"\n511:def load_or_prepare(\n513: stage: str,\n515: cache: Path | None,\n518: \"validation_eligible_candidates.npz\"\n519: if stage == \"validation\"\n523: if cache is not None and cache.is_file():\n524: prepared = load_cache(cache, stage, config)\n526: raise RuntimeError(\"cached catalog differs from candidate artifact\")\n528: prepared, built_eligibility = prepare_data(public, stage, config)\n529: if cache is not None:\n530: save_cache(cache, prepared, stage, config)\n534:def build_item_correction(\n578:def build_known_positive_filter(\n603: f\"full-history false-negative filter: users={len(prepared.user_ids):,} \"\n609:def invalid_full_history_negatives(\n610: negatives: np.ndarray,\n616: \"\"\"Return row-level invalid negatives using the complete training prefix.\"\"\"\n617: byte_columns = negatives >> 3\n618: bit_columns = negatives & 7\n621: invalid |= negatives == targets[:, None]\n622: invalid |= (negatives[:, :, None] == liked_items[:, None, :]).any(axis=2)\n623: for column in range(1, negatives.shape[1]):\n625: negatives[:, column, None] == negatives[:, :column]\n630:def draw_full_history_filtered_negatives(\n641: \"\"\"Draw mixed negatives and reject all known public-training positives.\"\"\"\n647: (batch_size, config.uniform_negatives),\n653: if config.inbatch_negatives:\n656: config.inbatch_negatives, max(len(unique_targets) - 1, 0)\n672: if corrected_count < config.inbatch_negatives:\n675: (batch_size, config.inbatch_negatives - corrected_count),\n681: negatives = torch.cat([uniform, inbatch], dim=1).numpy()\n682: correction = np.ones_like(negatives, dtype=np.float32)\n685: :, config.uniform_negatives : config.uniform_negatives + corrected_count\n688: invalid = invalid_full_history_negatives(\n689: negatives,\n697: torch.from_numpy(negatives).to(device),\n706: negatives[invalid] = replacements\n709: invalid = invalid_full_history_negatives(\n710: negatives,\n717: raise RuntimeError(\"full-history negative resampling failed\")\n719: torch.from_numpy(negatives).to(device),\n724:def draw_training_negatives(\n736: if config.negative_filter_scope == \"full-training-history\":\n738: raise RuntimeError(\"full-history negative filter was not built\")\n739: return draw_full_history_filtered_negatives(\n750: return base.draw_filtered_mixed_negatives(\n755: config.uniform_negatives,\n756: config.inbatch_negatives,\n763:def event_sequence(\n774:def pool_explicit_states(\n798:def user_queries(\n813:def candidate_vectors(\n816: negatives: torch.Tensor,\n818: all_items = torch.cat([positives, negatives.reshape(-1)])\n822: negative_vectors = encoded[inverse[len(positives) :]].reshape(\n823: len(positives), negatives.shape[1], -1\n825: return positive_vectors, negative_vectors\n828:def history_windows(\n845:def corrected_negative_loss(\n851: total = config.uniform_negatives + config.inbatch_negatives\n853: if config.uniform_negatives:\n854: result += config.uniform_negatives * raw[\n855: :, : config.uniform_negatives\n857: if config.inbatch_negatives:\n858: selected = slice(config.uniform_negatives, total)\n860: result += config.inbatch_negatives * (\n867:def retained_target_window_starts(\n869: validation_counts: np.ndarray,\n873: training_region_ends = total_targets - validation_counts\n878: or np.any(validation_counts < 0)\n880: raise RuntimeError(\"chronological target partition produced negative counts\")\n882: window_starts + training_counts + validation_counts, total_targets\n888:def chronological_target_partition(\n890: validation_fraction: float,\n892: max_validation_targets_per_user: int,\n896: validation = np.floor(totals * validation_fraction).astype(np.int64)\n897: validation[(totals >= 2) & (validation == 0)] = 1\n898: if max_validation_targets_per_user:\n899: validation = np.minimum(validation, max_validation_targets_per_user)\n901: history_training = totals - validation\n905: window_starts = retained_target_window_starts(totals, validation, training)\n909: validation_counts=validation,\n914:def load_organic_like_outcomes(\n915: public: Path, stage: str, item_ids: np.ndarray\n918: prefixes = (\"train\",) if stage == \"validation\" else (\"train\", \"val\")\n955:def build_target_weights(\n957: stage: str,\n978: public, stage, prepared.item_ids\n981: base.TRAIN_END if stage == \"validation\" else base.VALIDATION_END\n991: first_validation_target = last_training_target + 1\n993: int(prepared.sequence_timestamps[first_validation_target])\n994: if first_validation_target < end\n1035:def build_dense_block(\n1111:def score_target_microbatches(\n1119: negatives: torch.Tensor,\n1120: negative_weights: torch.Tensor,\n1134: \"negative_sum\": 0.0,\n1162: positive_vectors, negative_vectors = candidate_vectors(\n1165: negatives[begin:end],\n1168: negative_logits = torch.einsum(\n1169: \"bd,bkd->bk\", query_vectors, negative_vectors\n1172: negative_loss = corrected_negative_loss(\n1173: negative_logits, negative_weights[begin:end], config\n1179: selected_weights * (positive_loss + negative_loss) / 2.0\n1190: statistics[\"negative_sum\"] += float(negative_logits.detach().sum().cpu())\n1192: (positive_logits.detach()[:, None] > negative_logits.detach()).sum().cpu()\n1198:def release_accelerator_cache(device: torch.device) -> None:\n1200: torch.mps.empty_cache()\n1203:def run_training_epoch(\n1227: negative_count = config.uniform_negatives + config.inbatch_negatives\n1232: total_negative = 0.0\n1257: sequence_outputs, _ = event_sequence(\n1265: sequence_outputs[row, count - 1]\n1275: sequence_vectors = sequence_outputs.reshape(\n1293: negatives, negative_weights = draw_training_negatives(\n1314: negatives,\n1315: negative_weights,\n1331: total_negative += statistics[\"negative_sum\"]\n1335: release_accelerator_cache(device)\n1353: \"negative_logit\": total_negative / max(total_targets * negative_count, 1),\n1355: / max(total_targets * negative_count, 1),\n1364:def evaluate_validation_bce(\n1371: validation_counts: np.ndarray,\n1378: totals = training_counts + validation_counts\n1379: users_with_validation = np.flatnonzero(validation_counts > 0)\n1380: ordered = users_with_validation[\n1381: np.argsort(totals[users_with_validation], kind=\"stable\")\n1413: sequence_outputs, _ = event_sequence(\n1421: sequence_outputs[row, count - 1]\n1431: sequence_vectors = sequence_outputs.reshape(\n1449: negatives, negative_weights = draw_training_negatives(\n1469: negatives,\n1470: negative_weights,\n1479: release_accelerator_cache(device)\n1481: \"validation_loss\": total_loss / max(total_weight, 1.0e-12),\n1482: \"validation_targets\": float(total_targets),\n1483: \"validation_target_weight_sum\": total_weight,\n1484: \"validation_seconds\": time.monotonic() - started,\n1488:def snapshot_model(model: nn.Module) -> dict[str, torch.Tensor]:\n1495:def save_epoch_checkpoint(\n1521:def train_model(\n1523: stage: str,\n1532: config.validation_fraction,\n1534: config.max_validation_targets_per_user,\n1537: validation_counts = partition.validation_counts\n1540: f\"validation={int(validation_counts.sum()):,} \"\n1560: stage,\n1567: if config.negative_filter_scope == \"full-training-history\"\n1601: \"negative_filter_scope\", \"last-100\"\n1619: stored_config.get(\"validation_fraction\", 0.10),\n1621: stored_config.get(\"max_validation_targets_per_user\", 0),\n1624: config.validation_fraction,\n1626: config.max_validation_targets_per_user,\n1629: stored_filter_scope != config.negative_filter_scope\n1657: prior.pop(\"validation_loss\", None)\n1660: f\"{stored_filter_scope}->{config.negative_filter_scope}, \"\n1664: \"recomputing the resume checkpoint's validation BCE\"\n1666: if \"validation_loss\" not in prior:\n1668: evaluate_validation_bce(\n1675: validation_counts,\n1682: best_loss = float(prior[\"validation_loss\"])\n1686: f\"resumed validation-BCE training: epoch={start_epoch} \"\n1705: validation = evaluate_validation_bce(\n1712: validation_counts,\n1717: diagnostics.update(validation)\n1731: diagnostics[\"validation_loss\"]\n1735: best_loss = diagnostics[\"validation_loss\"]\n1755: f\"validation_loss={diagnostics['validation_loss']:.5f} \"\n1758: f\"validation_targets={int(diagnostics['validation_targets']):,} \"\n1760: f\"validation_seconds={diagnostics['validation_seconds']:.1f}\"\n1762: release_accelerator_cache(device)\n1766: f\"best_epoch={best_epoch} best_validation_loss={best_loss:.6f}\"\n1775: f\"restored best validation-BCE checkpoint: epoch={best_epoch} \"\n1782:def inference_queries(\n1797: output_rows = ordered[group_start : group_start + config.user_batch_size]\n1798: rows = sequence_rows[output_rows]\n1799: group_lengths = lengths[output_rows]\n1801: 1, len(output_rows), config.embedding_dim, device=device\n1809: (len(output_rows), steps),\n1813: features = np.zeros((len(output_rows), steps, 3), dtype=np.float16)\n1814: counts = np.zeros(len(output_rows), dtype=np.int32)\n1826: outputs, _ = event_sequence(\n1835: outputs[local_row, count - 1]\n1842: prepared.inference_liked_items[output_rows].astype(np.int64)\n1845: prepared.inference_disliked_items[output_rows].astype(np.int64)\n1853: prepared.inference_user_features[output_rows].astype(np.float32)\n1856: result[torch.from_numpy(output_rows.astype(np.int64))] = queries.cpu()\n1861:def rank_candidates(\n1912:def write_ranking(frame: pd.DataFrame, output: Path) -> None:\n1913: output.parent.mkdir(parents=True, exist_ok=True)\n1914: if output.suffix == \".parquet\":\n1915: frame.to_parquet(output, index=False, compression=\"zstd\")\n1916: elif output.suffix == \".csv\":\n1917: frame.to_csv(output, index=False)\n1919: raise ValueError(\"--output must end in .parquet or .csv\")\n1922:def main() -> int:\n1923: parser = argparse.ArgumentParser(description=__doc__)\n1925: \"--stage\", choices=(\"validation\", \"submission\"), default=\"validation\"\n1927: parser.add_argument(\"--output\", type=Path, required=True)\n1929: parser.add_argument(\"--prepared-cache\", type=Path)\n1944: \"--negative-filter-scope\",\n1946: default=Config.negative_filter_scope,\n1974: \"--validation-fraction\",\n1976: default=Config.validation_fraction,\n1982: help=\"retain this many recent pre-validation targets per user; 0 is uncapped\",\n1985: \"--max-validation-targets-per-user\",\n1987: default=Config.max_validation_targets_per_user,\n1988: help=\"retain this many final validation targets per user; 0 is uncapped\",\n2020: or args.max_validation_targets_per_user < 0\n2023: if not 0.0 < args.validation_fraction < 1.0:\n2024: parser.error(\"validation fraction must be strictly between zero and one\")\n2034: negative_filter_scope=args.negative_filter_scope,\n2040: validation_fraction=args.validation_fraction,\n2042: max_validation_targets_per_user=args.max_validation_targets_per_user,\n2057: args.stage,\n2059: args.prepared_cache.resolve() if args.prepared_cache else None,\n2063: config.validation_fraction,\n2065: config.max_validation_targets_per_user,\n if not len(items):\n return None\n\n event_features = np.zeros((len(items), 3), dtype=np.float16)\n event_features[:, 0] = organic.astype(np.float16)\n event_features[:, 1] = (\n np.minimum(completion, 100.0) / 100.0\n ).astype(np.float16)\n if len(items) > 1:\n gaps = np.maximum(0, timestamps[1:] - timestamps[:-1])\n event_features[1:, 2] = np.exp(\n -gaps.astype(np.float64) / (60.0 * base.DAY)\n ).astype(np.float16)\n\n activity = causal_activity_features(\n timestamps, items, organic, completion\n )\n states = np.zeros(len(items), dtype=np.uint32)\n active_likes: dict[int, int] = {}\n active_dislikes: dict[int, int] = {}\n explicit_position = 0\n current_state = 0\n for row, timestamp in enumerate(timestamps):\n changed = False\n while (\n explicit_position < len(explicit.timestamps)\n and int(explicit.timestamps[explicit_position]) < int(timestamp)\n ):\n item = int(explicit.items[explicit_position])\n event_timestamp = int(explicit.timestamps[explicit_position])\n destination = (\n active_likes\n if int(explicit.channel[explicit_position]) == 0\n else active_dislikes\n )\n if int(explicit.active[explicit_position]) == 1:\n destination[item] = event_timestamp\n else:\n destination.pop(item, None)\n explicit_position += 1\n changed = True\n if changed:\n current_state = append_state_snapshot(\n active_likes,\n active_dislikes,\n n_items,\n liked_states,\n disliked_states,\n state_counts,\n )\n states[row] = np.uint32(current_state)\n return timestamps, items.copy(), event_features, activity, states\n\n\ndef final_user_features(\n timestamps: np.ndarray,\n items: np.ndarray,\n event_features: np.ndarray,\n history_end: int,\n like_count: int,\n dislike_count: int,\n) -> np.ndarray:\n start = int(\n np.searchsorted(\n timestamps,\n history_end - base.PROFILE_LOOKBACK_DAYS * base.DAY,\n side=\"left\",\n )\n )\n result = np.zeros(6, dtype=np.float32)\n recent_items = items[start:]\n if len(recent_items):\n result[0] = np.log1p(len(recent_items)) / 8.0\n result[1] = np.log1p(len(np.unique(recent_items))) / 6.0\n result[2] = float(np.mean(event_features[start:, 0]))\n result[3] = float(np.mean(event_features[start:, 1]))\n result[4] = np.log1p(like_count) / 6.0\n result[5] = np.log1p(dislike_count) / 4.0\n return result\n\n\ndef prepare_data(\n public: Path, stage: str, config: Config\n) -> tuple[SequenceData, CandidateSet]:\n data = public / \"data\"\n catalog = pd.read_parquet(data / \"candidate_catalog.parquet\").sort_values(\n \"popularity_rank\"\n )\n item_ids = catalog.item_id.to_numpy(dtype=np.int64, copy=True)\n item_index = pd.Index(item_ids)\n n_items = len(item_ids)\n if n_items >= np.iinfo(np.uint16).max:\n raise RuntimeError(\"uint16 item storage is too small for this catalog\")\n\n if stage == \"validation\":\n prefixes = (\"train\",)\n history_end = base.TRAIN_END\n candidate_name = \"validation_eligible_candidates.npz\"\n state_name = \"validation_preference_state.parquet\"\n else:\n prefixes = (\"train\", \"val\")\n history_end = base.VALIDATION_END\n candidate_name = \"test_eligible_candidates.npz\"\n state_name = \"test_preference_state.parquet\"\n eligibility = CandidateSet.read(data / candidate_name)\n if not np.array_equal(eligibility.item_ids, item_ids):\n raise RuntimeError(\"catalog and candidate artifact item order differ\")\n\n explicit_by_user = base.load_explicit_events(data, prefixes, item_index)\n inference_likes, inference_dislikes, like_counts, dislike_counts = (\n base.load_inference_state(\n data / state_name,\n item_index,\n eligibility.uids,\n n_items,\n )\n )\n inference_rows = {int(uid): row for row, uid in enumerate(eligibility.uids)}\n inference_user_features = np.zeros((len(eligibility.uids), 6), dtype=np.float32)\n inference_sequence_rows = np.full(len(eligibility.uids), -1, dtype=np.int32)\n\n # Snapshot zero is the shared empty explicit state.\n liked_states = [np.full(base.LIKE_ITEMS, n_items, dtype=np.uint16)]\n disliked_states = [\n np.full(base.DISLIKE_ITEMS, n_items, dtype=np.uint16)\n ]\n state_counts: list[tuple[int, int]] = [(0, 0)]\n user_ids: list[int] = []\n offsets = [0]\n timestamp_parts: list[np.ndarray] = []\n item_parts: list[np.ndarray] = []\n event_parts: list[np.ndarray] = []\n activity_parts: list[np.ndarray] = []\n state_parts: list[np.ndarray] = []\n\n listen_paths = [data / f\"{prefix}_listens.parquet\" for prefix in prefixes]\n started = time.monotonic()\n for seen_users, (uid, listens) in enumerate(\n base.merge_listen_streams(listen_paths, item_index), start=1\n ):\n parts = build_sequence_parts(\n listens,\n explicit_by_user.get(uid, base.empty_explicit_events()),\n n_items,\n liked_states,\n disliked_states,\n state_counts,\n )\n if parts is None:\n continue\n sequence_row = len(user_ids)\n user_ids.append(uid)\n timestamp_parts.append(parts[0].astype(np.uint32, copy=False))\n item_parts.append(parts[1])\n event_parts.append(parts[2])\n activity_parts.append(parts[3])\n state_parts.append(parts[4])\n offsets.append(offsets[-1] + len(parts[1]))\n inference_row = inference_rows.get(uid)\n if inference_row is not None:\n inference_sequence_rows[inference_row] = sequence_row\n inference_user_features[inference_row] = final_user_features(\n parts[0],\n parts[1],\n parts[2],\n history_end,\n int(like_counts[inference_row]),\n int(dislike_counts[inference_row]),\n )\n if seen_users % 1_000 == 0:\n log(\n f\"prepared users={seen_users:,} tokens={offsets[-1]:,} \"\n f\"states={len(liked_states):,} \"\n f\"elapsed={time.monotonic() - started:.1f}s\"\n )\n\n missing = np.flatnonzero(inference_sequence_rows < 0)\n if len(missing):\n inference_user_features[missing, 4] = np.log1p(like_counts[missing]) / 6.0\n inference_user_features[missing, 5] = (\n np.log1p(dislike_counts[missing]) / 4.0\n )\n log(f\"target users without qualifying catalog sequence={len(missing):,}\")\n\n prepared = SequenceData(\n item_ids=item_ids,\n item_features=base.build_item_features(catalog),\n user_ids=np.asarray(user_ids, dtype=np.int64),\n offsets=np.asarray(offsets, dtype=np.int64),\n sequence_timestamps=np.concatenate(timestamp_parts),\n sequence_items=np.concatenate(item_parts),\n event_features=np.concatenate(event_parts),\n activity_features=np.concatenate(activity_parts),\n state_ids=np.concatenate(state_parts),\n liked_states=np.stack(liked_states),\n disliked_states=np.stack(disliked_states),\n state_counts=np.asarray(state_counts, dtype=np.int32),\n inference_user_rows=inference_sequence_rows,\n inference_liked_items=inference_likes,\n inference_disliked_items=inference_dislikes,\n inference_user_features=inference_user_features,\n )\n log(\n f\"prepared stage={stage} sequence_users={len(prepared.user_ids):,} \"\n f\"tokens={len(prepared.sequence_items):,} \"\n f\"next_item_targets={prepared.target_count:,} \"\n f\"states={len(prepared.liked_states):,}\"\n )\n return prepared, eligibility\n\n\ndef cache_metadata(stage: str, config: Config) -> dict[str, object]:\n return {\n \"cache_version\": CACHE_VERSION,\n \"stage\": stage,\n \"positive_listen_threshold\": base.LISTEN_POSITIVE_THRESHOLD,\n \"profile_lookback_days\": base.PROFILE_LOOKBACK_DAYS,\n \"event_time_feature\": \"exp-negative-gap-over-60-days\",\n }\n\n\ndef save_cache(\n path: Path, prepared: SequenceData, stage: str, config: Config\n) -> None:\n path.parent.mkdir(parents=True, exist_ok=True)\n np.savez(\n path,\n metadata=np.asarray(json.dumps(cache_metadata(stage, config), sort_keys=True)),\n **prepared.__dict__,\n )\n log(f\"saved public-derived full-sequence cache: {path}\")\n\n\ndef load_cache(path: Path, stage: str, config: Config) -> SequenceData:\n with np.load(path, allow_pickle=False) as artifact:\n actual = json.loads(str(artifact[\"metadata\"].item()))\n expected = cache_metadata(stage, config)\n if actual != expected:\n raise RuntimeError(f\"sequence cache differs: {actual} != {expected}\")\n values = {\n field: artifact[field].copy()\n for field in SequenceData.__dataclass_fields__\n }\n prepared = SequenceData(**values)\n log(\n f\"loaded full-sequence cache: tokens={len(prepared.sequence_items):,} \"\n f\"targets={prepared.target_count:,}\"\n )\n return prepared\n\n\ndef load_or_prepare(\n public: Path,\n stage: str,\n config: Config,\n cache: Path | None,\n) -> tuple[SequenceData, CandidateSet]:\n candidate_name = (\n \"validation_eligible_candidates.npz\"\n if stage == \"validation\"\n else \"test_eligible_candidates.npz\"\n )\n eligibility = CandidateSet.read(public / \"data\" / candidate_name)\n if cache is not None and cache.is_file():\n prepared = load_cache(cache, stage, config)\n if not np.array_equal(prepared.item_ids, eligibility.item_ids):\n raise RuntimeError(\"cached catalog differs from candidate artifact\")\n return prepared, eligibility\n prepared, built_eligibility = prepare_data(public, stage, config)\n if cache is not None:\n save_cache(cache, prepared, stage, config)\n return prepared, built_eligibility\n\n\ndef build_item_correction(\n prepared: SequenceData,\n config: Config,\n target_starts: np.ndarray,\n target_counts: np.ndarray,\n) -> torch.Tensor:\n \"\"\"Estimate in-batch propensities from the selected training targets.\"\"\"\n counts = np.zeros(len(prepared.item_ids), dtype=np.float64)\n for user_row, count in enumerate(target_counts):\n if not count:\n continue\n offset = int(prepared.offsets[user_row])\n target_start = int(target_starts[user_row])\n counts += np.bincount(\n prepared.sequence_items[\n offset\n + target_start\n + 1 : offset\n + target_start\n + int(count)\n + 1\n ].astype(np.int64),\n minlength=len(prepared.item_ids),\n )\n total = float(counts.sum())\n probabilities = (counts + 1.0) / (total + len(counts))\n raw = np.power(\n (1.0 / len(counts)) / probabilities,\n config.inbatch_correction_power,\n )\n normalization = float(np.sum(counts * raw) / max(total, 1.0))\n correction = np.clip(\n raw / max(normalization, 1.0e-12),\n config.inbatch_correction_min,\n config.inbatch_correction_max,\n ).astype(np.float32)\n mean_on_targets = float(np.sum(counts * correction) / max(total, 1.0))\n log(\n f\"inbatch correction: min={correction.min():.3f} \"\n f\"mean_on_targets={mean_on_targets:.3f} max={correction.max():.3f}\"\n )\n return torch.from_numpy(correction)\n\n\ndef build_known_positive_filter(\n prepared: SequenceData, training_counts: np.ndarray\n) -> np.ndarray:\n \"\"\"Pack each user's complete public-training positive-item set.\"\"\"\n width = (len(prepared.item_ids) + 7) // 8\n packed = np.zeros((len(prepared.user_ids), width), dtype=np.uint8)\n retained = 0\n for user_row, target_count in enumerate(training_counts):\n if not target_count:\n continue\n offset = int(prepared.offsets[user_row])\n # T adjacent targets consume sequence positions 0 through T. The\n # chronological BCE suffix starts at T + 1 and is intentionally absent.\n items = np.unique(\n prepared.sequence_items[\n offset : offset + int(target_count) + 1\n ].astype(np.int64)\n )\n byte_columns = items >> 3\n bit_values = np.left_shift(\n np.uint8(1), (items & 7).astype(np.uint8)\n )\n np.bitwise_or.at(packed[user_row], byte_columns, bit_values)\n retained += len(items)\n log(\n f\"full-history false-negative filter: users={len(prepared.user_ids):,} \"\n f\"unique_user_items={retained:,} bytes={packed.nbytes:,}\"\n )\n return packed\n\n\ndef invalid_full_history_negatives(\n negatives: np.ndarray,\n targets: np.ndarray,\n user_rows: np.ndarray,\n known_positive_packed: np.ndarray,\n liked_items: np.ndarray,\n) -> np.ndarray:\n \"\"\"Return row-level invalid negatives using the complete training prefix.\"\"\"\n byte_columns = negatives >> 3\n bit_columns = negatives & 7\n known = known_positive_packed[user_rows[:, None], byte_columns]\n invalid = ((known >> bit_columns) & 1).astype(bool)\n invalid |= negatives == targets[:, None]\n invalid |= (negatives[:, :, None] == liked_items[:, None, :]).any(axis=2)\n for column in range(1, negatives.shape[1]):\n invalid[:, column] |= (\n negatives[:, column, None] == negatives[:, :column]\n ).any(axis=1)\n return invalid\n\n\ndef draw_full_history_filtered_negatives(\n targets: torch.Tensor,\n user_rows: np.ndarray,\n liked_items: torch.Tensor,\n known_positive_packed: np.ndarray,\n n_items: int,\n config: Config,\n item_correction: torch.Tensor,\n generator: torch.Generator,\n device: torch.device,\n) -> tuple[torch.Tensor, torch.Tensor]:\n \"\"\"Draw mixed negatives and reject all known public-training positives.\"\"\"\n targets_cpu = targets.detach().cpu().numpy().astype(np.int64, copy=False)\n liked_cpu = liked_items.detach().cpu().numpy().astype(np.int64, copy=False)\n batch_size = len(targets_cpu)\n uniform = torch.randint(\n n_items,\n (batch_size, config.uniform_negatives),\n generator=generator,\n device=\"cpu\",\n )\n inbatch = torch.empty((batch_size, 0), dtype=torch.long)\n corrected_count = 0\n if config.inbatch_negatives:\n unique_targets = torch.unique(targets.detach().cpu())\n corrected_count = min(\n config.inbatch_negatives, max(len(unique_targets) - 1, 0)\n )\n if corrected_count:\n priorities = torch.rand(\n (batch_size, len(unique_targets)), generator=generator\n )\n priorities.masked_fill_(\n unique_targets[None, :].eq(\n torch.from_numpy(targets_cpu)[:, None]\n ),\n -1.0,\n )\n columns = torch.topk(\n priorities, k=corrected_count, dim=1\n ).indices\n inbatch = unique_targets[columns]\n if corrected_count < config.inbatch_negatives:\n filler = torch.randint(\n n_items,\n (batch_size, config.inbatch_negatives - corrected_count),\n generator=generator,\n device=\"cpu\",\n )\n inbatch = torch.cat([inbatch, filler], dim=1)\n\n negatives = torch.cat([uniform, inbatch], dim=1).numpy()\n correction = np.ones_like(negatives, dtype=np.float32)\n if corrected_count:\n correction[\n :, config.uniform_negatives : config.uniform_negatives + corrected_count\n ] = item_correction[inbatch[:, :corrected_count]].numpy()\n for _ in range(32):\n invalid = invalid_full_history_negatives(\n negatives,\n targets_cpu,\n user_rows,\n known_positive_packed,\n liked_cpu,\n )\n if not np.any(invalid):\n return (\n torch.from_numpy(negatives).to(device),\n torch.from_numpy(correction).to(device),\n )\n replacements = torch.randint(\n n_items,\n (int(invalid.sum()),),\n generator=generator,\n device=\"cpu\",\n ).numpy()\n negatives[invalid] = replacements\n # Rejected in-batch items become ordinary uniform draws.\n correction[invalid] = 1.0\n invalid = invalid_full_history_negatives(\n negatives,\n targets_cpu,\n user_rows,\n known_positive_packed,\n liked_cpu,\n )\n if np.any(invalid):\n raise RuntimeError(\"full-history negative resampling failed\")\n return (\n torch.from_numpy(negatives).to(device),\n torch.from_numpy(correction).to(device),\n )\n\n\ndef draw_training_negatives(\n positive_items: torch.Tensor,\n known_history: torch.Tensor,\n liked_items: torch.Tensor,\n user_rows: np.ndarray,\n known_positive_packed: np.ndarray | None,\n prepared: SequenceData,\n config: Config,\n item_correction: torch.Tensor,\n generator: torch.Generator,\n device: torch.device,\n) -> tuple[torch.Tensor, torch.Tensor]:\n if config.negative_filter_scope == \"full-training-history\":\n if known_positive_packed is None:\n raise RuntimeError(\"full-history negative filter was not built\")\n return draw_full_history_filtered_negatives(\n positive_items,\n user_rows,\n liked_items,\n known_positive_packed,\n len(prepared.item_ids),\n config,\n item_correction,\n generator,\n device,\n )\n return base.draw_filtered_mixed_negatives(\n positive_items,\n known_history,\n liked_items,\n len(prepared.item_ids),\n config.uniform_negatives,\n config.inbatch_negatives,\n item_correction,\n generator,\n device,\n )\n input_size=config.hidden_dim,\n hidden_size=dim,\n num_layers=1,\n batch_first=True,\n )\n self.user_tower = nn.Sequential(\n nn.Linear(dim * 3 + 6, config.user_hidden_1),\n nn.GELU(),\n nn.Dropout(config.dropout),\n nn.Linear(config.user_hidden_1, config.user_hidden_2),\n nn.GELU(),\n nn.Dropout(config.dropout),\n nn.Linear(config.user_hidden_2, dim),\n )\n self.candidate_embedding = nn.Embedding(n_items, dim)\n self.register_buffer(\n \"item_features\", torch.from_numpy(item_features), persistent=True\n )\n self.item_tower = nn.Sequential(\n nn.Linear(dim + n_item_features, config.item_hidden_1),\n nn.GELU(),\n nn.Dropout(config.dropout),\n nn.Linear(config.item_hidden_1, config.item_hidden_2),\n nn.GELU(),\n nn.Dropout(config.dropout),\n nn.Linear(config.item_hidden_2, dim),\n )\n nn.init.normal_(self.context_embedding.weight, std=0.02)\n nn.init.normal_(self.candidate_embedding.weight, std=0.02)\n with torch.no_grad():\n self.context_embedding.weight[n_items].zero_()\n\n def _mean_context_channel(self, items: torch.Tensor) -> torch.Tensor:\n mask = items.ne(self.n_items)\n vectors = self.context_embedding(items)\n return (vectors * mask.unsqueeze(-1)).sum(dim=1) / mask.sum(\n dim=1, keepdim=True\n ).clamp_min(1)\n\n def encode_items(self, items: torch.Tensor) -> torch.Tensor:\n inputs = torch.cat(\n [self.candidate_embedding(items), self.item_features[items]], dim=1\n )\n return self.item_tower(inputs)\n\n\ndef invalid_negative_mask(\n negatives: torch.Tensor,\n targets: torch.Tensor,\n context_items: torch.Tensor,\n liked_items: torch.Tensor,\n) -> torch.Tensor:\n \"\"\"Identify targets, known positives, active likes, and row duplicates.\"\"\"\n invalid = negatives.eq(targets[:, None])\n invalid |= negatives[:, :, None].eq(context_items[:, None, :]).any(dim=2)\n invalid |= negatives[:, :, None].eq(liked_items[:, None, :]).any(dim=2)\n for column in range(1, negatives.shape[1]):\n invalid[:, column] |= negatives[:, column, None].eq(\n negatives[:, :column]\n ).any(dim=1)\n return invalid\n\n\ndef draw_filtered_mixed_negatives(\n targets: torch.Tensor,\n context_items: torch.Tensor,\n liked_items: torch.Tensor,\n n_items: int,\n uniform_count: int,\n inbatch_count: int,\n item_correction: torch.Tensor,\n generator: torch.Generator,\n device: torch.device,\n) -> tuple[torch.Tensor, torch.Tensor]:\n \"\"\"Draw uniform and unique-target in-batch negatives.\"\"\"\n batch_size = len(targets)\n uniform = torch.randint(\n n_items,\n (batch_size, uniform_count),\n generator=generator,\n device=\"cpu\",\n )\n inbatch = torch.empty((batch_size, 0), dtype=torch.long, device=\"cpu\")\n corrected_count = 0\n if inbatch_count:\n unique_targets = torch.unique(targets.detach().cpu())\n corrected_count = min(inbatch_count, max(len(unique_targets) - 1, 0))\n if corrected_count:\n priorities = torch.rand(\n (batch_size, len(unique_targets)), generator=generator\n )\n priorities.masked_fill_(\n unique_targets[None, :].eq(targets.detach().cpu()[:, None]),\n -1.0,\n )\n columns = torch.topk(priorities, k=corrected_count, dim=1).indices\n inbatch = unique_targets[columns]\n if corrected_count < inbatch_count:\n filler = torch.randint(\n n_items,\n (batch_size, inbatch_count - corrected_count),\n generator=generator,\n device=\"cpu\",\n )\n inbatch = torch.cat([inbatch, filler], dim=1)\n\n negatives_cpu = torch.cat([uniform, inbatch], dim=1)\n correction_cpu = torch.ones_like(negatives_cpu, dtype=torch.float32)\n if corrected_count:\n correction_cpu[\n :, uniform_count : uniform_count + corrected_count\n ] = item_correction[inbatch[:, :corrected_count]]\n negatives = negatives_cpu.to(device)\n correction = correction_cpu.to(device)\n for _ in range(32):\n invalid = invalid_negative_mask(\n negatives, targets, context_items, liked_items\n )\n if not bool(invalid.any()):\n return negatives, correction\n replacements = torch.randint(\n n_items,\n (int(invalid.sum().detach().cpu()),),\n generator=generator,\n device=\"cpu\",\n ).to(device)\n negatives[invalid] = replacements\n correction[invalid] = 1.0\n invalid = invalid_negative_mask(\n negatives, targets, context_items, liked_items\n )\n if bool(invalid.any()):\n raise RuntimeError(\"mixed negative resampling failed false-negative filter\")\n return negatives, correction\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "session_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "root_turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "response_id": "resp_08539f19231b9e9f016ab5a8e230a887d2835268c3e560c6ac", "usage": { "input_tokens": 20381, "cached_input_tokens": 14720, "cache_write_input_tokens": 0, "output_tokens": 271, "reasoning_output_tokens": 105, "total_tokens": 20652 }, "turn_token_usage": { "input_tokens": 35281, "cached_input_tokens": 25856, "cache_write_input_tokens": 0, "output_tokens": 559, "reasoning_output_tokens": 189, "total_tokens": 35840 }, "thread_token_usage": { "input_tokens": 35281, "cached_input_tokens": 25856, "cache_write_input_tokens": 0, "output_tokens": 559, "reasoning_output_tokens": 189, "total_tokens": 35840 } } ## Step 9 · agent · 2026-09-24T22:49:13.431Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T22:49:15.147Z { "code": "const r = await tools.exec_command({\"cmd\":\"sed -n '760,1225p' /app/solution/train_current_model.py\\nsed -n '1225,1790p' /app/solution/train_current_model.py\\nsed -n '1790,2095p' /app/solution/train_current_model.py\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":60000}); text(r.output);" } ### Tool output · 2026-09-24T22:49:15.202Z Script completed Wall time 0.0 seconds Output: Warning: truncated output (original token count: 12627) Total output lines: 1338 ) def event_sequence( model: base.SequentialTwoTower, items: torch.Tensor, features: torch.Tensor, hidden: torch.Tensor, ) -> tuple[torch.Tensor, torch.Tensor]: inputs = torch.cat([model.context_embedding(items), features], dim=-1) projected = model.event_projection(inputs) return model.sequence_encoder(projected, hidden) def pool_explicit_states( model: base.SequentialTwoTower, prepared: SequenceData, state_ids: np.ndarray, device: torch.device, ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: unique_states, inverse = np.unique(state_ids, return_inverse=True) liked_items = torch.from_numpy( prepared.liked_states[unique_states].astype(np.int64) ).to(device) disliked_items = torch.from_numpy( prepared.disliked_states[unique_states].astype(np.int64) ).to(device) liked_vectors = model._mean_context_channel(liked_items) disliked_vectors = model._mean_context_channel(disliked_items) inverse_rows = torch.from_numpy(inverse.astype(np.int64)).to(device) counts = prepared.state_counts[state_ids] return ( liked_vectors[inverse_rows], disliked_vectors[inverse_rows], torch.from_numpy(counts.astype(np.float32)).to(device), ) def user_queries( model: base.SequentialTwoTower, sequence_vectors: torch.Tensor, liked_vectors: torch.Tensor, disliked_vectors: torch.Tensor, user_features: torch.Tensor, ) -> torch.Tensor: return model.user_tower( torch.cat( [sequence_vectors, liked_vectors, disliked_vectors, user_features], dim=1, ) ) def candidate_vectors( model: base.SequentialTwoTower, positives: torch.Tensor, negatives: torch.Tensor, ) -> tuple[torch.Tensor, torch.Tensor]: all_items = torch.cat([positives, negatives.reshape(-1)]) unique_items, inverse = torch.unique(all_items, return_inverse=True) encoded = model.encode_items(unique_items) positive_vectors = encoded[inverse[: len(positives)]] negative_vectors = encoded[inverse[len(positives) :]].reshape( len(positives), negatives.shape[1], -1 ) return positive_vectors, negative_vectors def history_windows( sequence: np.ndarray, start: int, count: int, width: int, padding_item: int, ) -> np.ndarray: base_start = max(0, start - width + 1) segment = sequence[base_start : start + count] padded = np.concatenate( [np.full(width - 1, padding_item, dtype=np.uint16), segment] ) windows = np.lib.stride_tricks.sliding_window_view(padded, width) offset = start - base_start return windows[offset : offset + count].copy() def corrected_negative_loss( logits: torch.Tensor, correction: torch.Tensor, config: Config, ) -> torch.Tensor: raw = F.softplus(logits) total = config.uniform_negatives + config.inbatch_negatives result = torch.zeros(len(logits), device=logits.device) if config.uniform_negatives: result += config.uniform_negatives * raw[ :, : config.uniform_negatives ].mean(dim=1) if config.inbatch_negatives: selected = slice(config.uniform_negatives, total) weights = correction[:, selected] result += config.inbatch_negatives * ( (raw[:, selected] * weights).sum(dim=1) / weights.sum(dim=1).clamp_min(1.0e-6) ) return result / total def retained_target_window_starts( total_targets: np.ndarray, validation_counts: np.ndarray, training_counts: np.ndarray, ) -> np.ndarray: """Return the discarded prefix before the retained chronological suffix.""" training_region_ends = total_targets - validation_counts window_starts = training_region_ends - training_counts if ( np.any(window_starts < 0) or np.any(training_counts < 0) or np.any(validation_counts < 0) ): raise RuntimeError("chronological target partition produced negative counts") if not np.array_equal( window_starts + training_counts + validation_counts, total_targets ): raise RuntimeError("chronological target window is not a final contiguous suffix") return window_starts def chronological_target_partition( prepared: SequenceData, validation_fraction: float, max_train_targets_per_user: int, max_validation_targets_per_user: int, ) -> TargetPartition: """Select a recent contiguous window with a final out-of-time BCE suffix.""" totals = np.maximum(prepared.lengths - 1, 0).astype(np.int64) validation = np.floor(totals * validation_fraction).astype(np.int64) validation[(totals >= 2) & (validation == 0)] = 1 if max_validation_targets_per_user: validation = np.minimum(validation, max_validation_targets_per_user) history_training = totals - validation training = history_training.copy() if max_train_targets_per_user: training = np.minimum(training, max_train_targets_per_user) window_starts = retained_target_window_starts(totals, validation, training) return TargetPartition( window_starts=window_starts, training_counts=training, validation_counts=validation, history_training_counts=history_training, ) def load_organic_like_outcomes( public: Path, stage: str, item_ids: np.ndarray ) -> dict[int, dict[int, np.ndarray]]: """Load mapped organic-like timestamps from public fitting history.""" prefixes = ("train",) if stage == "validation" else ("train", "val") frames = [ pd.read_parquet( public / "data" / f"{prefix}_likes.parquet", columns=["uid", "timestamp", "item_id", "is_organic"], ) for prefix in prefixes ] likes = pd.concat(frames, ignore_index=True) likes = likes[likes.is_organic == 1].copy() mapped = pd.Index(item_ids).get_indexer( likes.item_id.to_numpy(dtype=np.int64) ) likes = likes.loc[mapped >= 0, ["uid", "timestamp"]].copy() likes["item"] = mapped[mapped >= 0].astype(np.int32, copy=False) likes = likes.sort_values(["uid", "item", "timestamp"], kind="stable") nested: dict[int, dict[int, list[int]]] = {} for raw_uid, raw_timestamp, raw_item in likes.itertuples( index=False, name=None ): nested.setdefault(int(raw_uid), {}).setdefault(int(raw_item), []).append( int(raw_timestamp) ) result = { uid: { item: np.asarray(timestamps, dtype=np.int64) for item, timestamps in items.items() } for uid, items in nested.items() } log( f"loaded organic-like outcomes: rows={len(likes):,} " f"users={len(result):,}" ) return result def build_target_weights( public: Path, stage: str, prepared: SequenceData, training_counts: np.ndarray, config: Config, ) -> np.ndarray: """Build nonzero per-token weights without dropping adjacent targets.""" if config.target_weighting == "uniform": weights = np.ones(len(prepared.sequence_items), dtype=np.float32) else: weights = np.full( len(prepared.sequence_items), config.ordinary_target_weight, dtype=np.float32, ) organic_strong = ( (prepared.event_features[:, 0] > 0.5) & (prepared.event_features[:, 1] >= 0.8) ) weights[organic_strong] = config.organic_strong_target_weight if config.target_weighting == "discovery-graded": like_outcomes = load_organic_like_outcomes( public, stage, prepared.item_ids ) history_end = ( base.TRAIN_END if stage == "validation" else base.VALIDATION_END ) horizon = config.discovery_horizon_days * base.DAY grade_three = 0 for user_row, uid in enumerate(prepared.user_ids): start = int(prepared.offsets[user_row]) end = int(prepared.offsets[user_row + 1]) if end - start < 2: continue last_training_target = start + int(training_counts[user_row]) first_validation_target = last_training_target + 1 split_timestamp = ( int(prepared.sequence_timestamps[first_validation_target]) if first_validation_target < end else history_end ) user_likes = like_outcomes.get(int(uid), {}) seen = {int(prepared.sequence_items[start])} for position in range(start + 1, end): item = int(prepared.sequence_items[position]) novel = item not in seen seen.add(item) if not novel: # Repeats remain training examples, but they cannot be # eligible discovery outcomes at the corresponding # cutoff and therefore keep only the ordinary weight. weights[position] = config.ordinary_target_weight continue timestamps = user_likes.get(item) if timestamps is None: continue timestamp = int(prepared.sequence_timestamps[position]) upper = min( timestamp + horizon, split_timestamp if position <= last_training_target else history_end, ) outcome = int(np.searchsorted(timestamps, timestamp, side="left")) if outcome < len(timestamps) and int(timestamps[outcome]) < upper: weights[position] = config.organic_like_target_weight grade_three += 1 log( f"grade-3 first-encounter targets={grade_three:,} " f"horizon_days={config.discovery_horizon_days}" ) log( f"target weighting={config.target_weighting} " f"min={weights.min():.3f} mean={weights.mean():.3f} " f"max={weights.max():.3f}" ) return weights def build_dense_block( prepared: SequenceData, target_weights: np.ndarray, users: np.ndarray, window_starts: np.ndarray, process_counts: np.ndarray, score_starts: np.ndarray, start: int, steps: int, config: Config, ) -> tuple[np.ndarray, ...]: """Build one causal sequence block and mark the target rows to score.""" batch_size = len(users) padding_item = len(prepared.item_ids) input_items = np.full((batch_size, steps), padding_item, dtype=np.uint16) event_features = np.zeros((batch_size, steps, 3), dtype=np.float16) activity_features = np.zeros((batch_size, steps, 4), dtype=np.float16) state_ids = np.zeros((batch_size, steps), dtype=np.uint32) targets = np.zeros((batch_size, steps), dtype=np.uint16) weights = np.ones((batch_size, steps), dtype=np.float32) scored = np.zeros((batch_size, steps), dtype=bool) histories = np.full( (batch_size, steps, config.bptt_steps), padding_item, dtype=np.uint16, ) processed = np.zeros(batch_size, dtype=np.int32) lengths = prepared.lengths for row, user_row in enumerate(users): count = min(steps, max(int(process_counts[row]) - start, 0)) if not count: continue offset = int(prepared.offsets[user_row]) window_start = int(window_starts[row]) absolute_start = window_start + start positions = slice( offset + absolute_start, offset + absolute_start + count, ) target_positions = slice( offset + absolute_start + 1, offset + absolute_start + count + 1, ) input_items[row, :count] = prepared.sequence_items[positions] event_features[row, :count] = prepared.event_features[positions] activity_features[row, :count] = prepared.activity_features[positions] state_ids[row, :count] = prepared.state_ids[positions] targets[row, :count] = prepared.sequence_items[target_positions] weights[row, :count] = target_weights[target_positions] score_from = min(count, max(int(score_starts[row]) - start, 0)) scored[row, score_from:count] = True processed[row] = count user_sequence = prepared.sequence_items[ offset : offset + int(lengths[user_row]) ] histories[row, :count] = history_windows( user_sequence, absolute_start, count, config.bptt_steps, padding_item, ) return ( input_items, event_features, activity_features, state_ids, targets, scored, histories, processed, weights, ) def score_target_microbatches( model: base.SequentialTwoTower, prepared: SequenceData, config: Config, sequence_vectors: torch.Tensor, flat_states: np.ndarray, flat_activity: np.ndarray, positive_items: torch.Tensor, negatives: torch.Tensor, negative_weights: torch.Tensor, sample_weights: np.ndarray, device: torch.device, backward: bool, ) -> tuple[dict[str, float], torch.Tensor | None]: """Compute one logical block's exact BCE in bounded-memory slices.""" target_count = len(positive_items) total_weight = float(sample_weights.sum()) if total_weight <= 0: raise RuntimeError("target block has no positive sample weight") sequence_gradient = torch.zeros_like(sequence_vectors) if backward else None statistics = { "loss_sum": 0.0, "positive_sum": 0.0, "negative_sum": 0.0, "pair_correct": 0.0, "weight_sum": 0.0, } for begin in range(0, target_count, config.loss_microbatch_size): end = min(begin + config.loss_microbatch_size, target_count) selected_states = flat_states[begin:end] if backward: sequence_input = ( sequence_vectors[begin:end].detach().requires_grad_(True) ) else: sequence_input = sequence_vectors[begin:end] liked_vectors, disliked_vectors, _ = pool_explicit_states( model, prepared, selected_states, device ) counts = prepared.state_counts[selected_states] numeric = np.zeros((end - begin, 6), dtype=np.float32) numeric[:, :4] = flat_activity[begin:end] numeric[:, 4] = np.log1p(counts[:, 0]) / 6.0 numeric[:, 5] = np.log1p(counts[:, 1]) / 4.0 query_vectors = user_queries( model, sequence_input, liked_vectors, disliked_vectors, torch.from_numpy(numeric).to(device), ) positive_vectors, negative_vectors = candidate_vectors( model, positive_items[begin:end], negatives[begin:end], ) positive_logits = (query_vectors * positive_vectors).sum(dim=1) negative_logits = torch.einsum( "bd,bkd->bk", query_vectors, negative_vectors ) positive_loss = F.softplus(-positive_logits) negative_loss = corrected_negative_loss( negative_logits, negative_weights[begin:end], config ) selected_weights = torch.from_numpy( sample_weights[begin:end].astype(np.float32, copy=False) ).to(device) loss_sum = ( selected_weights * (positive_loss + negative_loss) / 2.0 ).sum() if not bool(torch.isfinite(loss_sum)): raise RuntimeError("dense next-item training produced non-finite loss") if backward: (loss_sum / total_weight).backward() if sequence_input.grad is None: raise RuntimeError("sequence microbatch did not produce a gradient") sequence_gradient[begin:end] = sequence_input.grad statistics["loss_sum"] += float(loss_sum.detach().cpu()) statistics["positive_sum"] += float(positive_logits.detach().sum().cpu()) statistics["negative_sum"] += float(negative_logits.detach().sum().cpu()) statistics["pair_correct"] += float( (positive_logits.detach()[:, None] > negative_logits.detach()).sum().cpu() ) statistics["weight_sum"] += float(selected_weights.sum().detach().cpu()) return statistics, sequence_gradient def release_accelerator_cache(device: torch.device) -> None: if device.type == "mps": torch.mps.empty_cache() def run_training_epoch( model: base.SequentialTwoTower, optimizer: torch.optim.Optimizer, prepared: SequenceData, config: Config, target_weights: np.ndarray, window_starts: np.ndarray, target_counts: np.ndarray, item_correction: torch.Tensor, known_positive_packed: np.ndarray | None, generator: torch.Generator, device: torch.device, epoch: int, ) -> dict[str, float]: model.train() train_users = np.flatnonzero(target_counts > 0) sorted_users = train_users[ np.argsort(target_counts[train_users], kind="stable") ] groups = [ sorted_users[start : start + config.user_batch_size] for start in range(0, len(sorted_users), config.user_batch_size) ] ] group_order = torch.randperm(len(groups), generator=generator).numpy() negative_count = config.uniform_negatives + config.inbatch_negatives total_loss = 0.0 total_targets = 0 total_weight = 0.0 total_positive = 0.0 total_negative = 0.0 total_pair_correct = 0.0 batches = 0 started = time.monotonic() for group_number in group_order: users = groups[int(group_number)] group_counts = target_counts[users] hidden = torch.zeros( 1, len(users), config.embedding_dim, device=device ) max_targets = int(group_counts.max()) for start in range(0, max_targets, config.bptt_steps): steps = min(config.bptt_steps, max_targets - start) block = build_dense_block( prepared, target_weights, users, window_starts[users], group_counts, np.zeros(len(users), dtype=np.int64), start, steps, config, ) sequence_outputs, _ = event_sequence( model, torch.from_numpy(block[0].astype(np.int64)).to(device), torch.from_numpy(block[1].astype(np.float32)).to(device), hidden, ) hidden = torch.stack( [ sequence_outputs[row, count - 1] if count else hidden[0, row] for row, count in enumerate(block[7]) ] ).unsqueeze(0).detach() flat_scored = block[5].reshape(-1) if not np.any(flat_scored): continue mask = torch.from_numpy(flat_scored).to(device) sequence_vectors = sequence_outputs.reshape( -1, config.embedding_dim )[mask] flat_states = block[3].reshape(-1)[flat_scored] positive_items = torch.from_numpy( block[4].reshape(-1)[flat_scored].astype(np.int64) ).to(device) known_history = torch.from_numpy( block[6].reshape(-1, config.bptt_steps)[flat_scored].astype( np.int64 ) ).to(device) liked_items = torch.from_numpy( prepared.liked_states[flat_states].astype(np.int64) ).to(device) flat_user_rows = np.broadcast_to( users[:, None], block[5…2627 tokens truncated…dden_1", "user_hidden_2", "item_hidden_1", "item_hidden_2", ) mismatches = { name: (stored_config.get(name), getattr(config, name)) for name in architecture_fields if stored_config.get(name) != getattr(config, name) } if mismatches: raise RuntimeError( f"resume checkpoint architecture differs: {mismatches}" ) stored_filter_scope = stored_config.get( "negative_filter_scope", "last-100" ) stored_target_weighting = stored_config.get( "target_weighting", "uniform" ) stored_weight_values = ( stored_config.get("ordinary_target_weight", 1.0), stored_config.get("organic_strong_target_weight", 1.0), stored_config.get("organic_like_target_weight", 3.0), stored_config.get("discovery_horizon_days", 7), ) current_weight_values = ( config.ordinary_target_weight, config.organic_strong_target_weight, config.organic_like_target_weight, config.discovery_horizon_days, ) stored_window_values = ( stored_config.get("validation_fraction", 0.10), stored_config.get("max_train_targets_per_user", 0), stored_config.get("max_validation_targets_per_user", 0), ) current_window_values = ( config.validation_fraction, config.max_train_targets_per_user, config.max_validation_targets_per_user, ) objective_changed = ( stored_filter_scope != config.negative_filter_scope or stored_target_weighting != config.target_weighting or stored_weight_values != current_weight_values or stored_window_values != current_window_values ) model.load_state_dict(payload["model_state"]) if "optimizer_state" in payload and not objective_changed: optimizer.load_state_dict(payload["optimizer_state"]) for group in optimizer.param_groups: group["lr"] = config.learning_rate group["weight_decay"] = config.weight_decay log(f"restored optimizer state from {resume_checkpoint}") else: log( f"using a fresh AdamW optimizer after loading {resume_checkpoint}" ) if "generator_state" in payload and not objective_changed: generator.set_state(payload["generator_state"]) start_epoch = int(payload["epoch"]) if start_epoch >= config.epochs: raise RuntimeError( f"resume epoch {start_epoch} must be below --epochs {config.epochs}" ) prior = { str(name): float(value) for name, value in payload.get("diagnostics", {}).items() } if objective_changed: prior.pop("validation_loss", None) log( "BCE objective changed: filter " f"{stored_filter_scope}->{config.negative_filter_scope}, " "target weighting " f"{stored_target_weighting}->{config.target_weighting}, " f"target window {stored_window_values}->{current_window_values}; " "recomputing the resume checkpoint's validation BCE" ) if "validation_loss" not in prior: prior.update( evaluate_validation_bce( model, prepared, config, target_weights, partition.window_starts, training_counts, validation_counts, item_correction, known_positive_packed, device, ) ) history.append(prior) best_loss = float(prior["validation_loss"]) best_epoch = start_epoch best_state = snapshot_model(model) log( f"resumed validation-BCE training: epoch={start_epoch} " f"loss={best_loss:.6f}" ) for epoch in range(start_epoch + 1, config.epochs + 1): diagnostics = run_training_epoch( model, optimizer, prepared, config, target_weights, partition.window_starts, training_counts, item_correction, known_positive_packed, generator, device, epoch, ) validation = evaluate_validation_bce( model, prepared, config, target_weights, partition.window_starts, training_counts, validation_counts, item_correction, known_positive_packed, device, ) diagnostics.update(validation) history.append(diagnostics) current_state = snapshot_model(model) if checkpoint_dir is not None: save_epoch_checkpoint( checkpoint_dir, epoch, current_state, config, diagnostics, optimizer, generator, ) improved = ( diagnostics["validation_loss"] < best_loss - config.early_stopping_min_delta ) if improved: best_loss = diagnostics["validation_loss"] best_epoch = epoch best_state = current_state epochs_without_improvement = 0 if checkpoint_dir is not None: torch.save( { "epoch": epoch, "config": asdict(config), "diagnostics": diagnostics, "model_state": current_state, "optimizer_state": optimizer.state_dict(), "generator_state": generator.get_state(), }, checkpoint_dir / "best.pt", ) else: epochs_without_improvement += 1 log( f"epoch={epoch}/{config.epochs} train_loss={diagnostics['loss']:.5f} " f"validation_loss={diagnostics['validation_loss']:.5f} " f"pair_accuracy={diagnostics['pair_accuracy']:.3f} " f"train_targets={int(diagnostics['targets']):,} " f"validation_targets={int(diagnostics['validation_targets']):,} " f"train_seconds={diagnostics['seconds']:.1f} " f"validation_seconds={diagnostics['validation_seconds']:.1f}" ) release_accelerator_cache(device) if epochs_without_improvement >= config.early_stopping_patience: log( f"early stopping after epoch={epoch}; " f"best_epoch={best_epoch} best_validation_loss={best_loss:.6f}" ) break if best_state is None: raise RuntimeError("training did not produce a valid checkpoint") model.load_state_dict(best_state) model.to(device).eval() log( f"restored best validation-BCE checkpoint: epoch={best_epoch} " f"loss={best_loss:.6f}" ) return model, history @torch.inference_mode() def inference_queries( model: base.SequentialTwoTower, prepared: SequenceData, config: Config, device: torch.device, ) -> torch.Tensor: sequence_rows = prepared.inference_user_rows lengths = np.where( sequence_rows >= 0, prepared.lengths[np.maximum(sequence_rows, 0)], 0 sequence_rows >= 0, prepared.lengths[np.maximum(sequence_rows, 0)], 0 ) ordered = np.argsort(lengths, kind="stable") result = torch.zeros( len(sequence_rows), config.embedding_dim, dtype=torch.float32 ) for group_start in range(0, len(ordered), config.user_batch_size): output_rows = ordered[group_start : group_start + config.user_batch_size] rows = sequence_rows[output_rows] group_lengths = lengths[output_rows] hidden = torch.zeros( 1, len(output_rows), config.embedding_dim, device=device ) for start in range(0, int(group_lengths.max(initial=0)), config.bptt_steps): steps = min( config.bptt_steps, int(group_lengths.max(initial=0)) - start, ) items = np.full( (len(output_rows), steps), len(prepared.item_ids), dtype=np.uint16, ) features = np.zeros((len(output_rows), steps, 3), dtype=np.float16) counts = np.zeros(len(output_rows), dtype=np.int32) for local_row, sequence_row in enumerate(rows): if sequence_row < 0: continue count = min(steps, max(int(group_lengths[local_row]) - start, 0)) if not count: continue offset = int(prepared.offsets[sequence_row]) selected = slice(offset + start, offset + start + count) items[local_row, :count] = prepared.sequence_items[selected] features[local_row, :count] = prepared.event_features[selected] counts[local_row] = count outputs, _ = event_sequence( model, torch.from_numpy(items.astype(np.int64)).to(device), torch.from_numpy(features.astype(np.float32)).to(device), hidden, ) next_rows: list[torch.Tensor] = [] for local_row, count in enumerate(counts): next_rows.append( outputs[local_row, count - 1] if count else hidden[0, local_row] ) hidden = torch.stack(next_rows).unsqueeze(0) liked_items = torch.from_numpy( prepared.inference_liked_items[output_rows].astype(np.int64) ).to(device) disliked_items = torch.from_numpy( prepared.inference_disliked_items[output_rows].astype(np.int64) ).to(device) queries = user_queries( model, hidden.squeeze(0), model._mean_context_channel(liked_items), model._mean_context_channel(disliked_items), torch.from_numpy( prepared.inference_user_features[output_rows].astype(np.float32) ).to(device), ) result[torch.from_numpy(output_rows.astype(np.int64))] = queries.cpu() return result @torch.inference_mode() def rank_candidates( model: base.SequentialTwoTower, prepared: SequenceData, eligibility: CandidateSet, config: Config, device: torch.device, ranking_batch_size: int, ) -> tuple[pd.DataFrame, dict[str, float]]: model.eval() queries = inference_queries(model, prepared, config, device) item_parts: list[torch.Tensor] = [] for start in range(0, len(prepared.item_ids), 2_048): rows = torch.arange( start, min(start + 2_048, len(prepared.item_ids)), dtype=torch.long, device=device, ) item_parts.append(model.encode_items(rows).cpu()) items = torch.cat(item_parts) rows: list[tuple[int, int, int]] = [] for start in range(0, len(eligibility.uids), ranking_batch_size): end = min(start + ranking_batch_size, len(eligibility.uids)) scores = queries[start:end] @ items.T eligible = np.unpackbits( eligibility.packed[start:end], axis=1, count=len(prepared.item_ids), bitorder="little", ).astype(bool, copy=False) scores.masked_fill_(~torch.from_numpy(eligible), -torch.inf) selected = torch.topk(scores, k=base.TOP_N, dim=1).indices.numpy() for local_row, columns in enumerate(selected): uid = int(eligibility.uids[start + local_row]) rows.extend( (uid, int(prepared.item_ids[column]), rank) for rank, column in enumerate(columns, start=1) ) if end == len(eligibility.uids) or end % 1_024 == 0: log(f"ranked users={end:,}/{len(eligibility.uids):,}") query_norm = torch.linalg.vector_norm(queries, dim=1).numpy() item_norm = torch.linalg.vector_norm(items, dim=1).numpy() diagnostics = { "query_norm_mean": float(query_norm.mean()), "query_norm_std": float(query_norm.std()), "item_norm_mean": float(item_norm.mean()), "item_norm_std": float(item_norm.std()), } return pd.DataFrame(rows, columns=["uid", "item_id", "rank"]), diagnostics def write_ranking(frame: pd.DataFrame, output: Path) -> None: output.parent.mkdir(parents=True, exist_ok=True) if output.suffix == ".parquet": frame.to_parquet(output, index=False, compression="zstd") elif output.suffix == ".csv": frame.to_csv(output, index=False) else: raise ValueError("--output must end in .parquet or .csv") def main() -> int: parser = argparse.ArgumentParser(description=__doc__) parser.add_argument( "--stage", choices=("validation", "submission"), default="validation" ) parser.add_argument("--output", type=Path, required=True) parser.add_argument("--public-dir", type=Path, required=True) parser.add_argument("--prepared-cache", type=Path) parser.add_argument("--device", choices=("cpu", "mps"), default="cpu") parser.add_argument("--threads", type=int, default=8) parser.add_argument("--bptt-steps", type=int, default=Config.bptt_steps) parser.add_argument( "--user-batch-size", type=int, default=Config.user_batch_size ) parser.add_argument("--epochs", type=int, default=Config.epochs) parser.add_argument("--learning-rate", type=float, default=Config.learning_rate) parser.add_argument( "--loss-microbatch-size", type=int, default=Config.loss_microbatch_size, ) parser.add_argument( "--negative-filter-scope", choices=("full-training-history", "last-100"), default=Config.negative_filter_scope, ) parser.add_argument( "--target-weighting", choices=("discovery-graded", "organic-strong", "uniform"), default=Config.target_weighting, ) parser.add_argument( "--ordinary-target-weight", type=float, default=Config.ordinary_target_weight, ) parser.add_argument( "--organic-strong-target-weight", type=float, default=Config.organic_strong_target_weight, ) parser.add_argument( "--organic-like-target-weight", type=float, default=Config.organic_like_target_weight, ) parser.add_argument( "--discovery-horizon-days", type=int, default=Config.discovery_horizon_days, ) parser.add_argument( "--validation-fraction", type=float, default=Config.validation_fraction, ) parser.add_argument( "--max-train-targets-per-user", type=int, default=Config.max_train_targets_per_user, help="retain this many recent pre-validation targets per user; 0 is uncapped", ) parser.add_argument( "--max-validation-targets-per-user", type=int, default=Config.max_validation_targets_per_user, help="retain this many final validation targets per user; 0 is uncapped", ) parser.add_argument( "--early-stopping-patience", type=int, default=Config.early_stopping_patience, ) parser.add_argument( "--early-stopping-min-delta", type=float, default=Config.early_stopping_min_delta, ) parser.add_argument("--checkpoint-dir", type=Path) parser.add_argument("--resume-checkpoint", type=Path) parser.add_argument("--seed", type=int, default=Config.seed) parser.add_argument("--ranking-batch-size", type=int, default=64) parser.add_argument("--prepare-only", action="store_true") args = parser.parse_args() if args.threads < 1 or args.bptt_steps < 2 or args.user_batch_size < 1: parser.error("threads, BPTT steps, and user batch size must be positive") if ( args.epochs < 1 or args.learning_rate <= 0 or args.loss_microbatch_size < 1 or args.early_stopping_patience < 1 or args.early_stopping_min_delta < 0 or args.ordinary_target_weight <= 0 or args.organic_strong_target_weight <= 0 or args.organic_like_target_weight <= 0 or args.discovery_horizon_days < 1 or args.max_train_targets_per_user < 0 or args.max_validation_targets_per_user < 0 ): parser.error("training counts, learning rate, and patience are invalid") if not 0.0 < args.validation_fraction < 1.0: parser.error("validation fraction must be strictly between zero and one") public = args.public_dir.resolve() if not (public / "data" / "candidate_catalog.parquet").is_file(): parser.error(f"public task data not found under {public}") config = Config( bptt_steps=args.bptt_steps, user_batch_size=args.user_batch_size, epochs=args.epochs, learning_rate=args.learning_rate, loss_microbatch_size=args.loss_microbatch_size, negative_filter_scope=args.negative_filter_scope, target_weighting=args.target_weighting, ordinary_target_weight=args.ordinary_target_weight, organic_strong_target_weight=args.organic_strong_target_weight, organic_like_target_weight=args.organic_like_target_weight, discovery_horizon_days=args.discovery_horizon_days, validation_fraction=args.validation_fraction, max_train_targets_per_user=args.max_train_targets_per_user, max_validation_targets_per_user=args.max_validation_targets_per_user, early_stopping_patience=args.early_stopping_patience, early_stopping_min_delta=args.early_stopping_min_delta, seed=args.seed, ) torch.set_num_threads(args.threads) torch.set_num_interop_threads(1) seed_everything(config.seed) device = torch.device(args.device) if device.type == "mps" and not torch.backends.mps.is_available(): parser.error("MPS was requested but is unavailable") started = time.monotonic() prepared, eligibility = load_or_prepare( public, args.stage, config, args.prepared_cache.resolve() if args.prepared_cache else None, ) target_partition = chronological_target_partition( prepared, config.validation_fraction, config.max_train_targets_per_user, config.max_validation_targets_per_user, ) if args.prepare_only: print( json.dumps( { "stage": args.stage, "sequence_users": len(prepared.user_ids), "sequence_tokens": len(prepared.sequence_items), "next_item_targets": prepared.target_count, "selected_training_targets": int( target_partition.training_counts.sum() ), "selected_validation_targets": int( target_partition.validation_counts.sum() ), "discarded_old_targets": int( target_partition.window_starts.sum() ), "target_users": len(eligibility.uids), "runtime_seconds": round(time.monotonic() - started, 3), "config": asdict(config), }, indent=2, ) ) return 0 model, training_history = train_model( public, args.stage, ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-21e2ff5d-6c78-456c-af62-edfa2366f842", "process_id": "61724", "command": [ "/bin/sh", "-lc", "sed -n '760,1225p' /app/solution/train_current_model.py\nsed -n '1225,1790p' /app/solution/train_current_model.py\nsed -n '1790,2095p' /app/solution/train_current_model.py" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "read", "cmd": "sed -n '760,1225p' /app/solution/train_current_model.py", "name": "train_current_model.py", "path": "/app/solution/train_current_model.py" }, { "type": "read", "cmd": "sed -n '1225,1790p' /app/solution/train_current_model.py", "name": "train_current_model.py", "path": "/app/solution/train_current_model.py" }, { "type": "read", "cmd": "sed -n '1790,2095p' /app/solution/train_current_model.py", "name": "train_current_model.py", "path": "/app/solution/train_current_model.py" } ], "source": "unified_exec_startup", "status": "completed", "stdout": " )\n\n\ndef event_sequence(\n model: base.SequentialTwoTower,\n items: torch.Tensor,\n features: torch.Tensor,\n hidden: torch.Tensor,\n) -> tuple[torch.Tensor, torch.Tensor]:\n inputs = torch.cat([model.context_embedding(items), features], dim=-1)\n projected = model.event_projection(inputs)\n return model.sequence_encoder(projected, hidden)\n\n\ndef pool_explicit_states(\n model: base.SequentialTwoTower,\n prepared: SequenceData,\n state_ids: np.ndarray,\n device: torch.device,\n) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:\n unique_states, inverse = np.unique(state_ids, return_inverse=True)\n liked_items = torch.from_numpy(\n prepared.liked_states[unique_states].astype(np.int64)\n ).to(device)\n disliked_items = torch.from_numpy(\n prepared.disliked_states[unique_states].astype(np.int64)\n ).to(device)\n liked_vectors = model._mean_context_channel(liked_items)\n disliked_vectors = model._mean_context_channel(disliked_items)\n inverse_rows = torch.from_numpy(inverse.astype(np.int64)).to(device)\n counts = prepared.state_counts[state_ids]\n return (\n liked_vectors[inverse_rows],\n disliked_vectors[inverse_rows],\n torch.from_numpy(counts.astype(np.float32)).to(device),\n )\n\n\ndef user_queries(\n model: base.SequentialTwoTower,\n sequence_vectors: torch.Tensor,\n liked_vectors: torch.Tensor,\n disliked_vectors: torch.Tensor,\n user_features: torch.Tensor,\n) -> torch.Tensor:\n return model.user_tower(\n torch.cat(\n [sequence_vectors, liked_vectors, disliked_vectors, user_features],\n dim=1,\n )\n )\n\n\ndef candidate_vectors(\n model: base.SequentialTwoTower,\n positives: torch.Tensor,\n negatives: torch.Tensor,\n) -> tuple[torch.Tensor, torch.Tensor]:\n all_items = torch.cat([positives, negatives.reshape(-1)])\n unique_items, inverse = torch.unique(all_items, return_inverse=True)\n encoded = model.encode_items(unique_items)\n positive_vectors = encoded[inverse[: len(positives)]]\n negative_vectors = encoded[inverse[len(positives) :]].reshape(\n len(positives), negatives.shape[1], -1\n )\n return positive_vectors, negative_vectors\n\n\ndef history_windows(\n sequence: np.ndarray,\n start: int,\n count: int,\n width: int,\n padding_item: int,\n) -> np.ndarray:\n base_start = max(0, start - width + 1)\n segment = sequence[base_start : start + count]\n padded = np.concatenate(\n [np.full(width - 1, padding_item, dtype=np.uint16), segment]\n )\n windows = np.lib.stride_tricks.sliding_window_view(padded, width)\n offset = start - base_start\n return windows[offset : offset + count].copy()\n\n\ndef corrected_negative_loss(\n logits: torch.Tensor,\n correction: torch.Tensor,\n config: Config,\n) -> torch.Tensor:\n raw = F.softplus(logits)\n total = config.uniform_negatives + config.inbatch_negatives\n result = torch.zeros(len(logits), device=logits.device)\n if config.uniform_negatives:\n result += config.uniform_negatives * raw[\n :, : config.uniform_negatives\n ].mean(dim=1)\n if config.inbatch_negatives:\n selected = slice(config.uniform_negatives, total)\n weights = correction[:, selected]\n result += config.inbatch_negatives * (\n (raw[:, selected] * weights).sum(dim=1)\n / weights.sum(dim=1).clamp_min(1.0e-6)\n )\n return result / total\n\n\ndef retained_target_window_starts(\n total_targets: np.ndarray,\n validation_counts: np.ndarray,\n training_counts: np.ndarray,\n) -> np.ndarray:\n \"\"\"Return the discarded prefix before the retained chronological suffix.\"\"\"\n training_region_ends = total_targets - validation_counts\n window_starts = training_region_ends - training_counts\n if (\n np.any(window_starts < 0)\n or np.any(training_counts < 0)\n or np.any(validation_counts < 0)\n ):\n raise RuntimeError(\"chronological target partition produced negative counts\")\n if not np.array_equal(\n window_starts + training_counts + validation_counts, total_targets\n ):\n raise RuntimeError(\"chronological target window is not a final contiguous suffix\")\n return window_starts\n\n\ndef chronological_target_partition(\n prepared: SequenceData,\n validation_fraction: float,\n max_train_targets_per_user: int,\n max_validation_targets_per_user: int,\n) -> TargetPartition:\n \"\"\"Select a recent contiguous window with a final out-of-time BCE suffix.\"\"\"\n totals = np.maximum(prepared.lengths - 1, 0).astype(np.int64)\n validation = np.floor(totals * validation_fraction).astype(np.int64)\n validation[(totals >= 2) & (validation == 0)] = 1\n if max_validation_targets_per_user:\n validation = np.minimum(validation, max_validation_targets_per_user)\n\n history_training = totals - validation\n training = history_training.copy()\n if max_train_targets_per_user:\n training = np.minimum(training, max_train_targets_per_user)\n window_starts = retained_target_window_starts(totals, validation, training)\n return TargetPartition(\n window_starts=window_starts,\n training_counts=training,\n validation_counts=validation,\n history_training_counts=history_training,\n )\n\n\ndef load_organic_like_outcomes(\n public: Path, stage: str, item_ids: np.ndarray\n) -> dict[int, dict[int, np.ndarray]]:\n \"\"\"Load mapped organic-like timestamps from public fitting history.\"\"\"\n prefixes = (\"train\",) if stage == \"validation\" else (\"train\", \"val\")\n frames = [\n pd.read_parquet(\n public / \"data\" / f\"{prefix}_likes.parquet\",\n columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\"],\n )\n for prefix in prefixes\n ]\n likes = pd.concat(frames, ignore_index=True)\n likes = likes[likes.is_organic == 1].copy()\n mapped = pd.Index(item_ids).get_indexer(\n likes.item_id.to_numpy(dtype=np.int64)\n )\n likes = likes.loc[mapped >= 0, [\"uid\", \"timestamp\"]].copy()\n likes[\"item\"] = mapped[mapped >= 0].astype(np.int32, copy=False)\n likes = likes.sort_values([\"uid\", \"item\", \"timestamp\"], kind=\"stable\")\n nested: dict[int, dict[int, list[int]]] = {}\n for raw_uid, raw_timestamp, raw_item in likes.itertuples(\n index=False, name=None\n ):\n nested.setdefault(int(raw_uid), {}).setdefault(int(raw_item), []).append(\n int(raw_timestamp)\n )\n result = {\n uid: {\n item: np.asarray(timestamps, dtype=np.int64)\n for item, timestamps in items.items()\n }\n for uid, items in nested.items()\n }\n log(\n f\"loaded organic-like outcomes: rows={len(likes):,} \"\n f\"users={len(result):,}\"\n )\n return result\n\n\ndef build_target_weights(\n public: Path,\n stage: str,\n prepared: SequenceData,\n training_counts: np.ndarray,\n config: Config,\n) -> np.ndarray:\n \"\"\"Build nonzero per-token weights without dropping adjacent targets.\"\"\"\n if config.target_weighting == \"uniform\":\n weights = np.ones(len(prepared.sequence_items), dtype=np.float32)\n else:\n weights = np.full(\n len(prepared.sequence_items),\n config.ordinary_target_weight,\n dtype=np.float32,\n )\n organic_strong = (\n (prepared.event_features[:, 0] > 0.5)\n & (prepared.event_features[:, 1] >= 0.8)\n )\n weights[organic_strong] = config.organic_strong_target_weight\n if config.target_weighting == \"discovery-graded\":\n like_outcomes = load_organic_like_outcomes(\n public, stage, prepared.item_ids\n )\n history_end = (\n base.TRAIN_END if stage == \"validation\" else base.VALIDATION_END\n )\n horizon = config.discovery_horizon_days * base.DAY\n grade_three = 0\n for user_row, uid in enumerate(prepared.user_ids):\n start = int(prepared.offsets[user_row])\n end = int(prepared.offsets[user_row + 1])\n if end - start < 2:\n continue\n last_training_target = start + int(training_counts[user_row])\n first_validation_target = last_training_target + 1\n split_timestamp = (\n int(prepared.sequence_timestamps[first_validation_target])\n if first_validation_target < end\n else history_end\n )\n user_likes = like_outcomes.get(int(uid), {})\n seen = {int(prepared.sequence_items[start])}\n for position in range(start + 1, end):\n item = int(prepared.sequence_items[position])\n novel = item not in seen\n seen.add(item)\n if not novel:\n # Repeats remain training examples, but they cannot be\n # eligible discovery outcomes at the corresponding\n # cutoff and therefore keep only the ordinary weight.\n weights[position] = config.ordinary_target_weight\n continue\n timestamps = user_likes.get(item)\n if timestamps is None:\n continue\n timestamp = int(prepared.sequence_timestamps[position])\n upper = min(\n timestamp + horizon,\n split_timestamp\n if position <= last_training_target\n else history_end,\n )\n outcome = int(np.searchsorted(timestamps, timestamp, side=\"left\"))\n if outcome < len(timestamps) and int(timestamps[outcome]) < upper:\n weights[position] = config.organic_like_target_weight\n grade_three += 1\n log(\n f\"grade-3 first-encounter targets={grade_three:,} \"\n f\"horizon_days={config.discovery_horizon_days}\"\n )\n log(\n f\"target weighting={config.target_weighting} \"\n f\"min={weights.min():.3f} mean={weights.mean():.3f} \"\n f\"max={weights.max():.3f}\"\n )\n return weights\n\n\ndef build_dense_block(\n prepared: SequenceData,\n target_weights: np.ndarray,\n users: np.ndarray,\n window_starts: np.ndarray,\n process_counts: np.ndarray,\n score_starts: np.ndarray,\n start: int,\n steps: int,\n config: Config,\n) -> tuple[np.ndarray, ...]:\n \"\"\"Build one causal sequence block and mark the target rows to score.\"\"\"\n batch_size = len(users)\n padding_item = len(prepared.item_ids)\n input_items = np.full((batch_size, steps), padding_item, dtype=np.uint16)\n event_features = np.zeros((batch_size, steps, 3), dtype=np.float16)\n activity_features = np.zeros((batch_size, steps, 4), dtype=np.float16)\n state_ids = np.zeros((batch_size, steps), dtype=np.uint32)\n targets = np.zeros((batch_size, steps), dtype=np.uint16)\n weights = np.ones((batch_size, steps), dtype=np.float32)\n scored = np.zeros((batch_size, steps), dtype=bool)\n histories = np.full(\n (batch_size, steps, config.bptt_steps),\n padding_item,\n dtype=np.uint16,\n )\n processed = np.zeros(batch_size, dtype=np.int32)\n\n lengths = prepared.lengths\n for row, user_row in enumerate(users):\n count = min(steps, max(int(process_counts[row]) - start, 0))\n if not count:\n continue\n offset = int(prepared.offsets[user_row])\n window_start = int(window_starts[row])\n absolute_start = window_start + start\n positions = slice(\n offset + absolute_start,\n offset + absolute_start + count,\n )\n target_positions = slice(\n offset + absolute_start + 1,\n offset + absolute_start + count + 1,\n )\n input_items[row, :count] = prepared.sequence_items[positions]\n event_features[row, :count] = prepared.event_features[positions]\n activity_features[row, :count] = prepared.activity_features[positions]\n state_ids[row, :count] = prepared.state_ids[positions]\n targets[row, :count] = prepared.sequence_items[target_positions]\n weights[row, :count] = target_weights[target_positions]\n score_from = min(count, max(int(score_starts[row]) - start, 0))\n scored[row, score_from:count] = True\n processed[row] = count\n user_sequence = prepared.sequence_items[\n offset : offset + int(lengths[user_row])\n ]\n histories[row, :count] = history_windows(\n user_sequence,\n absolute_start,\n count,\n config.bptt_steps,\n padding_item,\n )\n return (\n input_items,\n event_features,\n activity_features,\n state_ids,\n targets,\n scored,\n histories,\n processed,\n weights,\n )\n\n\ndef score_target_microbatches(\n model: base.SequentialTwoTower,\n prepared: SequenceData,\n config: Config,\n sequence_vectors: torch.Tensor,\n flat_states: np.ndarray,\n flat_activity: np.ndarray,\n positive_items: torch.Tensor,\n negatives: torch.Tensor,\n negative_weights: torch.Tensor,\n sample_weights: np.ndarray,\n device: torch.device,\n backward: bool,\n) -> tuple[dict[str, float], torch.Tensor | None]:\n \"\"\"Compute one logical block's exact BCE in bounded-memory slices.\"\"\"\n target_count = len(positive_items)\n total_weight = float(sample_weights.sum())\n if total_weight <= 0:\n raise RuntimeError(\"target block has no positive sample weight\")\n sequence_gradient = torch.zeros_like(sequence_vectors) if backward else None\n statistics = {\n \"loss_sum\": 0.0,\n \"positive_sum\": 0.0,\n \"negative_sum\": 0.0,\n \"pair_correct\": 0.0,\n \"weight_sum\": 0.0,\n }\n for begin in range(0, target_count, config.loss_microbatch_size):\n end = min(begin + config.loss_microbatch_size, target_count)\n selected_states = flat_states[begin:end]\n if backward:\n sequence_input = (\n sequence_vectors[begin:end].detach().requires_grad_(True)\n )\n else:\n sequence_input = sequence_vectors[begin:end]\n liked_vectors, disliked_vectors, _ = pool_explicit_states(\n model, prepared, selected_states, device\n )\n counts = prepared.state_counts[selected_states]\n numeric = np.zeros((end - begin, 6), dtype=np.float32)\n numeric[:, :4] = flat_activity[begin:end]\n numeric[:, 4] = np.log1p(counts[:, 0]) / 6.0\n numeric[:, 5] = np.log1p(counts[:, 1]) / 4.0\n query_vectors = user_queries(\n model,\n sequence_input,\n liked_vectors,\n disliked_vectors,\n torch.from_numpy(numeric).to(device),\n )\n positive_vectors, negative_vectors = candidate_vectors(\n model,\n positive_items[begin:end],\n negatives[begin:end],\n )\n positive_logits = (query_vectors * positive_vectors).sum(dim=1)\n negative_logits = torch.einsum(\n \"bd,bkd->bk\", query_vectors, negative_vectors\n )\n positive_loss = F.softplus(-positive_logits)\n negative_loss = corrected_negative_loss(\n negative_logits, negative_weights[begin:end], config\n )\n selected_weights = torch.from_numpy(\n sample_weights[begin:end].astype(np.float32, copy=False)\n ).to(device)\n loss_sum = (\n selected_weights * (positive_loss + negative_loss) / 2.0\n ).sum()\n if not bool(torch.isfinite(loss_sum)):\n raise RuntimeError(\"dense next-item training produced non-finite loss\")\n if backward:\n (loss_sum / total_weight).backward()\n if sequence_input.grad is None:\n raise RuntimeError(\"sequence microbatch did not produce a gradient\")\n sequence_gradient[begin:end] = sequence_input.grad\n statistics[\"loss_sum\"] += float(loss_sum.detach().cpu())\n statistics[\"positive_sum\"] += float(positive_logits.detach().sum().cpu())\n statistics[\"negative_sum\"] += float(negative_logits.detach().sum().cpu())\n statistics[\"pair_correct\"] += float(\n (positive_logits.detach()[:, None] > negative_logits.detach()).sum().cpu()\n )\n statistics[\"weight_sum\"] += float(selected_weights.sum().detach().cpu())\n return statistics, sequence_gradient\n\n\ndef release_accelerator_cache(device: torch.device) -> None:\n if device.type == \"mps\":\n torch.mps.empty_cache()\n\n\ndef run_training_epoch(\n model: base.SequentialTwoTower,\n optimizer: torch.optim.Optimizer,\n prepared: SequenceData,\n config: Config,\n target_weights: np.ndarray,\n window_starts: np.ndarray,\n target_counts: np.ndarray,\n item_correction: torch.Tensor,\n known_positive_packed: np.ndarray | None,\n generator: torch.Generator,\n device: torch.device,\n epoch: int,\n) -> dict[str, float]:\n model.train()\n train_users = np.flatnonzero(target_counts > 0)\n sorted_users = train_users[\n np.argsort(target_counts[train_users], kind=\"stable\")\n ]\n groups = [\n sorted_users[start : start + config.user_batch_size]\n for start in range(0, len(sorted_users), config.user_batch_size)\n ]\n ]\n group_order = torch.randperm(len(groups), generator=generator).numpy()\n negative_count = config.uniform_negatives + config.inbatch_negatives\n total_loss = 0.0\n total_targets = 0\n total_weight = 0.0\n total_positive = 0.0\n total_negative = 0.0\n total_pair_correct = 0.0\n batches = 0\n started = time.monotonic()\n\n for group_number in group_order:\n users = groups[int(group_number)]\n group_counts = target_counts[users]\n hidden = torch.zeros(\n 1, len(users), config.embedding_dim, device=device\n )\n max_targets = int(group_counts.max())\n for start in range(0, max_targets, config.bptt_steps):\n steps = min(config.bptt_steps, max_targets - start)\n block = build_dense_block(\n prepared,\n target_weights,\n users,\n window_starts[users],\n group_counts,\n np.zeros(len(users), dtype=np.int64),\n start,\n steps,\n config,\n )\n sequence_outputs, _ = event_sequence(\n model,\n torch.from_numpy(block[0].astype(np.int64)).to(device),\n torch.from_numpy(block[1].astype(np.float32)).to(device),\n hidden,\n )\n hidden = torch.stack(\n [\n sequence_outputs[row, count - 1]\n if count\n else hidden[0, row]\n for row, count in enumerate(block[7])\n ]\n ).unsqueeze(0).detach()\n flat_scored = block[5].reshape(-1)\n if not np.any(flat_scored):\n continue\n mask = torch.from_numpy(flat_scored).to(device)\n sequence_vectors = sequence_outputs.reshape(\n -1, config.embedding_dim\n )[mask]\n flat_states = block[3].reshape(-1)[flat_scored]\n positive_items = torch.from_numpy(\n block[4].reshape(-1)[flat_scored].astype(np.int64)\n ).to(device)\n known_history = torch.from_numpy(\n block[6].reshape(-1, config.bptt_steps)[flat_scored].astype(\n np.int64\n )\n ).to(device)\n liked_items = torch.from_numpy(\n prepared.liked_states[flat_states].astype(np.int64)\n ).to(device)\n flat_user_rows = np.broadcast_to(\n users[:, None], block[5].shape\n ).reshape(-1)[flat_scored]\n negatives, negative_weights = draw_training_negatives(\n positive_items,\n known_history,\n liked_items,\n flat_user_rows,\n known_positive_packed,\n prepared,\n config,\n item_correction,\n generator,\n device,\n )\n optimizer.zero_grad(set_to_none=True)\n statistics, sequence_gradient = score_target_microbatches(\n model,\n prepared,\n config,\n sequence_vectors,\n flat_states,\n block[2].reshape(-1, 4)[flat_scored].astype(np.float32),\n positive_items,\n negatives,\n negative_weights,\n block[8].reshape(-1)[flat_scored],\n device,\n backward=True,\n )\n if sequence_gradient is None:\n raise RuntimeError(\"training block omitted its sequence gradient\")\n sequence_vectors.backward(sequence_gradient)\n nn.utils.clip_grad_norm_(model.parameters(), config.gradient_clip)\n optimizer.step()\n\n count = len(positive_items)\n total_loss += statistics[\"loss_sum\"]\n total_targets += count\n total_weight += statistics[\"weight_sum\"]\n total_positive += statistics[\"positive_sum\"]\n total_negative += statistics[\"negative_sum\"]\n total_pair_correct += statistics[\"pair_correct\"]\n batches += 1\n if batches % 25 == 0:\n release_accelerator_cache(device)\n if batches % 250 == 0:\n batch_bce = statistics[\"loss_sum\"] / max(\n statistics[\"weight_sum\"], 1.0e-12\n )\n running_bce = total_loss / max(total_weight, 1.0e-12)\n log(\n f\"epoch={epoch}/{config.epochs} batches={batches:,} \"\n f\"targets={total_targets:,} \"\n f\"batch_bce={batch_bce:.6f} \"\n f\"running_bce={running_bce:.6f} \"\n f\"elapsed={time.monotonic() - started:.1f}s\"\n )\n\n return {\n \"epoch\": float(epoch),\n \"loss\": total_loss / max(total_weight, 1.0e-12),\n \"positive_logit\": total_positive / max(total_targets, 1),\n \"negative_logit\": total_negative / max(total_targets * negative_count, 1),\n \"pair_accuracy\": total_pair_correct\n / max(total_targets * negative_count, 1),\n \"targets\": float(total_targets),\n \"target_weight_sum\": total_weight,\n \"batches\": float(batches),\n \"seconds\": time.monotonic() - started,\n }\n\n\n@torch.no_grad()\ndef evaluate_validation_bce(\n model: base.SequentialTwoTower,\n prepared: SequenceData,\n config: Config,\n target_weights: np.ndarray,\n window_starts: np.ndarray,\n training_counts: np.ndarray,\n validation_counts: np.ndarray,\n item_correction: torch.Tensor,\n known_positive_packed: np.ndarray | None,\n device: torch.device,\n) -> dict[str, float]:\n \"\"\"Measure deterministic BCE on each user's chronological target suffix.\"\"\"\n model.eval()\n totals = training_counts + validation_counts\n users_with_validation = np.flatnonzero(validation_counts > 0)\n ordered = users_with_validation[\n np.argsort(totals[users_with_validation], kind=\"stable\")\n ]\n groups = [\n ordered[start : start + config.user_batch_size]\n for start in range(0, len(ordered), config.user_batch_size)\n ]\n generator = torch.Generator(device=\"cpu\").manual_seed(config.seed + 20_000)\n total_loss = 0.0\n total_targets = 0\n total_weight = 0.0\n started = time.monotonic()\n\n for group_number, users in enumerate(groups, start=1):\n group_totals = totals[users]\n group_starts = training_counts[users]\n hidden = torch.zeros(\n 1, len(users), config.embedding_dim, device=device\n )\n max_targets = int(group_totals.max())\n for start in range(0, max_targets, config.bptt_steps):\n steps = min(config.bptt_steps, max_targets - start)\n block = build_dense_block(\n prepared,\n target_weights,\n users,\n window_starts[users],\n group_totals,\n group_starts,\n start,\n steps,\n config,\n )\n sequence_outputs, _ = event_sequence(\n model,\n torch.from_numpy(block[0].astype(np.int64)).to(device),\n torch.from_numpy(block[1].astype(np.float32)).to(device),\n hidden,\n )\n hidden = torch.stack(\n [\n sequence_outputs[row, count - 1]\n if count\n else hidden[0, row]\n for row, count in enumerate(block[7])\n ]\n ).unsqueeze(0)\n flat_scored = block[5].reshape(-1)\n if not np.any(flat_scored):\n continue\n mask = torch.from_numpy(flat_scored).to(device)\n sequence_vectors = sequence_outputs.reshape(\n -1, config.embedding_dim\n )[mask]\n flat_states = block[3].reshape(-1)[flat_scored]\n positive_items = torch.from_numpy(\n block[4].reshape(-1)[flat_scored].astype(np.int64)\n ).to(device)\n known_history = torch.from_numpy(\n block[6].reshape(-1, config.bptt_steps)[flat_scored].astype(\n np.int64\n )\n ).to(device)\n liked_items = torch.from_numpy(\n prepared.liked_states[flat_states].astype(np.int64)\n ).to(device)\n flat_user_rows = np.broadcast_to(\n users[:, None], block[5].shape\n ).reshape(-1)[flat_scored]\n negatives, negative_weights = draw_training_negatives(\n positive_items,\n known_history,\n liked_items,\n flat_user_rows,\n known_positive_packed,\n prepared,\n config,\n item_correction,\n generator,\n device,\n )\n statistics, _ = score_target_microbatches(\n model,\n prepared,\n config,\n sequence_vectors,\n flat_states,\n block[2].reshape(-1, 4)[flat_scored].astype(np.float32),\n positive_items,\n negatives,\n negative_weights,\n block[8].reshape(-1)[flat_scored],\n device,\n backward=False,\n )\n total_loss += statistics[\"loss_sum\"]\n total_targets += len(positive_items)\n total_weight += statistics[\"weight_sum\"]\n if group_number % 25 == 0:\n release_accelerator_cache(device)\n return {\n \"validation_loss\": total_loss / max(total_weight, 1.0e-12),\n \"validation_targets\": float(total_targets),\n \"validation_target_weight_sum\": total_weight,\n \"validation_seconds\": time.monotonic() - started,\n }\n\n\ndef snapshot_model(model: nn.Module) -> dict[str, torch.Tensor]:\n return {\n name: value.detach().cpu().clone()\n for name, value in model.state_dict().items()\n }\n\n\ndef save_epoch_checkpoint(\n checkpoint_dir: Path,\n epoch: int,\n model_state: dict[str, torch.Tensor],\n config: Config,\n diagnostics: dict[str, float],\n optimizer: torch.optim.Optimizer,\n generator: torch.Generator,\n) -> Path:\n checkpoint_dir.mkdir(parents=True, exist_ok=True)\n path = checkpoint_dir / f\"epoch-{epoch:02d}.pt\"\n torch.save(\n {\n \"epoch\": epoch,\n \"config\": asdict(config),\n \"diagnostics\": diagnostics,\n \"model_state\": model_state,\n \"optimizer_state\": optimizer.state_dict(),\n \"generator_state\": generator.get_state(),\n },\n path,\n )\n log(f\"saved epoch checkpoint: {path}\")\n return path\n\n\ndef train_model(\n public: Path,\n stage: str,\n prepared: SequenceData,\n config: Config,\n device: torch.device,\n checkpoint_dir: Path | None,\n resume_checkpoint: Path | None,\n) -> tuple[base.SequentialTwoTower, list[dict[str, float]]]:\n partition = chronological_target_partition(\n prepared,\n config.validation_fraction,\n config.max_train_targets_per_user,\n config.max_validation_targets_per_user,\n )\n training_counts = partition.training_counts\n validation_counts = partition.validation_counts\n log(\n f\"chronological BCE window: train={int(training_counts.sum()):,} \"\n f\"validation={int(validation_counts.sum()):,} \"\n f\"selected={partition.selected_count:,} \"\n f\"discarded_old_targets={int(partition.window_starts.sum()):,} \"\n f\"all={prepared.target_count:,}\"\n )\n model = base.SequentialTwoTower(prepared.item_features, model_config(config)).to(\n device\n )\n optimizer = torch.optim.AdamW(\n model.parameters(), lr=config.learning_rate, weight_decay=config.weight_decay\n )\n generator = torch.Generator(device=\"cpu\").manual_seed(config.seed + 10_000)\n item_correction = build_item_correction(\n prepared,\n config,\n partition.window_starts,\n training_counts,\n )\n target_weights = build_target_weights(\n public,\n stage,\n prepared,\n partition.history_training_counts,\n config,\n )\n known_positive_packed = (\n build_known_positive_filter(prepared, partition.history_training_counts)\n if config.negative_filter_scope == \"full-training-history\"\n else None\n )\n history: list[dict[str, float]] = []\n best_loss = float(\"inf\")\n best_epoch = 0\n best_state: dict[str, torch.Tensor] | None = None\n epochs_without_improvement = 0\n start_epoch = 0\n\n if resume_checkpoint is not None:\n payload = torch.load(\n resume_checkpoint, map_location=device, weights_only=False\n )\n stored_config = payload.get(\"config\", {})\n architecture_fields = (\n \"bptt_steps\",\n \"embedding_dim\",\n \"hidden_dim\",\n \"user_hidden_1\",\n \"user_hidden_2\",\n \"item_hidden_1\",\n \"item_hidden_2\",\n )\n mismatches = {\n name: (stored_config.get(name), getattr(config, name))\n for name in architecture_fields\n if stored_config.get(name) != getattr(config, name)\n }\n if mismatches:\n raise RuntimeError(\n f\"resume checkpoint architecture differs: {mismatches}\"\n )\n stored_filter_scope = stored_config.get(\n \"negative_filter_scope\", \"last-100\"\n )\n stored_target_weighting = stored_config.get(\n \"target_weighting\", \"uniform\"\n )\n stored_weight_values = (\n stored_config.get(\"ordinary_target_weight\", 1.0),\n stored_config.get(\"organic_strong_target_weight\", 1.0),\n stored_config.get(\"organic_like_target_weight\", 3.0),\n stored_config.get(\"discovery_horizon_days\", 7),\n )\n current_weight_values = (\n config.ordinary_target_weight,\n config.organic_strong_target_weight,\n config.organic_like_target_weight,\n config.discovery_horizon_days,\n )\n stored_window_values = (\n stored_config.get(\"validation_fraction\", 0.10),\n stored_config.get(\"max_train_targets_per_user\", 0),\n stored_config.get(\"max_validation_targets_per_user\", 0),\n )\n current_window_values = (\n config.validation_fraction,\n config.max_train_targets_per_user,\n config.max_validation_targets_per_user,\n )\n objective_changed = (\n stored_filter_scope != config.negative_filter_scope\n or stored_target_weighting != config.target_weighting\n or stored_weight_values != current_weight_values\n or stored_window_values != current_window_values\n )\n model.load_state_dict(payload[\"model_state\"])\n if \"optimizer_state\" in payload and not objective_changed:\n optimizer.load_state_dict(payload[\"optimizer_state\"])\n for group in optimizer.param_groups:\n group[\"lr\"] = config.learning_rate\n group[\"weight_decay\"] = config.weight_decay\n log(f\"restored optimizer state from {resume_checkpoint}\")\n else:\n log(\n f\"using a fresh AdamW optimizer after loading {resume_checkpoint}\"\n )\n if \"generator_state\" in payload and not objective_changed:\n generator.set_state(payload[\"generator_state\"])\n start_epoch = int(payload[\"epoch\"])\n if start_epoch >= config.epochs:\n raise RuntimeError(\n f\"resume epoch {start_epoch} must be below --epochs {config.epochs}\"\n )\n prior = {\n str(name): float(value)\n for name, value in payload.get(\"diagnostics\", {}).items()\n }\n if objective_changed:\n prior.pop(\"validation_loss\", None)\n log(\n \"BCE objective changed: filter \"\n f\"{stored_filter_scope}->{config.negative_filter_scope}, \"\n \"target weighting \"\n f\"{stored_target_weighting}->{config.target_weighting}, \"\n f\"target window {stored_window_values}->{current_window_values}; \"\n \"recomputing the resume checkpoint's validation BCE\"\n )\n if \"validation_loss\" not in prior:\n prior.update(\n evaluate_validation_bce(\n model,\n prepared,\n config,\n target_weights,\n partition.window_starts,\n training_counts,\n validation_counts,\n item_correction,\n known_positive_packed,\n device,\n )\n )\n history.append(prior)\n best_loss = float(prior[\"validation_loss\"])\n best_epoch = start_epoch\n best_state = snapshot_model(model)\n log(\n f\"resumed validation-BCE training: epoch={start_epoch} \"\n f\"loss={best_loss:.6f}\"\n )\n\n for epoch in range(start_epoch + 1, config.epochs + 1):\n diagnostics = run_training_epoch(\n model,\n optimizer,\n prepared,\n config,\n target_weights,\n partition.window_starts,\n training_counts,\n item_correction,\n known_positive_packed,\n generator,\n device,\n epoch,\n )\n validation = evaluate_validation_bce(\n model,\n prepared,\n config,\n target_weights,\n partition.window_starts,\n training_counts,\n validation_counts,\n item_correction,\n known_positive_packed,\n device,\n )\n diagnostics.update(validation)\n history.append(diagnostics)\n current_state = snapshot_model(model)\n if checkpoint_dir is not None:\n save_epoch_checkpoint(\n checkpoint_dir,\n epoch,\n current_state,\n config,\n diagnostics,\n optimizer,\n generator,\n )\n improved = (\n diagnostics[\"validation_loss\"]\n < best_loss - config.early_stopping_min_delta\n )\n if improved:\n best_loss = diagnostics[\"validation_loss\"]\n best_epoch = epoch\n best_state = current_state\n epochs_without_improvement = 0\n if checkpoint_dir is not None:\n torch.save(\n {\n \"epoch\": epoch,\n \"config\": asdict(config),\n \"diagnostics\": diagnostics,\n \"model_state\": current_state,\n \"optimizer_state\": optimizer.state_dict(),\n \"generator_state\": generator.get_state(),\n },\n checkpoint_dir / \"best.pt\",\n )\n else:\n epochs_without_improvement += 1\n log(\n f\"epoch={epoch}/{config.epochs} train_loss={diagnostics['loss']:.5f} \"\n f\"validation_loss={diagnostics['validation_loss']:.5f} \"\n f\"pair_accuracy={diagnostics['pair_accuracy']:.3f} \"\n f\"train_targets={int(diagnostics['targets']):,} \"\n f\"validation_targets={int(diagnostics['validation_targets']):,} \"\n f\"train_seconds={diagnostics['seconds']:.1f} \"\n f\"validation_seconds={diagnostics['validation_seconds']:.1f}\"\n )\n release_accelerator_cache(device)\n if epochs_without_improvement >= config.early_stopping_patience:\n log(\n f\"early stopping after epoch={epoch}; \"\n f\"best_epoch={best_epoch} best_validation_loss={best_loss:.6f}\"\n )\n break\n\n if best_state is None:\n raise RuntimeError(\"training did not produce a valid checkpoint\")\n model.load_state_dict(best_state)\n model.to(device).eval()\n log(\n f\"restored best validation-BCE checkpoint: epoch={best_epoch} \"\n f\"loss={best_loss:.6f}\"\n )\n return model, history\n\n\n@torch.inference_mode()\ndef inference_queries(\n model: base.SequentialTwoTower,\n prepared: SequenceData,\n config: Config,\n device: torch.device,\n) -> torch.Tensor:\n sequence_rows = prepared.inference_user_rows\n lengths = np.where(\n sequence_rows >= 0, prepared.lengths[np.maximum(sequence_rows, 0)], 0\n sequence_rows >= 0, prepared.lengths[np.maximum(sequence_rows, 0)], 0\n )\n ordered = np.argsort(lengths, kind=\"stable\")\n result = torch.zeros(\n len(sequence_rows), config.embedding_dim, dtype=torch.float32\n )\n for group_start in range(0, len(ordered), config.user_batch_size):\n output_rows = ordered[group_start : group_start + config.user_batch_size]\n rows = sequence_rows[output_rows]\n group_lengths = lengths[output_rows]\n hidden = torch.zeros(\n 1, len(output_rows), config.embedding_dim, device=device\n )\n for start in range(0, int(group_lengths.max(initial=0)), config.bptt_steps):\n steps = min(\n config.bptt_steps,\n int(group_lengths.max(initial=0)) - start,\n )\n items = np.full(\n (len(output_rows), steps),\n len(prepared.item_ids),\n dtype=np.uint16,\n )\n features = np.zeros((len(output_rows), steps, 3), dtype=np.float16)\n counts = np.zeros(len(output_rows), dtype=np.int32)\n for local_row, sequence_row in enumerate(rows):\n if sequence_row < 0:\n continue\n count = min(steps, max(int(group_lengths[local_row]) - start, 0))\n if not count:\n continue\n offset = int(prepared.offsets[sequence_row])\n selected = slice(offset + start, offset + start + count)\n items[local_row, :count] = prepared.sequence_items[selected]\n features[local_row, :count] = prepared.event_features[selected]\n counts[local_row] = count\n outputs, _ = event_sequence(\n model,\n torch.from_numpy(items.astype(np.int64)).to(device),\n torch.from_numpy(features.astype(np.float32)).to(device),\n hidden,\n )\n next_rows: list[torch.Tensor] = []\n for local_row, count in enumerate(counts):\n next_rows.append(\n outputs[local_row, count - 1]\n if count\n else hidden[0, local_row]\n )\n hidden = torch.stack(next_rows).unsqueeze(0)\n\n liked_items = torch.from_numpy(\n prepared.inference_liked_items[output_rows].astype(np.int64)\n ).to(device)\n disliked_items = torch.from_numpy(\n prepared.inference_disliked_items[output_rows].astype(np.int64)\n ).to(device)\n queries = user_queries(\n model,\n hidden.squeeze(0),\n model._mean_context_channel(liked_items),\n model._mean_context_channel(disliked_items),\n torch.from_numpy(\n prepared.inference_user_features[output_rows].astype(np.float32)\n ).to(device),\n )\n result[torch.from_numpy(output_rows.astype(np.int64))] = queries.cpu()\n return result\n\n\n@torch.inference_mode()\ndef rank_candidates(\n model: base.SequentialTwoTower,\n prepared: SequenceData,\n eligibility: CandidateSet,\n config: Config,\n device: torch.device,\n ranking_batch_size: int,\n) -> tuple[pd.DataFrame, dict[str, float]]:\n model.eval()\n queries = inference_queries(model, prepared, config, device)\n item_parts: list[torch.Tensor] = []\n for start in range(0, len(prepared.item_ids), 2_048):\n rows = torch.arange(\n start,\n min(start + 2_048, len(prepared.item_ids)),\n dtype=torch.long,\n device=device,\n )\n item_parts.append(model.encode_items(rows).cpu())\n items = torch.cat(item_parts)\n rows: list[tuple[int, int, int]] = []\n for start in range(0, len(eligibility.uids), ranking_batch_size):\n end = min(start + ranking_batch_size, len(eligibility.uids))\n scores = queries[start:end] @ items.T\n eligible = np.unpackbits(\n eligibility.packed[start:end],\n axis=1,\n count=len(prepared.item_ids),\n bitorder=\"little\",\n ).astype(bool, copy=False)\n scores.masked_fill_(~torch.from_numpy(eligible), -torch.inf)\n selected = torch.topk(scores, k=base.TOP_N, dim=1).indices.numpy()\n for local_row, columns in enumerate(selected):\n uid = int(eligibility.uids[start + local_row])\n rows.extend(\n (uid, int(prepared.item_ids[column]), rank)\n for rank, column in enumerate(columns, start=1)\n )\n if end == len(eligibility.uids) or end % 1_024 == 0:\n log(f\"ranked users={end:,}/{len(eligibility.uids):,}\")\n query_norm = torch.linalg.vector_norm(queries, dim=1).numpy()\n item_norm = torch.linalg.vector_norm(items, dim=1).numpy()\n diagnostics = {\n \"query_norm_mean\": float(query_norm.mean()),\n \"query_norm_std\": float(query_norm.std()),\n \"item_norm_mean\": float(item_norm.mean()),\n \"item_norm_std\": float(item_norm.std()),\n }\n return pd.DataFrame(rows, columns=[\"uid\", \"item_id\", \"rank\"]), diagnostics\n\n\ndef write_ranking(frame: pd.DataFrame, output: Path) -> None:\n output.parent.mkdir(parents=True, exist_ok=True)\n if output.suffix == \".parquet\":\n frame.to_parquet(output, index=False, compression=\"zstd\")\n elif output.suffix == \".csv\":\n frame.to_csv(output, index=False)\n else:\n raise ValueError(\"--output must end in .parquet or .csv\")\n\n\ndef main() -> int:\n parser = argparse.ArgumentParser(description=__doc__)\n parser.add_argument(\n \"--stage\", choices=(\"validation\", \"submission\"), default=\"validation\"\n )\n parser.add_argument(\"--output\", type=Path, required=True)\n parser.add_argument(\"--public-dir\", type=Path, required=True)\n parser.add_argument(\"--prepared-cache\", type=Path)\n parser.add_argument(\"--device\", choices=(\"cpu\", \"mps\"), default=\"cpu\")\n parser.add_argument(\"--threads\", type=int, default=8)\n parser.add_argument(\"--bptt-steps\", type=int, default=Config.bptt_steps)\n parser.add_argument(\n \"--user-batch-size\", type=int, default=Config.user_batch_size\n )\n parser.add_argument(\"--epochs\", type=int, default=Config.epochs)\n parser.add_argument(\"--learning-rate\", type=float, default=Config.learning_rate)\n parser.add_argument(\n \"--loss-microbatch-size\",\n type=int,\n default=Config.loss_microbatch_size,\n )\n parser.add_argument(\n \"--negative-filter-scope\",\n choices=(\"full-training-history\", \"last-100\"),\n default=Config.negative_filter_scope,\n )\n parser.add_argument(\n \"--target-weighting\",\n choices=(\"discovery-graded\", \"organic-strong\", \"uniform\"),\n default=Config.target_weighting,\n )\n parser.add_argument(\n \"--ordinary-target-weight\",\n type=float,\n default=Config.ordinary_target_weight,\n )\n parser.add_argument(\n \"--organic-strong-target-weight\",\n type=float,\n default=Config.organic_strong_target_weight,\n )\n parser.add_argument(\n \"--organic-like-target-weight\",\n type=float,\n default=Config.organic_like_target_weight,\n )\n parser.add_argument(\n \"--discovery-horizon-days\",\n type=int,\n default=Config.discovery_horizon_days,\n )\n parser.add_argument(\n \"--validation-fraction\",\n type=float,\n default=Config.validation_fraction,\n )\n parser.add_argument(\n \"--max-train-targets-per-user\",\n type=int,\n default=Config.max_train_targets_per_user,\n help=\"retain this many recent pre-validation targets per user; 0 is uncapped\",\n )\n parser.add_argument(\n \"--max-validation-targets-per-user\",\n type=int,\n default=Config.max_validation_targets_per_user,\n help=\"retain this many final validation targets per user; 0 is uncapped\",\n )\n parser.add_argument(\n \"--early-stopping-patience\",\n type=int,\n default=Config.early_stopping_patience,\n )\n parser.add_argument(\n \"--early-stopping-min-delta\",\n type=float,\n default=Config.early_stopping_min_delta,\n )\n parser.add_argument(\"--checkpoint-dir\", type=Path)\n parser.add_argument(\"--resume-checkpoint\", type=Path)\n parser.add_argument(\"--seed\", type=int, default=Config.seed)\n parser.add_argument(\"--ranking-batch-size\", type=int, default=64)\n parser.add_argument(\"--prepare-only\", action=\"store_true\")\n args = parser.parse_args()\n\n if args.threads < 1 or args.bptt_steps < 2 or args.user_batch_size < 1:\n parser.error(\"threads, BPTT steps, and user batch size must be positive\")\n if (\n args.epochs < 1\n or args.learning_rate <= 0\n or args.loss_microbatch_size < 1\n or args.early_stopping_patience < 1\n or args.early_stopping_min_delta < 0\n or args.ordinary_target_weight <= 0\n or args.organic_strong_target_weight <= 0\n or args.organic_like_target_weight <= 0\n or args.discovery_horizon_days < 1\n or args.max_train_targets_per_user < 0\n or args.max_validation_targets_per_user < 0\n ):\n parser.error(\"training counts, learning rate, and patience are invalid\")\n if not 0.0 < args.validation_fraction < 1.0:\n parser.error(\"validation fraction must be strictly between zero and one\")\n public = args.public_dir.resolve()\n if not (public / \"data\" / \"candidate_catalog.parquet\").is_file():\n parser.error(f\"public task data not found under {public}\")\n config = Config(\n bptt_steps=args.bptt_steps,\n user_batch_size=args.user_batch_size,\n epochs=args.epochs,\n learning_rate=args.learning_rate,\n loss_microbatch_size=args.loss_microbatch_size,\n negative_filter_scope=args.negative_filter_scope,\n target_weighting=args.target_weighting,\n ordinary_target_weight=args.ordinary_target_weight,\n organic_strong_target_weight=args.organic_strong_target_weight,\n organic_like_target_weight=args.organic_like_target_weight,\n discovery_horizon_days=args.discovery_horizon_days,\n validation_fraction=args.validation_fraction,\n max_train_targets_per_user=args.max_train_targets_per_user,\n max_validation_targets_per_user=args.max_validation_targets_per_user,\n early_stopping_patience=args.early_stopping_patience,\n early_stopping_min_delta=args.early_stopping_min_delta,\n seed=args.seed,\n )\n torch.set_num_threads(args.threads)\n torch.set_num_interop_threads(1)\n seed_everything(config.seed)\n device = torch.device(args.device)\n if device.type == \"mps\" and not torch.backends.mps.is_available():\n parser.error(\"MPS was requested but is unavailable\")\n\n started = time.monotonic()\n prepared, eligibility = load_or_prepare(\n public,\n args.stage,\n config,\n args.prepared_cache.resolve() if args.prepared_cache else None,\n )\n target_partition = chronological_target_partition(\n prepared,\n config.validation_fraction,\n config.max_train_targets_per_user,\n config.max_validation_targets_per_user,\n )\n if args.prepare_only:\n print(\n json.dumps(\n {\n \"stage\": args.stage,\n \"sequence_users\": len(prepared.user_ids),\n \"sequence_tokens\": len(prepared.sequence_items),\n \"next_item_targets\": prepared.target_count,\n \"selected_training_targets\": int(\n target_partition.training_counts.sum()\n ),\n \"selected_validation_targets\": int(\n target_partition.validation_counts.sum()\n ),\n \"discarded_old_targets\": int(\n target_partition.window_starts.sum()\n ),\n \"target_users\": len(eligibility.uids),\n \"runtime_seconds\": round(time.monotonic() - started, 3),\n \"config\": asdict(config),\n },\n indent=2,\n )\n )\n return 0\n\n model, training_history = train_model(\n public,\n args.stage,\n", "stderr": "", "aggregated_output": " )\n\n\ndef event_sequence(\n model: base.SequentialTwoTower,\n items: torch.Tensor,\n features: torch.Tensor,\n hidden: torch.Tensor,\n) -> tuple[torch.Tensor, torch.Tensor]:\n inputs = torch.cat([model.context_embedding(items), features], dim=-1)\n projected = model.event_projection(inputs)\n return model.sequence_encoder(projected, hidden)\n\n\ndef pool_explicit_states(\n model: base.SequentialTwoTower,\n prepared: SequenceData,\n state_ids: np.ndarray,\n device: torch.device,\n) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:\n unique_states, inverse = np.unique(state_ids, return_inverse=True)\n liked_items = torch.from_numpy(\n prepared.liked_states[unique_states].astype(np.int64)\n ).to(device)\n disliked_items = torch.from_numpy(\n prepared.disliked_states[unique_states].astype(np.int64)\n ).to(device)\n liked_vectors = model._mean_context_channel(liked_items)\n disliked_vectors = model._mean_context_channel(disliked_items)\n inverse_rows = torch.from_numpy(inverse.astype(np.int64)).to(device)\n counts = prepared.state_counts[state_ids]\n return (\n liked_vectors[inverse_rows],\n disliked_vectors[inverse_rows],\n torch.from_numpy(counts.astype(np.float32)).to(device),\n )\n\n\ndef user_queries(\n model: base.SequentialTwoTower,\n sequence_vectors: torch.Tensor,\n liked_vectors: torch.Tensor,\n disliked_vectors: torch.Tensor,\n user_features: torch.Tensor,\n) -> torch.Tensor:\n return model.user_tower(\n torch.cat(\n [sequence_vectors, liked_vectors, disliked_vectors, user_features],\n dim=1,\n )\n )\n\n\ndef candidate_vectors(\n model: base.SequentialTwoTower,\n positives: torch.Tensor,\n negatives: torch.Tensor,\n) -> tuple[torch.Tensor, torch.Tensor]:\n all_items = torch.cat([positives, negatives.reshape(-1)])\n unique_items, inverse = torch.unique(all_items, return_inverse=True)\n encoded = model.encode_items(unique_items)\n positive_vectors = encoded[inverse[: len(positives)]]\n negative_vectors = encoded[inverse[len(positives) :]].reshape(\n len(positives), negatives.shape[1], -1\n )\n return positive_vectors, negative_vectors\n\n\ndef history_windows(\n sequence: np.ndarray,\n start: int,\n count: int,\n width: int,\n padding_item: int,\n) -> np.ndarray:\n base_start = max(0, start - width + 1)\n segment = sequence[base_start : start + count]\n padded = np.concatenate(\n [np.full(width - 1, padding_item, dtype=np.uint16), segment]\n )\n windows = np.lib.stride_tricks.sliding_window_view(padded, width)\n offset = start - base_start\n return windows[offset : offset + count].copy()\n\n\ndef corrected_negative_loss(\n logits: torch.Tensor,\n correction: torch.Tensor,\n config: Config,\n) -> torch.Tensor:\n raw = F.softplus(logits)\n total = config.uniform_negatives + config.inbatch_negatives\n result = torch.zeros(len(logits), device=logits.device)\n if config.uniform_negatives:\n result += config.uniform_negatives * raw[\n :, : config.uniform_negatives\n ].mean(dim=1)\n if config.inbatch_negatives:\n selected = slice(config.uniform_negatives, total)\n weights = correction[:, selected]\n result += config.inbatch_negatives * (\n (raw[:, selected] * weights).sum(dim=1)\n / weights.sum(dim=1).clamp_min(1.0e-6)\n )\n return result / total\n\n\ndef retained_target_window_starts(\n total_targets: np.ndarray,\n validation_counts: np.ndarray,\n training_counts: np.ndarray,\n) -> np.ndarray:\n \"\"\"Return the discarded prefix before the retained chronological suffix.\"\"\"\n training_region_ends = total_targets - validation_counts\n window_starts = training_region_ends - training_counts\n if (\n np.any(window_starts < 0)\n or np.any(training_counts < 0)\n or np.any(validation_counts < 0)\n ):\n raise RuntimeError(\"chronological target partition produced negative counts\")\n if not np.array_equal(\n window_starts + training_counts + validation_counts, total_targets\n ):\n raise RuntimeError(\"chronological target window is not a final contiguous suffix\")\n return window_starts\n\n\ndef chronological_target_partition(\n prepared: SequenceData,\n validation_fraction: float,\n max_train_targets_per_user: int,\n max_validation_targets_per_user: int,\n) -> TargetPartition:\n \"\"\"Select a recent contiguous window with a final out-of-time BCE suffix.\"\"\"\n totals = np.maximum(prepared.lengths - 1, 0).astype(np.int64)\n validation = np.floor(totals * validation_fraction).astype(np.int64)\n validation[(totals >= 2) & (validation == 0)] = 1\n if max_validation_targets_per_user:\n validation = np.minimum(validation, max_validation_targets_per_user)\n\n history_training = totals - validation\n training = history_training.copy()\n if max_train_targets_per_user:\n training = np.minimum(training, max_train_targets_per_user)\n window_starts = retained_target_window_starts(totals, validation, training)\n return TargetPartition(\n window_starts=window_starts,\n training_counts=training,\n validation_counts=validation,\n history_training_counts=history_training,\n )\n\n\ndef load_organic_like_outcomes(\n public: Path, stage: str, item_ids: np.ndarray\n) -> dict[int, dict[int, np.ndarray]]:\n \"\"\"Load mapped organic-like timestamps from public fitting history.\"\"\"\n prefixes = (\"train\",) if stage == \"validation\" else (\"train\", \"val\")\n frames = [\n pd.read_parquet(\n public / \"data\" / f\"{prefix}_likes.parquet\",\n columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\"],\n )\n for prefix in prefixes\n ]\n likes = pd.concat(frames, ignore_index=True)\n likes = likes[likes.is_organic == 1].copy()\n mapped = pd.Index(item_ids).get_indexer(\n likes.item_id.to_numpy(dtype=np.int64)\n )\n likes = likes.loc[mapped >= 0, [\"uid\", \"timestamp\"]].copy()\n likes[\"item\"] = mapped[mapped >= 0].astype(np.int32, copy=False)\n likes = likes.sort_values([\"uid\", \"item\", \"timestamp\"], kind=\"stable\")\n nested: dict[int, dict[int, list[int]]] = {}\n for raw_uid, raw_timestamp, raw_item in likes.itertuples(\n index=False, name=None\n ):\n nested.setdefault(int(raw_uid), {}).setdefault(int(raw_item), []).append(\n int(raw_timestamp)\n )\n result = {\n uid: {\n item: np.asarray(timestamps, dtype=np.int64)\n for item, timestamps in items.items()\n }\n for uid, items in nested.items()\n }\n log(\n f\"loaded organic-like outcomes: rows={len(likes):,} \"\n f\"users={len(result):,}\"\n )\n return result\n\n\ndef build_target_weights(\n public: Path,\n stage: str,\n prepared: SequenceData,\n training_counts: np.ndarray,\n config: Config,\n) -> np.ndarray:\n \"\"\"Build nonzero per-token weights without dropping adjacent targets.\"\"\"\n if config.target_weighting == \"uniform\":\n weights = np.ones(len(prepared.sequence_items), dtype=np.float32)\n else:\n weights = np.full(\n len(prepared.sequence_items),\n config.ordinary_target_weight,\n dtype=np.float32,\n )\n organic_strong = (\n (prepared.event_features[:, 0] > 0.5)\n & (prepared.event_features[:, 1] >= 0.8)\n )\n weights[organic_strong] = config.organic_strong_target_weight\n if config.target_weighting == \"discovery-graded\":\n like_outcomes = load_organic_like_outcomes(\n public, stage, prepared.item_ids\n )\n history_end = (\n base.TRAIN_END if stage == \"validation\" else base.VALIDATION_END\n )\n horizon = config.discovery_horizon_days * base.DAY\n grade_three = 0\n for user_row, uid in enumerate(prepared.user_ids):\n start = int(prepared.offsets[user_row])\n end = int(prepared.offsets[user_row + 1])\n if end - start < 2:\n continue\n last_training_target = start + int(training_counts[user_row])\n first_validation_target = last_training_target + 1\n split_timestamp = (\n int(prepared.sequence_timestamps[first_validation_target])\n if first_validation_target < end\n else history_end\n )\n user_likes = like_outcomes.get(int(uid), {})\n seen = {int(prepared.sequence_items[start])}\n for position in range(start + 1, end):\n item = int(prepared.sequence_items[position])\n novel = item not in seen\n seen.add(item)\n if not novel:\n # Repeats remain training examples, but they cannot be\n # eligible discovery outcomes at the corresponding\n # cutoff and therefore keep only the ordinary weight.\n weights[position] = config.ordinary_target_weight\n continue\n timestamps = user_likes.get(item)\n if timestamps is None:\n continue\n timestamp = int(prepared.sequence_timestamps[position])\n upper = min(\n timestamp + horizon,\n split_timestamp\n if position <= last_training_target\n else history_end,\n )\n outcome = int(np.searchsorted(timestamps, timestamp, side=\"left\"))\n if outcome < len(timestamps) and int(timestamps[outcome]) < upper:\n weights[position] = config.organic_like_target_weight\n grade_three += 1\n log(\n f\"grade-3 first-encounter targets={grade_three:,} \"\n f\"horizon_days={config.discovery_horizon_days}\"\n )\n log(\n f\"target weighting={config.target_weighting} \"\n f\"min={weights.min():.3f} mean={weights.mean():.3f} \"\n f\"max={weights.max():.3f}\"\n )\n return weights\n\n\ndef build_dense_block(\n prepared: SequenceData,\n target_weights: np.ndarray,\n users: np.ndarray,\n window_starts: np.ndarray,\n process_counts: np.ndarray,\n score_starts: np.ndarray,\n start: int,\n steps: int,\n config: Config,\n) -> tuple[np.ndarray, ...]:\n \"\"\"Build one causal sequence block and mark the target rows to score.\"\"\"\n batch_size = len(users)\n padding_item = len(prepared.item_ids)\n input_items = np.full((batch_size, steps), padding_item, dtype=np.uint16)\n event_features = np.zeros((batch_size, steps, 3), dtype=np.float16)\n activity_features = np.zeros((batch_size, steps, 4), dtype=np.float16)\n state_ids = np.zeros((batch_size, steps), dtype=np.uint32)\n targets = np.zeros((batch_size, steps), dtype=np.uint16)\n weights = np.ones((batch_size, steps), dtype=np.float32)\n scored = np.zeros((batch_size, steps), dtype=bool)\n histories = np.full(\n (batch_size, steps, config.bptt_steps),\n padding_item,\n dtype=np.uint16,\n )\n processed = np.zeros(batch_size, dtype=np.int32)\n\n lengths = prepared.lengths\n for row, user_row in enumerate(users):\n count = min(steps, max(int(process_counts[row]) - start, 0))\n if not count:\n continue\n offset = int(prepared.offsets[user_row])\n window_start = int(window_starts[row])\n absolute_start = window_start + start\n positions = slice(\n offset + absolute_start,\n offset + absolute_start + count,\n )\n target_positions = slice(\n offset + absolute_start + 1,\n offset + absolute_start + count + 1,\n )\n input_items[row, :count] = prepared.sequence_items[positions]\n event_features[row, :count] = prepared.event_features[positions]\n activity_features[row, :count] = prepared.activity_features[positions]\n state_ids[row, :count] = prepared.state_ids[positions]\n targets[row, :count] = prepared.sequence_items[target_positions]\n weights[row, :count] = target_weights[target_positions]\n score_from = min(count, max(int(score_starts[row]) - start, 0))\n scored[row, score_from:count] = True\n processed[row] = count\n user_sequence = prepared.sequence_items[\n offset : offset + int(lengths[user_row])\n ]\n histories[row, :count] = history_windows(\n user_sequence,\n absolute_start,\n count,\n config.bptt_steps,\n padding_item,\n )\n return (\n input_items,\n event_features,\n activity_features,\n state_ids,\n targets,\n scored,\n histories,\n processed,\n weights,\n )\n\n\ndef score_target_microbatches(\n model: base.SequentialTwoTower,\n prepared: SequenceData,\n config: Config,\n sequence_vectors: torch.Tensor,\n flat_states: np.ndarray,\n flat_activity: np.ndarray,\n positive_items: torch.Tensor,\n negatives: torch.Tensor,\n negative_weights: torch.Tensor,\n sample_weights: np.ndarray,\n device: torch.device,\n backward: bool,\n) -> tuple[dict[str, float], torch.Tensor | None]:\n \"\"\"Compute one logical block's exact BCE in bounded-memory slices.\"\"\"\n target_count = len(positive_items)\n total_weight = float(sample_weights.sum())\n if total_weight <= 0:\n raise RuntimeError(\"target block has no positive sample weight\")\n sequence_gradient = torch.zeros_like(sequence_vectors) if backward else None\n statistics = {\n \"loss_sum\": 0.0,\n \"positive_sum\": 0.0,\n \"negative_sum\": 0.0,\n \"pair_correct\": 0.0,\n \"weight_sum\": 0.0,\n }\n for begin in range(0, target_count, config.loss_microbatch_size):\n end = min(begin + config.loss_microbatch_size, target_count)\n selected_states = flat_states[begin:end]\n if backward:\n sequence_input = (\n sequence_vectors[begin:end].detach().requires_grad_(True)\n )\n else:\n sequence_input = sequence_vectors[begin:end]\n liked_vectors, disliked_vectors, _ = pool_explicit_states(\n model, prepared, selected_states, device\n )\n counts = prepared.state_counts[selected_states]\n numeric = np.zeros((end - begin, 6), dtype=np.float32)\n numeric[:, :4] = flat_activity[begin:end]\n numeric[:, 4] = np.log1p(counts[:, 0]) / 6.0\n numeric[:, 5] = np.log1p(counts[:, 1]) / 4.0\n query_vectors = user_queries(\n model,\n sequence_input,\n liked_vectors,\n disliked_vectors,\n torch.from_numpy(numeric).to(device),\n )\n positive_vectors, negative_vectors = candidate_vectors(\n model,\n positive_items[begin:end],\n negatives[begin:end],\n )\n positive_logits = (query_vectors * positive_vectors).sum(dim=1)\n negative_logits = torch.einsum(\n \"bd,bkd->bk\", query_vectors, negative_vectors\n )\n positive_loss = F.softplus(-positive_logits)\n negative_loss = corrected_negative_loss(\n negative_logits, negative_weights[begin:end], config\n )\n selected_weights = torch.from_numpy(\n sample_weights[begin:end].astype(np.float32, copy=False)\n ).to(device)\n loss_sum = (\n selected_weights * (positive_loss + negative_loss) / 2.0\n ).sum()\n if not bool(torch.isfinite(loss_sum)):\n raise RuntimeError(\"dense next-item training produced non-finite loss\")\n if backward:\n (loss_sum / total_weight).backward()\n if sequence_input.grad is None:\n raise RuntimeError(\"sequence microbatch did not produce a gradient\")\n sequence_gradient[begin:end] = sequence_input.grad\n statistics[\"loss_sum\"] += float(loss_sum.detach().cpu())\n statistics[\"positive_sum\"] += float(positive_logits.detach().sum().cpu())\n statistics[\"negative_sum\"] += float(negative_logits.detach().sum().cpu())\n statistics[\"pair_correct\"] += float(\n (positive_logits.detach()[:, None] > negative_logits.detach()).sum().cpu()\n )\n statistics[\"weight_sum\"] += float(selected_weights.sum().detach().cpu())\n return statistics, sequence_gradient\n\n\ndef release_accelerator_cache(device: torch.device) -> None:\n if device.type == \"mps\":\n torch.mps.empty_cache()\n\n\ndef run_training_epoch(\n model: base.SequentialTwoTower,\n optimizer: torch.optim.Optimizer,\n prepared: SequenceData,\n config: Config,\n target_weights: np.ndarray,\n window_starts: np.ndarray,\n target_counts: np.ndarray,\n item_correction: torch.Tensor,\n known_positive_packed: np.ndarray | None,\n generator: torch.Generator,\n device: torch.device,\n epoch: int,\n) -> dict[str, float]:\n model.train()\n train_users = np.flatnonzero(target_counts > 0)\n sorted_users = train_users[\n np.argsort(target_counts[train_users], kind=\"stable\")\n ]\n groups = [\n sorted_users[start : start + config.user_batch_size]\n for start in range(0, len(sorted_users), config.user_batch_size)\n ]\n ]\n group_order = torch.randperm(len(groups), generator=generator).numpy()\n negative_count = config.uniform_negatives + config.inbatch_negatives\n total_loss = 0.0\n total_targets = 0\n total_weight = 0.0\n total_positive = 0.0\n total_negative = 0.0\n total_pair_correct = 0.0\n batches = 0\n started = time.monotonic()\n\n for group_number in group_order:\n users = groups[int(group_number)]\n group_counts = target_counts[users]\n hidden = torch.zeros(\n 1, len(users), config.embedding_dim, device=device\n )\n max_targets = int(group_counts.max())\n for start in range(0, max_targets, config.bptt_steps):\n steps = min(config.bptt_steps, max_targets - start)\n block = build_dense_block(\n prepared,\n target_weights,\n users,\n window_starts[users],\n group_counts,\n np.zeros(len(users), dtype=np.int64),\n start,\n steps,\n config,\n )\n sequence_outputs, _ = event_sequence(\n model,\n torch.from_numpy(block[0].astype(np.int64)).to(device),\n torch.from_numpy(block[1].astype(np.float32)).to(device),\n hidden,\n )\n hidden = torch.stack(\n [\n sequence_outputs[row, count - 1]\n if count\n else hidden[0, row]\n for row, count in enumerate(block[7])\n ]\n ).unsqueeze(0).detach()\n flat_scored = block[5].reshape(-1)\n if not np.any(flat_scored):\n continue\n mask = torch.from_numpy(flat_scored).to(device)\n sequence_vectors = sequence_outputs.reshape(\n -1, config.embedding_dim\n )[mask]\n flat_states = block[3].reshape(-1)[flat_scored]\n positive_items = torch.from_numpy(\n block[4].reshape(-1)[flat_scored].astype(np.int64)\n ).to(device)\n known_history = torch.from_numpy(\n block[6].reshape(-1, config.bptt_steps)[flat_scored].astype(\n np.int64\n )\n ).to(device)\n liked_items = torch.from_numpy(\n prepared.liked_states[flat_states].astype(np.int64)\n ).to(device)\n flat_user_rows = np.broadcast_to(\n users[:, None], block[5].shape\n ).reshape(-1)[flat_scored]\n negatives, negative_weights = draw_training_negatives(\n positive_items,\n known_history,\n liked_items,\n flat_user_rows,\n known_positive_packed,\n prepared,\n config,\n item_correction,\n generator,\n device,\n )\n optimizer.zero_grad(set_to_none=True)\n statistics, sequence_gradient = score_target_microbatches(\n model,\n prepared,\n config,\n sequence_vectors,\n flat_states,\n block[2].reshape(-1, 4)[flat_scored].astype(np.float32),\n positive_items,\n negatives,\n negative_weights,\n block[8].reshape(-1)[flat_scored],\n device,\n backward=True,\n )\n if sequence_gradient is None:\n raise RuntimeError(\"training block omitted its sequence gradient\")\n sequence_vectors.backward(sequence_gradient)\n nn.utils.clip_grad_norm_(model.parameters(), config.gradient_clip)\n optimizer.step()\n\n count = len(positive_items)\n total_loss += statistics[\"loss_sum\"]\n total_targets += count\n total_weight += statistics[\"weight_sum\"]\n total_positive += statistics[\"positive_sum\"]\n total_negative += statistics[\"negative_sum\"]\n total_pair_correct += statistics[\"pair_correct\"]\n batches += 1\n if batches % 25 == 0:\n release_accelerator_cache(device)\n if batches % 250 == 0:\n batch_bce = statistics[\"loss_sum\"] / max(\n statistics[\"weight_sum\"], 1.0e-12\n )\n running_bce = total_loss / max(total_weight, 1.0e-12)\n log(\n f\"epoch={epoch}/{config.epochs} batches={batches:,} \"\n f\"targets={total_targets:,} \"\n f\"batch_bce={batch_bce:.6f} \"\n f\"running_bce={running_bce:.6f} \"\n f\"elapsed={time.monotonic() - started:.1f}s\"\n )\n\n return {\n \"epoch\": float(epoch),\n \"loss\": total_loss / max(total_weight, 1.0e-12),\n \"positive_logit\": total_positive / max(total_targets, 1),\n \"negative_logit\": total_negative / max(total_targets * negative_count, 1),\n \"pair_accuracy\": total_pair_correct\n / max(total_targets * negative_count, 1),\n \"targets\": float(total_targets),\n \"target_weight_sum\": total_weight,\n \"batches\": float(batches),\n \"seconds\": time.monotonic() - started,\n }\n\n\n@torch.no_grad()\ndef evaluate_validation_bce(\n model: base.SequentialTwoTower,\n prepared: SequenceData,\n config: Config,\n target_weights: np.ndarray,\n window_starts: np.ndarray,\n training_counts: np.ndarray,\n validation_counts: np.ndarray,\n item_correction: torch.Tensor,\n known_positive_packed: np.ndarray | None,\n device: torch.device,\n) -> dict[str, float]:\n \"\"\"Measure deterministic BCE on each user's chronological target suffix.\"\"\"\n model.eval()\n totals = training_counts + validation_counts\n users_with_validation = np.flatnonzero(validation_counts > 0)\n ordered = users_with_validation[\n np.argsort(totals[users_with_validation], kind=\"stable\")\n ]\n groups = [\n ordered[start : start + config.user_batch_size]\n for start in range(0, len(ordered), config.user_batch_size)\n ]\n generator = torch.Generator(device=\"cpu\").manual_seed(config.seed + 20_000)\n total_loss = 0.0\n total_targets = 0\n total_weight = 0.0\n started = time.monotonic()\n\n for group_number, users in enumerate(groups, start=1):\n group_totals = totals[users]\n group_starts = training_counts[users]\n hidden = torch.zeros(\n 1, len(users), config.embedding_dim, device=device\n )\n max_targets = int(group_totals.max())\n for start in range(0, max_targets, config.bptt_steps):\n steps = min(config.bptt_steps, max_targets - start)\n block = build_dense_block(\n prepared,\n target_weights,\n users,\n window_starts[users],\n group_totals,\n group_starts,\n start,\n steps,\n config,\n )\n sequence_outputs, _ = event_sequence(\n model,\n torch.from_numpy(block[0].astype(np.int64)).to(device),\n torch.from_numpy(block[1].astype(np.float32)).to(device),\n hidden,\n )\n hidden = torch.stack(\n [\n sequence_outputs[row, count - 1]\n if count\n else hidden[0, row]\n for row, count in enumerate(block[7])\n ]\n ).unsqueeze(0)\n flat_scored = block[5].reshape(-1)\n if not np.any(flat_scored):\n continue\n mask = torch.from_numpy(flat_scored).to(device)\n sequence_vectors = sequence_outputs.reshape(\n -1, config.embedding_dim\n )[mask]\n flat_states = block[3].reshape(-1)[flat_scored]\n positive_items = torch.from_numpy(\n block[4].reshape(-1)[flat_scored].astype(np.int64)\n ).to(device)\n known_history = torch.from_numpy(\n block[6].reshape(-1, config.bptt_steps)[flat_scored].astype(\n np.int64\n )\n ).to(device)\n liked_items = torch.from_numpy(\n prepared.liked_states[flat_states].astype(np.int64)\n ).to(device)\n flat_user_rows = np.broadcast_to(\n users[:, None], block[5].shape\n ).reshape(-1)[flat_scored]\n negatives, negative_weights = draw_training_negatives(\n positive_items,\n known_history,\n liked_items,\n flat_user_rows,\n known_positive_packed,\n prepared,\n config,\n item_correction,\n generator,\n device,\n )\n statistics, _ = score_target_microbatches(\n model,\n prepared,\n config,\n sequence_vectors,\n flat_states,\n block[2].reshape(-1, 4)[flat_scored].astype(np.float32),\n positive_items,\n negatives,\n negative_weights,\n block[8].reshape(-1)[flat_scored],\n device,\n backward=False,\n )\n total_loss += statistics[\"loss_sum\"]\n total_targets += len(positive_items)\n total_weight += statistics[\"weight_sum\"]\n if group_number % 25 == 0:\n release_accelerator_cache(device)\n return {\n \"validation_loss\": total_loss / max(total_weight, 1.0e-12),\n \"validation_targets\": float(total_targets),\n \"validation_target_weight_sum\": total_weight,\n \"validation_seconds\": time.monotonic() - started,\n }\n\n\ndef snapshot_model(model: nn.Module) -> dict[str, torch.Tensor]:\n return {\n name: value.detach().cpu().clone()\n for name, value in model.state_dict().items()\n }\n\n\ndef save_epoch_checkpoint(\n checkpoint_dir: Path,\n epoch: int,\n model_state: dict[str, torch.Tensor],\n config: Config,\n diagnostics: dict[str, float],\n optimizer: torch.optim.Optimizer,\n generator: torch.Generator,\n) -> Path:\n checkpoint_dir.mkdir(parents=True, exist_ok=True)\n path = checkpoint_dir / f\"epoch-{epoch:02d}.pt\"\n torch.save(\n {\n \"epoch\": epoch,\n \"config\": asdict(config),\n \"diagnostics\": diagnostics,\n \"model_state\": model_state,\n \"optimizer_state\": optimizer.state_dict(),\n \"generator_state\": generator.get_state(),\n },\n path,\n )\n log(f\"saved epoch checkpoint: {path}\")\n return path\n\n\ndef train_model(\n public: Path,\n stage: str,\n prepared: SequenceData,\n config: Config,\n device: torch.device,\n checkpoint_dir: Path | None,\n resume_checkpoint: Path | None,\n) -> tuple[base.SequentialTwoTower, list[dict[str, float]]]:\n partition = chronological_target_partition(\n prepared,\n config.validation_fraction,\n config.max_train_targets_per_user,\n config.max_validation_targets_per_user,\n )\n training_counts = partition.training_counts\n validation_counts = partition.validation_counts\n log(\n f\"chronological BCE window: train={int(training_counts.sum()):,} \"\n f\"validation={int(validation_counts.sum()):,} \"\n f\"selected={partition.selected_count:,} \"\n f\"discarded_old_targets={int(partition.window_starts.sum()):,} \"\n f\"all={prepared.target_count:,}\"\n )\n model = base.SequentialTwoTower(prepared.item_features, model_config(config)).to(\n device\n )\n optimizer = torch.optim.AdamW(\n model.parameters(), lr=config.learning_rate, weight_decay=config.weight_decay\n )\n generator = torch.Generator(device=\"cpu\").manual_seed(config.seed + 10_000)\n item_correction = build_item_correction(\n prepared,\n config,\n partition.window_starts,\n training_counts,\n )\n target_weights = build_target_weights(\n public,\n stage,\n prepared,\n partition.history_training_counts,\n config,\n )\n known_positive_packed = (\n build_known_positive_filter(prepared, partition.history_training_counts)\n if config.negative_filter_scope == \"full-training-history\"\n else None\n )\n history: list[dict[str, float]] = []\n best_loss = float(\"inf\")\n best_epoch = 0\n best_state: dict[str, torch.Tensor] | None = None\n epochs_without_improvement = 0\n start_epoch = 0\n\n if resume_checkpoint is not None:\n payload = torch.load(\n resume_checkpoint, map_location=device, weights_only=False\n )\n stored_config = payload.get(\"config\", {})\n architecture_fields = (\n \"bptt_steps\",\n \"embedding_dim\",\n \"hidden_dim\",\n \"user_hidden_1\",\n \"user_hidden_2\",\n \"item_hidden_1\",\n \"item_hidden_2\",\n )\n mismatches = {\n name: (stored_config.get(name), getattr(config, name))\n for name in architecture_fields\n if stored_config.get(name) != getattr(config, name)\n }\n if mismatches:\n raise RuntimeError(\n f\"resume checkpoint architecture differs: {mismatches}\"\n )\n stored_filter_scope = stored_config.get(\n \"negative_filter_scope\", \"last-100\"\n )\n stored_target_weighting = stored_config.get(\n \"target_weighting\", \"uniform\"\n )\n stored_weight_values = (\n stored_config.get(\"ordinary_target_weight\", 1.0),\n stored_config.get(\"organic_strong_target_weight\", 1.0),\n stored_config.get(\"organic_like_target_weight\", 3.0),\n stored_config.get(\"discovery_horizon_days\", 7),\n )\n current_weight_values = (\n config.ordinary_target_weight,\n config.organic_strong_target_weight,\n config.organic_like_target_weight,\n config.discovery_horizon_days,\n )\n stored_window_values = (\n stored_config.get(\"validation_fraction\", 0.10),\n stored_config.get(\"max_train_targets_per_user\", 0),\n stored_config.get(\"max_validation_targets_per_user\", 0),\n )\n current_window_values = (\n config.validation_fraction,\n config.max_train_targets_per_user,\n config.max_validation_targets_per_user,\n )\n objective_changed = (\n stored_filter_scope != config.negative_filter_scope\n or stored_target_weighting != config.target_weighting\n or stored_weight_values != current_weight_values\n or stored_window_values != current_window_values\n )\n model.load_state_dict(payload[\"model_state\"])\n if \"optimizer_state\" in payload and not objective_changed:\n optimizer.load_state_dict(payload[\"optimizer_state\"])\n for group in optimizer.param_groups:\n group[\"lr\"] = config.learning_rate\n group[\"weight_decay\"] = config.weight_decay\n log(f\"restored optimizer state from {resume_checkpoint}\")\n else:\n log(\n f\"using a fresh AdamW optimizer after loading {resume_checkpoint}\"\n )\n if \"generator_state\" in payload and not objective_changed:\n generator.set_state(payload[\"generator_state\"])\n start_epoch = int(payload[\"epoch\"])\n if start_epoch >= config.epochs:\n raise RuntimeError(\n f\"resume epoch {start_epoch} must be below --epochs {config.epochs}\"\n )\n prior = {\n str(name): float(value)\n for name, value in payload.get(\"diagnostics\", {}).items()\n }\n if objective_changed:\n prior.pop(\"validation_loss\", None)\n log(\n \"BCE objective changed: filter \"\n f\"{stored_filter_scope}->{config.negative_filter_scope}, \"\n \"target weighting \"\n f\"{stored_target_weighting}->{config.target_weighting}, \"\n f\"target window {stored_window_values}->{current_window_values}; \"\n \"recomputing the resume checkpoint's validation BCE\"\n )\n if \"validation_loss\" not in prior:\n prior.update(\n evaluate_validation_bce(\n model,\n prepared,\n config,\n target_weights,\n partition.window_starts,\n training_counts,\n validation_counts,\n item_correction,\n known_positive_packed,\n device,\n )\n )\n history.append(prior)\n best_loss = float(prior[\"validation_loss\"])\n best_epoch = start_epoch\n best_state = snapshot_model(model)\n log(\n f\"resumed validation-BCE training: epoch={start_epoch} \"\n f\"loss={best_loss:.6f}\"\n )\n\n for epoch in range(start_epoch + 1, config.epochs + 1):\n diagnostics = run_training_epoch(\n model,\n optimizer,\n prepared,\n config,\n target_weights,\n partition.window_starts,\n training_counts,\n item_correction,\n known_positive_packed,\n generator,\n device,\n epoch,\n )\n validation = evaluate_validation_bce(\n model,\n prepared,\n config,\n target_weights,\n partition.window_starts,\n training_counts,\n validation_counts,\n item_correction,\n known_positive_packed,\n device,\n )\n diagnostics.update(validation)\n history.append(diagnostics)\n current_state = snapshot_model(model)\n if checkpoint_dir is not None:\n save_epoch_checkpoint(\n checkpoint_dir,\n epoch,\n current_state,\n config,\n diagnostics,\n optimizer,\n generator,\n )\n improved = (\n diagnostics[\"validation_loss\"]\n < best_loss - config.early_stopping_min_delta\n )\n if improved:\n best_loss = diagnostics[\"validation_loss\"]\n best_epoch = epoch\n best_state = current_state\n epochs_without_improvement = 0\n if checkpoint_dir is not None:\n torch.save(\n {\n \"epoch\": epoch,\n \"config\": asdict(config),\n \"diagnostics\": diagnostics,\n \"model_state\": current_state,\n \"optimizer_state\": optimizer.state_dict(),\n \"generator_state\": generator.get_state(),\n },\n checkpoint_dir / \"best.pt\",\n )\n else:\n epochs_without_improvement += 1\n log(\n f\"epoch={epoch}/{config.epochs} train_loss={diagnostics['loss']:.5f} \"\n f\"validation_loss={diagnostics['validation_loss']:.5f} \"\n f\"pair_accuracy={diagnostics['pair_accuracy']:.3f} \"\n f\"train_targets={int(diagnostics['targets']):,} \"\n f\"validation_targets={int(diagnostics['validation_targets']):,} \"\n f\"train_seconds={diagnostics['seconds']:.1f} \"\n f\"validation_seconds={diagnostics['validation_seconds']:.1f}\"\n )\n release_accelerator_cache(device)\n if epochs_without_improvement >= config.early_stopping_patience:\n log(\n f\"early stopping after epoch={epoch}; \"\n f\"best_epoch={best_epoch} best_validation_loss={best_loss:.6f}\"\n )\n break\n\n if best_state is None:\n raise RuntimeError(\"training did not produce a valid checkpoint\")\n model.load_state_dict(best_state)\n model.to(device).eval()\n log(\n f\"restored best validation-BCE checkpoint: epoch={best_epoch} \"\n f\"loss={best_loss:.6f}\"\n )\n return model, history\n\n\n@torch.inference_mode()\ndef inference_queries(\n model: base.SequentialTwoTower,\n prepared: SequenceData,\n config: Config,\n device: torch.device,\n) -> torch.Tensor:\n sequence_rows = prepared.inference_user_rows\n lengths = np.where(\n sequence_rows >= 0, prepared.lengths[np.maximum(sequence_rows, 0)], 0\n sequence_rows >= 0, prepared.lengths[np.maximum(sequence_rows, 0)], 0\n )\n ordered = np.argsort(lengths, kind=\"stable\")\n result = torch.zeros(\n len(sequence_rows), config.embedding_dim, dtype=torch.float32\n )\n for group_start in range(0, len(ordered), config.user_batch_size):\n output_rows = ordered[group_start : group_start + config.user_batch_size]\n rows = sequence_rows[output_rows]\n group_lengths = lengths[output_rows]\n hidden = torch.zeros(\n 1, len(output_rows), config.embedding_dim, device=device\n )\n for start in range(0, int(group_lengths.max(initial=0)), config.bptt_steps):\n steps = min(\n config.bptt_steps,\n int(group_lengths.max(initial=0)) - start,\n )\n items = np.full(\n (len(output_rows), steps),\n len(prepared.item_ids),\n dtype=np.uint16,\n )\n features = np.zeros((len(output_rows), steps, 3), dtype=np.float16)\n counts = np.zeros(len(output_rows), dtype=np.int32)\n for local_row, sequence_row in enumerate(rows):\n if sequence_row < 0:\n continue\n count = min(steps, max(int(group_lengths[local_row]) - start, 0))\n if not count:\n continue\n offset = int(prepared.offsets[sequence_row])\n selected = slice(offset + start, offset + start + count)\n items[local_row, :count] = prepared.sequence_items[selected]\n features[local_row, :count] = prepared.event_features[selected]\n counts[local_row] = count\n outputs, _ = event_sequence(\n model,\n torch.from_numpy(items.astype(np.int64)).to(device),\n torch.from_numpy(features.astype(np.float32)).to(device),\n hidden,\n )\n next_rows: list[torch.Tensor] = []\n for local_row, count in enumerate(counts):\n next_rows.append(\n outputs[local_row, count - 1]\n if count\n else hidden[0, local_row]\n )\n hidden = torch.stack(next_rows).unsqueeze(0)\n\n liked_items = torch.from_numpy(\n prepared.inference_liked_items[output_rows].astype(np.int64)\n ).to(device)\n disliked_items = torch.from_numpy(\n prepared.inference_disliked_items[output_rows].astype(np.int64)\n ).to(device)\n queries = user_queries(\n model,\n hidden.squeeze(0),\n model._mean_context_channel(liked_items),\n model._mean_context_channel(disliked_items),\n torch.from_numpy(\n prepared.inference_user_features[output_rows].astype(np.float32)\n ).to(device),\n )\n result[torch.from_numpy(output_rows.astype(np.int64))] = queries.cpu()\n return result\n\n\n@torch.inference_mode()\ndef rank_candidates(\n model: base.SequentialTwoTower,\n prepared: SequenceData,\n eligibility: CandidateSet,\n config: Config,\n device: torch.device,\n ranking_batch_size: int,\n) -> tuple[pd.DataFrame, dict[str, float]]:\n model.eval()\n queries = inference_queries(model, prepared, config, device)\n item_parts: list[torch.Tensor] = []\n for start in range(0, len(prepared.item_ids), 2_048):\n rows = torch.arange(\n start,\n min(start + 2_048, len(prepared.item_ids)),\n dtype=torch.long,\n device=device,\n )\n item_parts.append(model.encode_items(rows).cpu())\n items = torch.cat(item_parts)\n rows: list[tuple[int, int, int]] = []\n for start in range(0, len(eligibility.uids), ranking_batch_size):\n end = min(start + ranking_batch_size, len(eligibility.uids))\n scores = queries[start:end] @ items.T\n eligible = np.unpackbits(\n eligibility.packed[start:end],\n axis=1,\n count=len(prepared.item_ids),\n bitorder=\"little\",\n ).astype(bool, copy=False)\n scores.masked_fill_(~torch.from_numpy(eligible), -torch.inf)\n selected = torch.topk(scores, k=base.TOP_N, dim=1).indices.numpy()\n for local_row, columns in enumerate(selected):\n uid = int(eligibility.uids[start + local_row])\n rows.extend(\n (uid, int(prepared.item_ids[column]), rank)\n for rank, column in enumerate(columns, start=1)\n )\n if end == len(eligibility.uids) or end % 1_024 == 0:\n log(f\"ranked users={end:,}/{len(eligibility.uids):,}\")\n query_norm = torch.linalg.vector_norm(queries, dim=1).numpy()\n item_norm = torch.linalg.vector_norm(items, dim=1).numpy()\n diagnostics = {\n \"query_norm_mean\": float(query_norm.mean()),\n \"query_norm_std\": float(query_norm.std()),\n \"item_norm_mean\": float(item_norm.mean()),\n \"item_norm_std\": float(item_norm.std()),\n }\n return pd.DataFrame(rows, columns=[\"uid\", \"item_id\", \"rank\"]), diagnostics\n\n\ndef write_ranking(frame: pd.DataFrame, output: Path) -> None:\n output.parent.mkdir(parents=True, exist_ok=True)\n if output.suffix == \".parquet\":\n frame.to_parquet(output, index=False, compression=\"zstd\")\n elif output.suffix == \".csv\":\n frame.to_csv(output, index=False)\n else:\n raise ValueError(\"--output must end in .parquet or .csv\")\n\n\ndef main() -> int:\n parser = argparse.ArgumentParser(description=__doc__)\n parser.add_argument(\n \"--stage\", choices=(\"validation\", \"submission\"), default=\"validation\"\n )\n parser.add_argument(\"--output\", type=Path, required=True)\n parser.add_argument(\"--public-dir\", type=Path, required=True)\n parser.add_argument(\"--prepared-cache\", type=Path)\n parser.add_argument(\"--device\", choices=(\"cpu\", \"mps\"), default=\"cpu\")\n parser.add_argument(\"--threads\", type=int, default=8)\n parser.add_argument(\"--bptt-steps\", type=int, default=Config.bptt_steps)\n parser.add_argument(\n \"--user-batch-size\", type=int, default=Config.user_batch_size\n )\n parser.add_argument(\"--epochs\", type=int, default=Config.epochs)\n parser.add_argument(\"--learning-rate\", type=float, default=Config.learning_rate)\n parser.add_argument(\n \"--loss-microbatch-size\",\n type=int,\n default=Config.loss_microbatch_size,\n )\n parser.add_argument(\n \"--negative-filter-scope\",\n choices=(\"full-training-history\", \"last-100\"),\n default=Config.negative_filter_scope,\n )\n parser.add_argument(\n \"--target-weighting\",\n choices=(\"discovery-graded\", \"organic-strong\", \"uniform\"),\n default=Config.target_weighting,\n )\n parser.add_argument(\n \"--ordinary-target-weight\",\n type=float,\n default=Config.ordinary_target_weight,\n )\n parser.add_argument(\n \"--organic-strong-target-weight\",\n type=float,\n default=Config.organic_strong_target_weight,\n )\n parser.add_argument(\n \"--organic-like-target-weight\",\n type=float,\n default=Config.organic_like_target_weight,\n )\n parser.add_argument(\n \"--discovery-horizon-days\",\n type=int,\n default=Config.discovery_horizon_days,\n )\n parser.add_argument(\n \"--validation-fraction\",\n type=float,\n default=Config.validation_fraction,\n )\n parser.add_argument(\n \"--max-train-targets-per-user\",\n type=int,\n default=Config.max_train_targets_per_user,\n help=\"retain this many recent pre-validation targets per user; 0 is uncapped\",\n )\n parser.add_argument(\n \"--max-validation-targets-per-user\",\n type=int,\n default=Config.max_validation_targets_per_user,\n help=\"retain this many final validation targets per user; 0 is uncapped\",\n )\n parser.add_argument(\n \"--early-stopping-patience\",\n type=int,\n default=Config.early_stopping_patience,\n )\n parser.add_argument(\n \"--early-stopping-min-delta\",\n type=float,\n default=Config.early_stopping_min_delta,\n )\n parser.add_argument(\"--checkpoint-dir\", type=Path)\n parser.add_argument(\"--resume-checkpoint\", type=Path)\n parser.add_argument(\"--seed\", type=int, default=Config.seed)\n parser.add_argument(\"--ranking-batch-size\", type=int, default=64)\n parser.add_argument(\"--prepare-only\", action=\"store_true\")\n args = parser.parse_args()\n\n if args.threads < 1 or args.bptt_steps < 2 or args.user_batch_size < 1:\n parser.error(\"threads, BPTT steps, and user batch size must be positive\")\n if (\n args.epochs < 1\n or args.learning_rate <= 0\n or args.loss_microbatch_size < 1\n or args.early_stopping_patience < 1\n or args.early_stopping_min_delta < 0\n or args.ordinary_target_weight <= 0\n or args.organic_strong_target_weight <= 0\n or args.organic_like_target_weight <= 0\n or args.discovery_horizon_days < 1\n or args.max_train_targets_per_user < 0\n or args.max_validation_targets_per_user < 0\n ):\n parser.error(\"training counts, learning rate, and patience are invalid\")\n if not 0.0 < args.validation_fraction < 1.0:\n parser.error(\"validation fraction must be strictly between zero and one\")\n public = args.public_dir.resolve()\n if not (public / \"data\" / \"candidate_catalog.parquet\").is_file():\n parser.error(f\"public task data not found under {public}\")\n config = Config(\n bptt_steps=args.bptt_steps,\n user_batch_size=args.user_batch_size,\n epochs=args.epochs,\n learning_rate=args.learning_rate,\n loss_microbatch_size=args.loss_microbatch_size,\n negative_filter_scope=args.negative_filter_scope,\n target_weighting=args.target_weighting,\n ordinary_target_weight=args.ordinary_target_weight,\n organic_strong_target_weight=args.organic_strong_target_weight,\n organic_like_target_weight=args.organic_like_target_weight,\n discovery_horizon_days=args.discovery_horizon_days,\n validation_fraction=args.validation_fraction,\n max_train_targets_per_user=args.max_train_targets_per_user,\n max_validation_targets_per_user=args.max_validation_targets_per_user,\n early_stopping_patience=args.early_stopping_patience,\n early_stopping_min_delta=args.early_stopping_min_delta,\n seed=args.seed,\n )\n torch.set_num_threads(args.threads)\n torch.set_num_interop_threads(1)\n seed_everything(config.seed)\n device = torch.device(args.device)\n if device.type == \"mps\" and not torch.backends.mps.is_available():\n parser.error(\"MPS was requested but is unavailable\")\n\n started = time.monotonic()\n prepared, eligibility = load_or_prepare(\n public,\n args.stage,\n config,\n args.prepared_cache.resolve() if args.prepared_cache else None,\n )\n target_partition = chronological_target_partition(\n prepared,\n config.validation_fraction,\n config.max_train_targets_per_user,\n config.max_validation_targets_per_user,\n )\n if args.prepare_only:\n print(\n json.dumps(\n {\n \"stage\": args.stage,\n \"sequence_users\": len(prepared.user_ids),\n \"sequence_tokens\": len(prepared.sequence_items),\n \"next_item_targets\": prepared.target_count,\n \"selected_training_targets\": int(\n target_partition.training_counts.sum()\n ),\n \"selected_validation_targets\": int(\n target_partition.validation_counts.sum()\n ),\n \"discarded_old_targets\": int(\n target_partition.window_starts.sum()\n ),\n \"target_users\": len(eligibility.uids),\n \"runtime_seconds\": round(time.monotonic() - started, 3),\n \"config\": asdict(config),\n },\n indent=2,\n )\n )\n return 0\n\n model, training_history = train_model(\n public,\n args.stage,\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 11971 }, "formatted_output": "Warning: truncated output (original token count: 12627)\nTotal output lines: 1338\n\n )\n\n\ndef event_sequence(\n model: base.SequentialTwoTower,\n items: torch.Tensor,\n features: torch.Tensor,\n hidden: torch.Tensor,\n) -> tuple[torch.Tensor, torch.Tensor]:\n inputs = torch.cat([model.context_embedding(items), features], dim=-1)\n projected = model.event_projection(inputs)\n return model.sequence_encoder(projected, hidden)\n\n\ndef pool_explicit_states(\n model: base.SequentialTwoTower,\n prepared: SequenceData,\n state_ids: np.ndarray,\n device: torch.device,\n) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:\n unique_states, inverse = np.unique(state_ids, return_inverse=True)\n liked_items = torch.from_numpy(\n prepared.liked_states[unique_states].astype(np.int64)\n ).to(device)\n disliked_items = torch.from_numpy(\n prepared.disliked_states[unique_states].astype(np.int64)\n ).to(device)\n liked_vectors = model._mean_context_channel(liked_items)\n disliked_vectors = model._mean_context_channel(disliked_items)\n inverse_rows = torch.from_numpy(inverse.astype(np.int64)).to(device)\n counts = prepared.state_counts[state_ids]\n return (\n liked_vectors[inverse_rows],\n disliked_vectors[inverse_rows],\n torch.from_numpy(counts.astype(np.float32)).to(device),\n )\n\n\ndef user_queries(\n model: base.SequentialTwoTower,\n sequence_vectors: torch.Tensor,\n liked_vectors: torch.Tensor,\n disliked_vectors: torch.Tensor,\n user_features: torch.Tensor,\n) -> torch.Tensor:\n return model.user_tower(\n torch.cat(\n [sequence_vectors, liked_vectors, disliked_vectors, user_features],\n dim=1,\n )\n )\n\n\ndef candidate_vectors(\n model: base.SequentialTwoTower,\n positives: torch.Tensor,\n negatives: torch.Tensor,\n) -> tuple[torch.Tensor, torch.Tensor]:\n all_items = torch.cat([positives, negatives.reshape(-1)])\n unique_items, inverse = torch.unique(all_items, return_inverse=True)\n encoded = model.encode_items(unique_items)\n positive_vectors = encoded[inverse[: len(positives)]]\n negative_vectors = encoded[inverse[len(positives) :]].reshape(\n len(positives), negatives.shape[1], -1\n )\n return positive_vectors, negative_vectors\n\n\ndef history_windows(\n sequence: np.ndarray,\n start: int,\n count: int,\n width: int,\n padding_item: int,\n) -> np.ndarray:\n base_start = max(0, start - width + 1)\n segment = sequence[base_start : start + count]\n padded = np.concatenate(\n [np.full(width - 1, padding_item, dtype=np.uint16), segment]\n )\n windows = np.lib.stride_tricks.sliding_window_view(padded, width)\n offset = start - base_start\n return windows[offset : offset + count].copy()\n\n\ndef corrected_negative_loss(\n logits: torch.Tensor,\n correction: torch.Tensor,\n config: Config,\n) -> torch.Tensor:\n raw = F.softplus(logits)\n total = config.uniform_negatives + config.inbatch_negatives\n result = torch.zeros(len(logits), device=logits.device)\n if config.uniform_negatives:\n result += config.uniform_negatives * raw[\n :, : config.uniform_negatives\n ].mean(dim=1)\n if config.inbatch_negatives:\n selected = slice(config.uniform_negatives, total)\n weights = correction[:, selected]\n result += config.inbatch_negatives * (\n (raw[:, selected] * weights).sum(dim=1)\n / weights.sum(dim=1).clamp_min(1.0e-6)\n )\n return result / total\n\n\ndef retained_target_window_starts(\n total_targets: np.ndarray,\n validation_counts: np.ndarray,\n training_counts: np.ndarray,\n) -> np.ndarray:\n \"\"\"Return the discarded prefix before the retained chronological suffix.\"\"\"\n training_region_ends = total_targets - validation_counts\n window_starts = training_region_ends - training_counts\n if (\n np.any(window_starts < 0)\n or np.any(training_counts < 0)\n or np.any(validation_counts < 0)\n ):\n raise RuntimeError(\"chronological target partition produced negative counts\")\n if not np.array_equal(\n window_starts + training_counts + validation_counts, total_targets\n ):\n raise RuntimeError(\"chronological target window is not a final contiguous suffix\")\n return window_starts\n\n\ndef chronological_target_partition(\n prepared: SequenceData,\n validation_fraction: float,\n max_train_targets_per_user: int,\n max_validation_targets_per_user: int,\n) -> TargetPartition:\n \"\"\"Select a recent contiguous window with a final out-of-time BCE suffix.\"\"\"\n totals = np.maximum(prepared.lengths - 1, 0).astype(np.int64)\n validation = np.floor(totals * validation_fraction).astype(np.int64)\n validation[(totals >= 2) & (validation == 0)] = 1\n if max_validation_targets_per_user:\n validation = np.minimum(validation, max_validation_targets_per_user)\n\n history_training = totals - validation\n training = history_training.copy()\n if max_train_targets_per_user:\n training = np.minimum(training, max_train_targets_per_user)\n window_starts = retained_target_window_starts(totals, validation, training)\n return TargetPartition(\n window_starts=window_starts,\n training_counts=training,\n validation_counts=validation,\n history_training_counts=history_training,\n )\n\n\ndef load_organic_like_outcomes(\n public: Path, stage: str, item_ids: np.ndarray\n) -> dict[int, dict[int, np.ndarray]]:\n \"\"\"Load mapped organic-like timestamps from public fitting history.\"\"\"\n prefixes = (\"train\",) if stage == \"validation\" else (\"train\", \"val\")\n frames = [\n pd.read_parquet(\n public / \"data\" / f\"{prefix}_likes.parquet\",\n columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\"],\n )\n for prefix in prefixes\n ]\n likes = pd.concat(frames, ignore_index=True)\n likes = likes[likes.is_organic == 1].copy()\n mapped = pd.Index(item_ids).get_indexer(\n likes.item_id.to_numpy(dtype=np.int64)\n )\n likes = likes.loc[mapped >= 0, [\"uid\", \"timestamp\"]].copy()\n likes[\"item\"] = mapped[mapped >= 0].astype(np.int32, copy=False)\n likes = likes.sort_values([\"uid\", \"item\", \"timestamp\"], kind=\"stable\")\n nested: dict[int, dict[int, list[int]]] = {}\n for raw_uid, raw_timestamp, raw_item in likes.itertuples(\n index=False, name=None\n ):\n nested.setdefault(int(raw_uid), {}).setdefault(int(raw_item), []).append(\n int(raw_timestamp)\n )\n result = {\n uid: {\n item: np.asarray(timestamps, dtype=np.int64)\n for item, timestamps in items.items()\n }\n for uid, items in nested.items()\n }\n log(\n f\"loaded organic-like outcomes: rows={len(likes):,} \"\n f\"users={len(result):,}\"\n )\n return result\n\n\ndef build_target_weights(\n public: Path,\n stage: str,\n prepared: SequenceData,\n training_counts: np.ndarray,\n config: Config,\n) -> np.ndarray:\n \"\"\"Build nonzero per-token weights without dropping adjacent targets.\"\"\"\n if config.target_weighting == \"uniform\":\n weights = np.ones(len(prepared.sequence_items), dtype=np.float32)\n else:\n weights = np.full(\n len(prepared.sequence_items),\n config.ordinary_target_weight,\n dtype=np.float32,\n )\n organic_strong = (\n (prepared.event_features[:, 0] > 0.5)\n & (prepared.event_features[:, 1] >= 0.8)\n )\n weights[organic_strong] = config.organic_strong_target_weight\n if config.target_weighting == \"discovery-graded\":\n like_outcomes = load_organic_like_outcomes(\n public, stage, prepared.item_ids\n )\n history_end = (\n base.TRAIN_END if stage == \"validation\" else base.VALIDATION_END\n )\n horizon = config.discovery_horizon_days * base.DAY\n grade_three = 0\n for user_row, uid in enumerate(prepared.user_ids):\n start = int(prepared.offsets[user_row])\n end = int(prepared.offsets[user_row + 1])\n if end - start < 2:\n continue\n last_training_target = start + int(training_counts[user_row])\n first_validation_target = last_training_target + 1\n split_timestamp = (\n int(prepared.sequence_timestamps[first_validation_target])\n if first_validation_target < end\n else history_end\n )\n user_likes = like_outcomes.get(int(uid), {})\n seen = {int(prepared.sequence_items[start])}\n for position in range(start + 1, end):\n item = int(prepared.sequence_items[position])\n novel = item not in seen\n seen.add(item)\n if not novel:\n # Repeats remain training examples, but they cannot be\n # eligible discovery outcomes at the corresponding\n # cutoff and therefore keep only the ordinary weight.\n weights[position] = config.ordinary_target_weight\n continue\n timestamps = user_likes.get(item)\n if timestamps is None:\n continue\n timestamp = int(prepared.sequence_timestamps[position])\n upper = min(\n timestamp + horizon,\n split_timestamp\n if position <= last_training_target\n else history_end,\n )\n outcome = int(np.searchsorted(timestamps, timestamp, side=\"left\"))\n if outcome < len(timestamps) and int(timestamps[outcome]) < upper:\n weights[position] = config.organic_like_target_weight\n grade_three += 1\n log(\n f\"grade-3 first-encounter targets={grade_three:,} \"\n f\"horizon_days={config.discovery_horizon_days}\"\n )\n log(\n f\"target weighting={config.target_weighting} \"\n f\"min={weights.min():.3f} mean={weights.mean():.3f} \"\n f\"max={weights.max():.3f}\"\n )\n return weights\n\n\ndef build_dense_block(\n prepared: SequenceData,\n target_weights: np.ndarray,\n users: np.ndarray,\n window_starts: np.ndarray,\n process_counts: np.ndarray,\n score_starts: np.ndarray,\n start: int,\n steps: int,\n config: Config,\n) -> tuple[np.ndarray, ...]:\n \"\"\"Build one causal sequence block and mark the target rows to score.\"\"\"\n batch_size = len(users)\n padding_item = len(prepared.item_ids)\n input_items = np.full((batch_size, steps), padding_item, dtype=np.uint16)\n event_features = np.zeros((batch_size, steps, 3), dtype=np.float16)\n activity_features = np.zeros((batch_size, steps, 4), dtype=np.float16)\n state_ids = np.zeros((batch_size, steps), dtype=np.uint32)\n targets = np.zeros((batch_size, steps), dtype=np.uint16)\n weights = np.ones((batch_size, steps), dtype=np.float32)\n scored = np.zeros((batch_size, steps), dtype=bool)\n histories = np.full(\n (batch_size, steps, config.bptt_steps),\n padding_item,\n dtype=np.uint16,\n )\n processed = np.zeros(batch_size, dtype=np.int32)\n\n lengths = prepared.lengths\n for row, user_row in enumerate(users):\n count = min(steps, max(int(process_counts[row]) - start, 0))\n if not count:\n continue\n offset = int(prepared.offsets[user_row])\n window_start = int(window_starts[row])\n absolute_start = window_start + start\n positions = slice(\n offset + absolute_start,\n offset + absolute_start + count,\n )\n target_positions = slice(\n offset + absolute_start + 1,\n offset + absolute_start + count + 1,\n )\n input_items[row, :count] = prepared.sequence_items[positions]\n event_features[row, :count] = prepared.event_features[positions]\n activity_features[row, :count] = prepared.activity_features[positions]\n state_ids[row, :count] = prepared.state_ids[positions]\n targets[row, :count] = prepared.sequence_items[target_positions]\n weights[row, :count] = target_weights[target_positions]\n score_from = min(count, max(int(score_starts[row]) - start, 0))\n scored[row, score_from:count] = True\n processed[row] = count\n user_sequence = prepared.sequence_items[\n offset : offset + int(lengths[user_row])\n ]\n histories[row, :count] = history_windows(\n user_sequence,\n absolute_start,\n count,\n config.bptt_steps,\n padding_item,\n )\n return (\n input_items,\n event_features,\n activity_features,\n state_ids,\n targets,\n scored,\n histories,\n processed,\n weights,\n )\n\n\ndef score_target_microbatches(\n model: base.SequentialTwoTower,\n prepared: SequenceData,\n config: Config,\n sequence_vectors: torch.Tensor,\n flat_states: np.ndarray,\n flat_activity: np.ndarray,\n positive_items: torch.Tensor,\n negatives: torch.Tensor,\n negative_weights: torch.Tensor,\n sample_weights: np.ndarray,\n device: torch.device,\n backward: bool,\n) -> tuple[dict[str, float], torch.Tensor | None]:\n \"\"\"Compute one logical block's exact BCE in bounded-memory slices.\"\"\"\n target_count = len(positive_items)\n total_weight = float(sample_weights.sum())\n if total_weight <= 0:\n raise RuntimeError(\"target block has no positive sample weight\")\n sequence_gradient = torch.zeros_like(sequence_vectors) if backward else None\n statistics = {\n \"loss_sum\": 0.0,\n \"positive_sum\": 0.0,\n \"negative_sum\": 0.0,\n \"pair_correct\": 0.0,\n \"weight_sum\": 0.0,\n }\n for begin in range(0, target_count, config.loss_microbatch_size):\n end = min(begin + config.loss_microbatch_size, target_count)\n selected_states = flat_states[begin:end]\n if backward:\n sequence_input = (\n sequence_vectors[begin:end].detach().requires_grad_(True)\n )\n else:\n sequence_input = sequence_vectors[begin:end]\n liked_vectors, disliked_vectors, _ = pool_explicit_states(\n model, prepared, selected_states, device\n )\n counts = prepared.state_counts[selected_states]\n numeric = np.zeros((end - begin, 6), dtype=np.float32)\n numeric[:, :4] = flat_activity[begin:end]\n numeric[:, 4] = np.log1p(counts[:, 0]) / 6.0\n numeric[:, 5] = np.log1p(counts[:, 1]) / 4.0\n query_vectors = user_queries(\n model,\n sequence_input,\n liked_vectors,\n disliked_vectors,\n torch.from_numpy(numeric).to(device),\n )\n positive_vectors, negative_vectors = candidate_vectors(\n model,\n positive_items[begin:end],\n negatives[begin:end],\n )\n positive_logits = (query_vectors * positive_vectors).sum(dim=1)\n negative_logits = torch.einsum(\n \"bd,bkd->bk\", query_vectors, negative_vectors\n )\n positive_loss = F.softplus(-positive_logits)\n negative_loss = corrected_negative_loss(\n negative_logits, negative_weights[begin:end], config\n )\n selected_weights = torch.from_numpy(\n sample_weights[begin:end].astype(np.float32, copy=False)\n ).to(device)\n loss_sum = (\n selected_weights * (positive_loss + negative_loss) / 2.0\n ).sum()\n if not bool(torch.isfinite(loss_sum)):\n raise RuntimeError(\"dense next-item training produced non-finite loss\")\n if backward:\n (loss_sum / total_weight).backward()\n if sequence_input.grad is None:\n raise RuntimeError(\"sequence microbatch did not produce a gradient\")\n sequence_gradient[begin:end] = sequence_input.grad\n statistics[\"loss_sum\"] += float(loss_sum.detach().cpu())\n statistics[\"positive_sum\"] += float(positive_logits.detach().sum().cpu())\n statistics[\"negative_sum\"] += float(negative_logits.detach().sum().cpu())\n statistics[\"pair_correct\"] += float(\n (positive_logits.detach()[:, None] > negative_logits.detach()).sum().cpu()\n )\n statistics[\"weight_sum\"] += float(selected_weights.sum().detach().cpu())\n return statistics, sequence_gradient\n\n\ndef release_accelerator_cache(device: torch.device) -> None:\n if device.type == \"mps\":\n torch.mps.empty_cache()\n\n\ndef run_training_epoch(\n model: base.SequentialTwoTower,\n optimizer: torch.optim.Optimizer,\n prepared: SequenceData,\n config: Config,\n target_weights: np.ndarray,\n window_starts: np.ndarray,\n target_counts: np.ndarray,\n item_correction: torch.Tensor,\n known_positive_packed: np.ndarray | None,\n generator: torch.Generator,\n device: torch.device,\n epoch: int,\n) -> dict[str, float]:\n model.train()\n train_users = np.flatnonzero(target_counts > 0)\n sorted_users = train_users[\n np.argsort(target_counts[train_users], kind=\"stable\")\n ]\n groups = [\n sorted_users[start : start + config.user_batch_size]\n for start in range(0, len(sorted_users), config.user_batch_size)\n ]\n ]\n group_order = torch.randperm(len(groups), generator=generator).numpy()\n negative_count = config.uniform_negatives + config.inbatch_negatives\n total_loss = 0.0\n total_targets = 0\n total_weight = 0.0\n total_positive = 0.0\n total_negative = 0.0\n total_pair_correct = 0.0\n batches = 0\n started = time.monotonic()\n\n for group_number in group_order:\n users = groups[int(group_number)]\n group_counts = target_counts[users]\n hidden = torch.zeros(\n 1, len(users), config.embedding_dim, device=device\n )\n max_targets = int(group_counts.max())\n for start in range(0, max_targets, config.bptt_steps):\n steps = min(config.bptt_steps, max_targets - start)\n block = build_dense_block(\n prepared,\n target_weights,\n users,\n window_starts[users],\n group_counts,\n np.zeros(len(users), dtype=np.int64),\n start,\n steps,\n config,\n )\n sequence_outputs, _ = event_sequence(\n model,\n torch.from_numpy(block[0].astype(np.int64)).to(device),\n torch.from_numpy(block[1].astype(np.float32)).to(device),\n hidden,\n )\n hidden = torch.stack(\n [\n sequence_outputs[row, count - 1]\n if count\n else hidden[0, row]\n for row, count in enumerate(block[7])\n ]\n ).unsqueeze(0).detach()\n flat_scored = block[5].reshape(-1)\n if not np.any(flat_scored):\n continue\n mask = torch.from_numpy(flat_scored).to(device)\n sequence_vectors = sequence_outputs.reshape(\n -1, config.embedding_dim\n )[mask]\n flat_states = block[3].reshape(-1)[flat_scored]\n positive_items = torch.from_numpy(\n block[4].reshape(-1)[flat_scored].astype(np.int64)\n ).to(device)\n known_history = torch.from_numpy(\n block[6].reshape(-1, config.bptt_steps)[flat_scored].astype(\n np.int64\n )\n ).to(device)\n liked_items = torch.from_numpy(\n prepared.liked_states[flat_states].astype(np.int64)\n ).to(device)\n flat_user_rows = np.broadcast_to(\n users[:, None], block[5…2627 tokens truncated…dden_1\",\n \"user_hidden_2\",\n \"item_hidden_1\",\n \"item_hidden_2\",\n )\n mismatches = {\n name: (stored_config.get(name), getattr(config, name))\n for name in architecture_fields\n if stored_config.get(name) != getattr(config, name)\n }\n if mismatches:\n raise RuntimeError(\n f\"resume checkpoint architecture differs: {mismatches}\"\n )\n stored_filter_scope = stored_config.get(\n \"negative_filter_scope\", \"last-100\"\n )\n stored_target_weighting = stored_config.get(\n \"target_weighting\", \"uniform\"\n )\n stored_weight_values = (\n stored_config.get(\"ordinary_target_weight\", 1.0),\n stored_config.get(\"organic_strong_target_weight\", 1.0),\n stored_config.get(\"organic_like_target_weight\", 3.0),\n stored_config.get(\"discovery_horizon_days\", 7),\n )\n current_weight_values = (\n config.ordinary_target_weight,\n config.organic_strong_target_weight,\n config.organic_like_target_weight,\n config.discovery_horizon_days,\n )\n stored_window_values = (\n stored_config.get(\"validation_fraction\", 0.10),\n stored_config.get(\"max_train_targets_per_user\", 0),\n stored_config.get(\"max_validation_targets_per_user\", 0),\n )\n current_window_values = (\n config.validation_fraction,\n config.max_train_targets_per_user,\n config.max_validation_targets_per_user,\n )\n objective_changed = (\n stored_filter_scope != config.negative_filter_scope\n or stored_target_weighting != config.target_weighting\n or stored_weight_values != current_weight_values\n or stored_window_values != current_window_values\n )\n model.load_state_dict(payload[\"model_state\"])\n if \"optimizer_state\" in payload and not objective_changed:\n optimizer.load_state_dict(payload[\"optimizer_state\"])\n for group in optimizer.param_groups:\n group[\"lr\"] = config.learning_rate\n group[\"weight_decay\"] = config.weight_decay\n log(f\"restored optimizer state from {resume_checkpoint}\")\n else:\n log(\n f\"using a fresh AdamW optimizer after loading {resume_checkpoint}\"\n )\n if \"generator_state\" in payload and not objective_changed:\n generator.set_state(payload[\"generator_state\"])\n start_epoch = int(payload[\"epoch\"])\n if start_epoch >= config.epochs:\n raise RuntimeError(\n f\"resume epoch {start_epoch} must be below --epochs {config.epochs}\"\n )\n prior = {\n str(name): float(value)\n for name, value in payload.get(\"diagnostics\", {}).items()\n }\n if objective_changed:\n prior.pop(\"validation_loss\", None)\n log(\n \"BCE objective changed: filter \"\n f\"{stored_filter_scope}->{config.negative_filter_scope}, \"\n \"target weighting \"\n f\"{stored_target_weighting}->{config.target_weighting}, \"\n f\"target window {stored_window_values}->{current_window_values}; \"\n \"recomputing the resume checkpoint's validation BCE\"\n )\n if \"validation_loss\" not in prior:\n prior.update(\n evaluate_validation_bce(\n model,\n prepared,\n config,\n target_weights,\n partition.window_starts,\n training_counts,\n validation_counts,\n item_correction,\n known_positive_packed,\n device,\n )\n )\n history.append(prior)\n best_loss = float(prior[\"validation_loss\"])\n best_epoch = start_epoch\n best_state = snapshot_model(model)\n log(\n f\"resumed validation-BCE training: epoch={start_epoch} \"\n f\"loss={best_loss:.6f}\"\n )\n\n for epoch in range(start_epoch + 1, config.epochs + 1):\n diagnostics = run_training_epoch(\n model,\n optimizer,\n prepared,\n config,\n target_weights,\n partition.window_starts,\n training_counts,\n item_correction,\n known_positive_packed,\n generator,\n device,\n epoch,\n )\n validation = evaluate_validation_bce(\n model,\n prepared,\n config,\n target_weights,\n partition.window_starts,\n training_counts,\n validation_counts,\n item_correction,\n known_positive_packed,\n device,\n )\n diagnostics.update(validation)\n history.append(diagnostics)\n current_state = snapshot_model(model)\n if checkpoint_dir is not None:\n save_epoch_checkpoint(\n checkpoint_dir,\n epoch,\n current_state,\n config,\n diagnostics,\n optimizer,\n generator,\n )\n improved = (\n diagnostics[\"validation_loss\"]\n < best_loss - config.early_stopping_min_delta\n )\n if improved:\n best_loss = diagnostics[\"validation_loss\"]\n best_epoch = epoch\n best_state = current_state\n epochs_without_improvement = 0\n if checkpoint_dir is not None:\n torch.save(\n {\n \"epoch\": epoch,\n \"config\": asdict(config),\n \"diagnostics\": diagnostics,\n \"model_state\": current_state,\n \"optimizer_state\": optimizer.state_dict(),\n \"generator_state\": generator.get_state(),\n },\n checkpoint_dir / \"best.pt\",\n )\n else:\n epochs_without_improvement += 1\n log(\n f\"epoch={epoch}/{config.epochs} train_loss={diagnostics['loss']:.5f} \"\n f\"validation_loss={diagnostics['validation_loss']:.5f} \"\n f\"pair_accuracy={diagnostics['pair_accuracy']:.3f} \"\n f\"train_targets={int(diagnostics['targets']):,} \"\n f\"validation_targets={int(diagnostics['validation_targets']):,} \"\n f\"train_seconds={diagnostics['seconds']:.1f} \"\n f\"validation_seconds={diagnostics['validation_seconds']:.1f}\"\n )\n release_accelerator_cache(device)\n if epochs_without_improvement >= config.early_stopping_patience:\n log(\n f\"early stopping after epoch={epoch}; \"\n f\"best_epoch={best_epoch} best_validation_loss={best_loss:.6f}\"\n )\n break\n\n if best_state is None:\n raise RuntimeError(\"training did not produce a valid checkpoint\")\n model.load_state_dict(best_state)\n model.to(device).eval()\n log(\n f\"restored best validation-BCE checkpoint: epoch={best_epoch} \"\n f\"loss={best_loss:.6f}\"\n )\n return model, history\n\n\n@torch.inference_mode()\ndef inference_queries(\n model: base.SequentialTwoTower,\n prepared: SequenceData,\n config: Config,\n device: torch.device,\n) -> torch.Tensor:\n sequence_rows = prepared.inference_user_rows\n lengths = np.where(\n sequence_rows >= 0, prepared.lengths[np.maximum(sequence_rows, 0)], 0\n sequence_rows >= 0, prepared.lengths[np.maximum(sequence_rows, 0)], 0\n )\n ordered = np.argsort(lengths, kind=\"stable\")\n result = torch.zeros(\n len(sequence_rows), config.embedding_dim, dtype=torch.float32\n )\n for group_start in range(0, len(ordered), config.user_batch_size):\n output_rows = ordered[group_start : group_start + config.user_batch_size]\n rows = sequence_rows[output_rows]\n group_lengths = lengths[output_rows]\n hidden = torch.zeros(\n 1, len(output_rows), config.embedding_dim, device=device\n )\n for start in range(0, int(group_lengths.max(initial=0)), config.bptt_steps):\n steps = min(\n config.bptt_steps,\n int(group_lengths.max(initial=0)) - start,\n )\n items = np.full(\n (len(output_rows), steps),\n len(prepared.item_ids),\n dtype=np.uint16,\n )\n features = np.zeros((len(output_rows), steps, 3), dtype=np.float16)\n counts = np.zeros(len(output_rows), dtype=np.int32)\n for local_row, sequence_row in enumerate(rows):\n if sequence_row < 0:\n continue\n count = min(steps, max(int(group_lengths[local_row]) - start, 0))\n if not count:\n continue\n offset = int(prepared.offsets[sequence_row])\n selected = slice(offset + start, offset + start + count)\n items[local_row, :count] = prepared.sequence_items[selected]\n features[local_row, :count] = prepared.event_features[selected]\n counts[local_row] = count\n outputs, _ = event_sequence(\n model,\n torch.from_numpy(items.astype(np.int64)).to(device),\n torch.from_numpy(features.astype(np.float32)).to(device),\n hidden,\n )\n next_rows: list[torch.Tensor] = []\n for local_row, count in enumerate(counts):\n next_rows.append(\n outputs[local_row, count - 1]\n if count\n else hidden[0, local_row]\n )\n hidden = torch.stack(next_rows).unsqueeze(0)\n\n liked_items = torch.from_numpy(\n prepared.inference_liked_items[output_rows].astype(np.int64)\n ).to(device)\n disliked_items = torch.from_numpy(\n prepared.inference_disliked_items[output_rows].astype(np.int64)\n ).to(device)\n queries = user_queries(\n model,\n hidden.squeeze(0),\n model._mean_context_channel(liked_items),\n model._mean_context_channel(disliked_items),\n torch.from_numpy(\n prepared.inference_user_features[output_rows].astype(np.float32)\n ).to(device),\n )\n result[torch.from_numpy(output_rows.astype(np.int64))] = queries.cpu()\n return result\n\n\n@torch.inference_mode()\ndef rank_candidates(\n model: base.SequentialTwoTower,\n prepared: SequenceData,\n eligibility: CandidateSet,\n config: Config,\n device: torch.device,\n ranking_batch_size: int,\n) -> tuple[pd.DataFrame, dict[str, float]]:\n model.eval()\n queries = inference_queries(model, prepared, config, device)\n item_parts: list[torch.Tensor] = []\n for start in range(0, len(prepared.item_ids), 2_048):\n rows = torch.arange(\n start,\n min(start + 2_048, len(prepared.item_ids)),\n dtype=torch.long,\n device=device,\n )\n item_parts.append(model.encode_items(rows).cpu())\n items = torch.cat(item_parts)\n rows: list[tuple[int, int, int]] = []\n for start in range(0, len(eligibility.uids), ranking_batch_size):\n end = min(start + ranking_batch_size, len(eligibility.uids))\n scores = queries[start:end] @ items.T\n eligible = np.unpackbits(\n eligibility.packed[start:end],\n axis=1,\n count=len(prepared.item_ids),\n bitorder=\"little\",\n ).astype(bool, copy=False)\n scores.masked_fill_(~torch.from_numpy(eligible), -torch.inf)\n selected = torch.topk(scores, k=base.TOP_N, dim=1).indices.numpy()\n for local_row, columns in enumerate(selected):\n uid = int(eligibility.uids[start + local_row])\n rows.extend(\n (uid, int(prepared.item_ids[column]), rank)\n for rank, column in enumerate(columns, start=1)\n )\n if end == len(eligibility.uids) or end % 1_024 == 0:\n log(f\"ranked users={end:,}/{len(eligibility.uids):,}\")\n query_norm = torch.linalg.vector_norm(queries, dim=1).numpy()\n item_norm = torch.linalg.vector_norm(items, dim=1).numpy()\n diagnostics = {\n \"query_norm_mean\": float(query_norm.mean()),\n \"query_norm_std\": float(query_norm.std()),\n \"item_norm_mean\": float(item_norm.mean()),\n \"item_norm_std\": float(item_norm.std()),\n }\n return pd.DataFrame(rows, columns=[\"uid\", \"item_id\", \"rank\"]), diagnostics\n\n\ndef write_ranking(frame: pd.DataFrame, output: Path) -> None:\n output.parent.mkdir(parents=True, exist_ok=True)\n if output.suffix == \".parquet\":\n frame.to_parquet(output, index=False, compression=\"zstd\")\n elif output.suffix == \".csv\":\n frame.to_csv(output, index=False)\n else:\n raise ValueError(\"--output must end in .parquet or .csv\")\n\n\ndef main() -> int:\n parser = argparse.ArgumentParser(description=__doc__)\n parser.add_argument(\n \"--stage\", choices=(\"validation\", \"submission\"), default=\"validation\"\n )\n parser.add_argument(\"--output\", type=Path, required=True)\n parser.add_argument(\"--public-dir\", type=Path, required=True)\n parser.add_argument(\"--prepared-cache\", type=Path)\n parser.add_argument(\"--device\", choices=(\"cpu\", \"mps\"), default=\"cpu\")\n parser.add_argument(\"--threads\", type=int, default=8)\n parser.add_argument(\"--bptt-steps\", type=int, default=Config.bptt_steps)\n parser.add_argument(\n \"--user-batch-size\", type=int, default=Config.user_batch_size\n )\n parser.add_argument(\"--epochs\", type=int, default=Config.epochs)\n parser.add_argument(\"--learning-rate\", type=float, default=Config.learning_rate)\n parser.add_argument(\n \"--loss-microbatch-size\",\n type=int,\n default=Config.loss_microbatch_size,\n )\n parser.add_argument(\n \"--negative-filter-scope\",\n choices=(\"full-training-history\", \"last-100\"),\n default=Config.negative_filter_scope,\n )\n parser.add_argument(\n \"--target-weighting\",\n choices=(\"discovery-graded\", \"organic-strong\", \"uniform\"),\n default=Config.target_weighting,\n )\n parser.add_argument(\n \"--ordinary-target-weight\",\n type=float,\n default=Config.ordinary_target_weight,\n )\n parser.add_argument(\n \"--organic-strong-target-weight\",\n type=float,\n default=Config.organic_strong_target_weight,\n )\n parser.add_argument(\n \"--organic-like-target-weight\",\n type=float,\n default=Config.organic_like_target_weight,\n )\n parser.add_argument(\n \"--discovery-horizon-days\",\n type=int,\n default=Config.discovery_horizon_days,\n )\n parser.add_argument(\n \"--validation-fraction\",\n type=float,\n default=Config.validation_fraction,\n )\n parser.add_argument(\n \"--max-train-targets-per-user\",\n type=int,\n default=Config.max_train_targets_per_user,\n help=\"retain this many recent pre-validation targets per user; 0 is uncapped\",\n )\n parser.add_argument(\n \"--max-validation-targets-per-user\",\n type=int,\n default=Config.max_validation_targets_per_user,\n help=\"retain this many final validation targets per user; 0 is uncapped\",\n )\n parser.add_argument(\n \"--early-stopping-patience\",\n type=int,\n default=Config.early_stopping_patience,\n )\n parser.add_argument(\n \"--early-stopping-min-delta\",\n type=float,\n default=Config.early_stopping_min_delta,\n )\n parser.add_argument(\"--checkpoint-dir\", type=Path)\n parser.add_argument(\"--resume-checkpoint\", type=Path)\n parser.add_argument(\"--seed\", type=int, default=Config.seed)\n parser.add_argument(\"--ranking-batch-size\", type=int, default=64)\n parser.add_argument(\"--prepare-only\", action=\"store_true\")\n args = parser.parse_args()\n\n if args.threads < 1 or args.bptt_steps < 2 or args.user_batch_size < 1:\n parser.error(\"threads, BPTT steps, and user batch size must be positive\")\n if (\n args.epochs < 1\n or args.learning_rate <= 0\n or args.loss_microbatch_size < 1\n or args.early_stopping_patience < 1\n or args.early_stopping_min_delta < 0\n or args.ordinary_target_weight <= 0\n or args.organic_strong_target_weight <= 0\n or args.organic_like_target_weight <= 0\n or args.discovery_horizon_days < 1\n or args.max_train_targets_per_user < 0\n or args.max_validation_targets_per_user < 0\n ):\n parser.error(\"training counts, learning rate, and patience are invalid\")\n if not 0.0 < args.validation_fraction < 1.0:\n parser.error(\"validation fraction must be strictly between zero and one\")\n public = args.public_dir.resolve()\n if not (public / \"data\" / \"candidate_catalog.parquet\").is_file():\n parser.error(f\"public task data not found under {public}\")\n config = Config(\n bptt_steps=args.bptt_steps,\n user_batch_size=args.user_batch_size,\n epochs=args.epochs,\n learning_rate=args.learning_rate,\n loss_microbatch_size=args.loss_microbatch_size,\n negative_filter_scope=args.negative_filter_scope,\n target_weighting=args.target_weighting,\n ordinary_target_weight=args.ordinary_target_weight,\n organic_strong_target_weight=args.organic_strong_target_weight,\n organic_like_target_weight=args.organic_like_target_weight,\n discovery_horizon_days=args.discovery_horizon_days,\n validation_fraction=args.validation_fraction,\n max_train_targets_per_user=args.max_train_targets_per_user,\n max_validation_targets_per_user=args.max_validation_targets_per_user,\n early_stopping_patience=args.early_stopping_patience,\n early_stopping_min_delta=args.early_stopping_min_delta,\n seed=args.seed,\n )\n torch.set_num_threads(args.threads)\n torch.set_num_interop_threads(1)\n seed_everything(config.seed)\n device = torch.device(args.device)\n if device.type == \"mps\" and not torch.backends.mps.is_available():\n parser.error(\"MPS was requested but is unavailable\")\n\n started = time.monotonic()\n prepared, eligibility = load_or_prepare(\n public,\n args.stage,\n config,\n args.prepared_cache.resolve() if args.prepared_cache else None,\n )\n target_partition = chronological_target_partition(\n prepared,\n config.validation_fraction,\n config.max_train_targets_per_user,\n config.max_validation_targets_per_user,\n )\n if args.prepare_only:\n print(\n json.dumps(\n {\n \"stage\": args.stage,\n \"sequence_users\": len(prepared.user_ids),\n \"sequence_tokens\": len(prepared.sequence_items),\n \"next_item_targets\": prepared.target_count,\n \"selected_training_targets\": int(\n target_partition.training_counts.sum()\n ),\n \"selected_validation_targets\": int(\n target_partition.validation_counts.sum()\n ),\n \"discarded_old_targets\": int(\n target_partition.window_starts.sum()\n ),\n \"target_users\": len(eligibility.uids),\n \"runtime_seconds\": round(time.monotonic() - started, 3),\n \"config\": asdict(config),\n },\n indent=2,\n )\n )\n return 0\n\n model, training_history = train_model(\n public,\n args.stage,\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "session_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "root_turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "response_id": "resp_08539f19231b9e9f016ab5a8e81c8087d2aa2c950e43162ce0", "usage": { "input_tokens": 29179, "cached_input_tokens": 20224, "cache_write_input_tokens": 0, "output_tokens": 136, "reasoning_output_tokens": 31, "total_tokens": 29315 }, "turn_token_usage": { "input_tokens": 64460, "cached_input_tokens": 46080, "cache_write_input_tokens": 0, "output_tokens": 695, "reasoning_output_tokens": 220, "total_tokens": 65155 }, "thread_token_usage": { "input_tokens": 64460, "cached_input_tokens": 46080, "cache_write_input_tokens": 0, "output_tokens": 695, "reasoning_output_tokens": 220, "total_tokens": 65155 } } ## Step 10 · agent · 2026-09-24T22:49:17.564Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T22:49:18.114Z { "code": "const r = await tools.exec_command({\"cmd\":\"sed -n '2040,2115p' /app/solution/train_current_model.py\\nsed -n '1500,1788p' /app/solution/train_current_model.py\\nsed -n '1200,1510p' /app/solution/train_current_model.py\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":40000}); text(r.output);" } ### Tool output · 2026-09-24T22:49:18.162Z Script completed Wall time 0.0 seconds Output: validation_fraction=args.validation_fraction, max_train_targets_per_user=args.max_train_targets_per_user, max_validation_targets_per_user=args.max_validation_targets_per_user, early_stopping_patience=args.early_stopping_patience, early_stopping_min_delta=args.early_stopping_min_delta, seed=args.seed, ) torch.set_num_threads(args.threads) torch.set_num_interop_threads(1) seed_everything(config.seed) device = torch.device(args.device) if device.type == "mps" and not torch.backends.mps.is_available(): parser.error("MPS was requested but is unavailable") started = time.monotonic() prepared, eligibility = load_or_prepare( public, args.stage, config, args.prepared_cache.resolve() if args.prepared_cache else None, ) target_partition = chronological_target_partition( prepared, config.validation_fraction, config.max_train_targets_per_user, config.max_validation_targets_per_user, ) if args.prepare_only: print( json.dumps( { "stage": args.stage, "sequence_users": len(prepared.user_ids), "sequence_tokens": len(prepared.sequence_items), "next_item_targets": prepared.target_count, "selected_training_targets": int( target_partition.training_counts.sum() ), "selected_validation_targets": int( target_partition.validation_counts.sum() ), "discarded_old_targets": int( target_partition.window_starts.sum() ), "target_users": len(eligibility.uids), "runtime_seconds": round(time.monotonic() - started, 3), "config": asdict(config), }, indent=2, ) ) return 0 model, training_history = train_model( public, args.stage, prepared, config, device, args.checkpoint_dir.resolve() if args.checkpoint_dir else None, args.resume_checkpoint.resolve() if args.resume_checkpoint else None, ) ranking, embedding_diagnostics = rank_candidates( model, prepared, eligibility, config, device, args.ranking_batch_size, ) write_ranking(ranking, args.output.resolve()) print( json.dumps( { "baseline": "yambda-windowed-sequence-two-tower-base", "stage": args.stage, diagnostics: dict[str, float], optimizer: torch.optim.Optimizer, generator: torch.Generator, ) -> Path: checkpoint_dir.mkdir(parents=True, exist_ok=True) path = checkpoint_dir / f"epoch-{epoch:02d}.pt" torch.save( { "epoch": epoch, "config": asdict(config), "diagnostics": diagnostics, "model_state": model_state, "optimizer_state": optimizer.state_dict(), "generator_state": generator.get_state(), }, path, ) log(f"saved epoch checkpoint: {path}") return path def train_model( public: Path, stage: str, prepared: SequenceData, config: Config, device: torch.device, checkpoint_dir: Path | None, resume_checkpoint: Path | None, ) -> tuple[base.SequentialTwoTower, list[dict[str, float]]]: partition = chronological_target_partition( prepared, config.validation_fraction, config.max_train_targets_per_user, config.max_validation_targets_per_user, ) training_counts = partition.training_counts validation_counts = partition.validation_counts log( f"chronological BCE window: train={int(training_counts.sum()):,} " f"validation={int(validation_counts.sum()):,} " f"selected={partition.selected_count:,} " f"discarded_old_targets={int(partition.window_starts.sum()):,} " f"all={prepared.target_count:,}" ) model = base.SequentialTwoTower(prepared.item_features, model_config(config)).to( device ) optimizer = torch.optim.AdamW( model.parameters(), lr=config.learning_rate, weight_decay=config.weight_decay ) generator = torch.Generator(device="cpu").manual_seed(config.seed + 10_000) item_correction = build_item_correction( prepared, config, partition.window_starts, training_counts, ) target_weights = build_target_weights( public, stage, prepared, partition.history_training_counts, config, ) known_positive_packed = ( build_known_positive_filter(prepared, partition.history_training_counts) if config.negative_filter_scope == "full-training-history" else None ) history: list[dict[str, float]] = [] best_loss = float("inf") best_epoch = 0 best_state: dict[str, torch.Tensor] | None = None epochs_without_improvement = 0 start_epoch = 0 if resume_checkpoint is not None: payload = torch.load( resume_checkpoint, map_location=device, weights_only=False ) stored_config = payload.get("config", {}) architecture_fields = ( "bptt_steps", "embedding_dim", "hidden_dim", "user_hidden_1", "user_hidden_2", "item_hidden_1", "item_hidden_2", ) mismatches = { name: (stored_config.get(name), getattr(config, name)) for name in architecture_fields if stored_config.get(name) != getattr(config, name) } if mismatches: raise RuntimeError( f"resume checkpoint architecture differs: {mismatches}" ) stored_filter_scope = stored_config.get( "negative_filter_scope", "last-100" ) stored_target_weighting = stored_config.get( "target_weighting", "uniform" ) stored_weight_values = ( stored_config.get("ordinary_target_weight", 1.0), stored_config.get("organic_strong_target_weight", 1.0), stored_config.get("organic_like_target_weight", 3.0), stored_config.get("discovery_horizon_days", 7), ) current_weight_values = ( config.ordinary_target_weight, config.organic_strong_target_weight, config.organic_like_target_weight, config.discovery_horizon_days, ) stored_window_values = ( stored_config.get("validation_fraction", 0.10), stored_config.get("max_train_targets_per_user", 0), stored_config.get("max_validation_targets_per_user", 0), ) current_window_values = ( config.validation_fraction, config.max_train_targets_per_user, config.max_validation_targets_per_user, ) objective_changed = ( stored_filter_scope != config.negative_filter_scope or stored_target_weighting != config.target_weighting or stored_weight_values != current_weight_values or stored_window_values != current_window_values ) model.load_state_dict(payload["model_state"]) if "optimizer_state" in payload and not objective_changed: optimizer.load_state_dict(payload["optimizer_state"]) for group in optimizer.param_groups: group["lr"] = config.learning_rate group["weight_decay"] = config.weight_decay log(f"restored optimizer state from {resume_checkpoint}") else: log( f"using a fresh AdamW optimizer after loading {resume_checkpoint}" ) if "generator_state" in payload and not objective_changed: generator.set_state(payload["generator_state"]) start_epoch = int(payload["epoch"]) if start_epoch >= config.epochs: raise RuntimeError( f"resume epoch {start_epoch} must be below --epochs {config.epochs}" ) prior = { str(name): float(value) for name, value in payload.get("diagnostics", {}).items() } if objective_changed: prior.pop("validation_loss", None) log( "BCE objective changed: filter " f"{stored_filter_scope}->{config.negative_filter_scope}, " "target weighting " f"{stored_target_weighting}->{config.target_weighting}, " f"target window {stored_window_values}->{current_window_values}; " "recomputing the resume checkpoint's validation BCE" ) if "validation_loss" not in prior: prior.update( evaluate_validation_bce( model, prepared, config, target_weights, partition.window_starts, training_counts, validation_counts, item_correction, known_positive_packed, device, ) ) history.append(prior) best_loss = float(prior["validation_loss"]) best_epoch = start_epoch best_state = snapshot_model(model) log( f"resumed validation-BCE training: epoch={start_epoch} " f"loss={best_loss:.6f}" ) for epoch in range(start_epoch + 1, config.epochs + 1): diagnostics = run_training_epoch( model, optimizer, prepared, config, target_weights, partition.window_starts, training_counts, item_correction, known_positive_packed, generator, device, epoch, ) validation = evaluate_validation_bce( model, prepared, config, target_weights, partition.window_starts, training_counts, validation_counts, item_correction, known_positive_packed, device, ) diagnostics.update(validation) history.append(diagnostics) current_state = snapshot_model(model) if checkpoint_dir is not None: save_epoch_checkpoint( checkpoint_dir, epoch, current_state, config, diagnostics, optimizer, generator, ) improved = ( diagnostics["validation_loss"] < best_loss - config.early_stopping_min_delta ) if improved: best_loss = diagnostics["validation_loss"] best_epoch = epoch best_state = current_state epochs_without_improvement = 0 if checkpoint_dir is not None: torch.save( { "epoch": epoch, "config": asdict(config), "diagnostics": diagnostics, "model_state": current_state, "optimizer_state": optimizer.state_dict(), "generator_state": generator.get_state(), }, checkpoint_dir / "best.pt", ) else: epochs_without_improvement += 1 log( f"epoch={epoch}/{config.epochs} train_loss={diagnostics['loss']:.5f} " f"validation_loss={diagnostics['validation_loss']:.5f} " f"pair_accuracy={diagnostics['pair_accuracy']:.3f} " f"train_targets={int(diagnostics['targets']):,} " f"validation_targets={int(diagnostics['validation_targets']):,} " f"train_seconds={diagnostics['seconds']:.1f} " f"validation_seconds={diagnostics['validation_seconds']:.1f}" ) release_accelerator_cache(device) if epochs_without_improvement >= config.early_stopping_patience: log( f"early stopping after epoch={epoch}; " f"best_epoch={best_epoch} best_validation_loss={best_loss:.6f}" ) break if best_state is None: raise RuntimeError("training did not produce a valid checkpoint") model.load_state_dict(best_state) model.to(device).eval() log( f"restored best validation-BCE checkpoint: epoch={best_epoch} " f"loss={best_loss:.6f}" ) return model, history @torch.inference_mode() def inference_queries( model: base.SequentialTwoTower, prepared: SequenceData, config: Config, device: torch.device, ) -> torch.Tensor: sequence_rows = prepared.inference_user_rows torch.mps.empty_cache() def run_training_epoch( model: base.SequentialTwoTower, optimizer: torch.optim.Optimizer, prepared: SequenceData, config: Config, target_weights: np.ndarray, window_starts: np.ndarray, target_counts: np.ndarray, item_correction: torch.Tensor, known_positive_packed: np.ndarray | None, generator: torch.Generator, device: torch.device, epoch: int, ) -> dict[str, float]: model.train() train_users = np.flatnonzero(target_counts > 0) sorted_users = train_users[ np.argsort(target_counts[train_users], kind="stable") ] groups = [ sorted_users[start : start + config.user_batch_size] for start in range(0, len(sorted_users), config.user_batch_size) ] group_order = torch.randperm(len(groups), generator=generator).numpy() negative_count = config.uniform_negatives + config.inbatch_negatives total_loss = 0.0 total_targets = 0 total_weight = 0.0 total_positive = 0.0 total_negative = 0.0 total_pair_correct = 0.0 batches = 0 started = time.monotonic() for group_number in group_order: users = groups[int(group_number)] group_counts = target_counts[users] hidden = torch.zeros( 1, len(users), config.embedding_dim, device=device ) max_targets = int(group_counts.max()) for start in range(0, max_targets, config.bptt_steps): steps = min(config.bptt_steps, max_targets - start) block = build_dense_block( prepared, target_weights, users, window_starts[users], group_counts, np.zeros(len(users), dtype=np.int64), start, steps, config, ) sequence_outputs, _ = event_sequence( model, torch.from_numpy(block[0].astype(np.int64)).to(device), torch.from_numpy(block[1].astype(np.float32)).to(device), hidden, ) hidden = torch.stack( [ sequence_outputs[row, count - 1] if count else hidden[0, row] for row, count in enumerate(block[7]) ] ).unsqueeze(0).detach() flat_scored = block[5].reshape(-1) if not np.any(flat_scored): continue mask = torch.from_numpy(flat_scored).to(device) sequence_vectors = sequence_outputs.reshape( -1, config.embedding_dim )[mask] flat_states = block[3].reshape(-1)[flat_scored] positive_items = torch.from_numpy( block[4].reshape(-1)[flat_scored].astype(np.int64) ).to(device) known_history = torch.from_numpy( block[6].reshape(-1, config.bptt_steps)[flat_scored].astype( np.int64 ) ).to(device) liked_items = torch.from_numpy( prepared.liked_states[flat_states].astype(np.int64) ).to(device) flat_user_rows = np.broadcast_to( users[:, None], block[5].shape ).reshape(-1)[flat_scored] negatives, negative_weights = draw_training_negatives( positive_items, known_history, liked_items, flat_user_rows, known_positive_packed, prepared, config, item_correction, generator, device, ) optimizer.zero_grad(set_to_none=True) statistics, sequence_gradient = score_target_microbatches( model, prepared, config, sequence_vectors, flat_states, block[2].reshape(-1, 4)[flat_scored].astype(np.float32), positive_items, negatives, negative_weights, block[8].reshape(-1)[flat_scored], device, backward=True, ) if sequence_gradient is None: raise RuntimeError("training block omitted its sequence gradient") sequence_vectors.backward(sequence_gradient) nn.utils.clip_grad_norm_(model.parameters(), config.gradient_clip) optimizer.step() count = len(positive_items) total_loss += statistics["loss_sum"] total_targets += count total_weight += statistics["weight_sum"] total_positive += statistics["positive_sum"] total_negative += statistics["negative_sum"] total_pair_correct += statistics["pair_correct"] batches += 1 if batches % 25 == 0: release_accelerator_cache(device) if batches % 250 == 0: batch_bce = statistics["loss_sum"] / max( statistics["weight_sum"], 1.0e-12 ) running_bce = total_loss / max(total_weight, 1.0e-12) log( f"epoch={epoch}/{config.epochs} batches={batches:,} " f"targets={total_targets:,} " f"batch_bce={batch_bce:.6f} " f"running_bce={running_bce:.6f} " f"elapsed={time.monotonic() - started:.1f}s" ) return { "epoch": float(epoch), "loss": total_loss / max(total_weight, 1.0e-12), "positive_logit": total_positive / max(total_targets, 1), "negative_logit": total_negative / max(total_targets * negative_count, 1), "pair_accuracy": total_pair_correct / max(total_targets * negative_count, 1), "targets": float(total_targets), "target_weight_sum": total_weight, "batches": float(batches), "seconds": time.monotonic() - started, } @torch.no_grad() def evaluate_validation_bce( model: base.SequentialTwoTower, prepared: SequenceData, config: Config, target_weights: np.ndarray, window_starts: np.ndarray, training_counts: np.ndarray, validation_counts: np.ndarray, item_correction: torch.Tensor, known_positive_packed: np.ndarray | None, device: torch.device, ) -> dict[str, float]: """Measure deterministic BCE on each user's chronological target suffix.""" model.eval() totals = training_counts + validation_counts users_with_validation = np.flatnonzero(validation_counts > 0) ordered = users_with_validation[ np.argsort(totals[users_with_validation], kind="stable") ] groups = [ ordered[start : start + config.user_batch_size] for start in range(0, len(ordered), config.user_batch_size) ] generator = torch.Generator(device="cpu").manual_seed(config.seed + 20_000) total_loss = 0.0 total_targets = 0 total_weight = 0.0 started = time.monotonic() for group_number, users in enumerate(groups, start=1): group_totals = totals[users] group_starts = training_counts[users] hidden = torch.zeros( 1, len(users), config.embedding_dim, device=device ) max_targets = int(group_totals.max()) for start in range(0, max_targets, config.bptt_steps): steps = min(config.bptt_steps, max_targets - start) block = build_dense_block( prepared, target_weights, users, window_starts[users], group_totals, group_starts, start, steps, config, ) sequence_outputs, _ = event_sequence( model, torch.from_numpy(block[0].astype(np.int64)).to(device), torch.from_numpy(block[1].astype(np.float32)).to(device), hidden, ) hidden = torch.stack( [ sequence_outputs[row, count - 1] if count else hidden[0, row] for row, count in enumerate(block[7]) ] ).unsqueeze(0) flat_scored = block[5].reshape(-1) if not np.any(flat_scored): continue mask = torch.from_numpy(flat_scored).to(device) sequence_vectors = sequence_outputs.reshape( -1, config.embedding_dim )[mask] flat_states = block[3].reshape(-1)[flat_scored] positive_items = torch.from_numpy( block[4].reshape(-1)[flat_scored].astype(np.int64) ).to(device) known_history = torch.from_numpy( block[6].reshape(-1, config.bptt_steps)[flat_scored].astype( np.int64 ) ).to(device) liked_items = torch.from_numpy( prepared.liked_states[flat_states].astype(np.int64) ).to(device) flat_user_rows = np.broadcast_to( users[:, None], block[5].shape ).reshape(-1)[flat_scored] negatives, negative_weights = draw_training_negatives( positive_items, known_history, liked_items, flat_user_rows, known_positive_packed, prepared, config, item_correction, generator, device, ) statistics, _ = score_target_microbatches( model, prepared, config, sequence_vectors, flat_states, block[2].reshape(-1, 4)[flat_scored].astype(np.float32), positive_items, negatives, negative_weights, block[8].reshape(-1)[flat_scored], device, backward=False, ) total_loss += statistics["loss_sum"] total_targets += len(positive_items) total_weight += statistics["weight_sum"] if group_number % 25 == 0: release_accelerator_cache(device) return { "validation_loss": total_loss / max(total_weight, 1.0e-12), "validation_targets": float(total_targets), "validation_target_weight_sum": total_weight, "validation_seconds": time.monotonic() - started, } def snapshot_model(model: nn.Module) -> dict[str, torch.Tensor]: return { name: value.detach().cpu().clone() for name, value in model.state_dict().items() } def save_epoch_checkpoint( checkpoint_dir: Path, epoch: int, model_state: dict[str, torch.Tensor], config: Config, diagnostics: dict[str, float], optimizer: torch.optim.Optimizer, generator: torch.Generator, ) -> Path: checkpoint_dir.mkdir(parents=True, exist_ok=True) path = checkpoint_dir / f"epoch-{epoch:02d}.pt" torch.save( { "epoch": epoch, "config": asdict(config), "diagnostics": diagnostics, ### Harness context { "type": "token_usage_record", "thread_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "session_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "root_turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "response_id": "resp_08539f19231b9e9f016ab5a8eb7d0087d287a5d0c2a515cf1e", "usage": { "input_tokens": 37773, "cached_input_tokens": 29056, "cache_write_input_tokens": 0, "output_tokens": 114, "reasoning_output_tokens": 8, "total_tokens": 37887 }, "turn_token_usage": { "input_tokens": 102233, "cached_input_tokens": 75136, "cache_write_input_tokens": 0, "output_tokens": 809, "reasoning_output_tokens": 228, "total_tokens": 103042 }, "thread_token_usage": { "input_tokens": 102233, "cached_input_tokens": 75136, "cache_write_input_tokens": 0, "output_tokens": 809, "reasoning_output_tokens": 228, "total_tokens": 103042 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-c8975174-738c-49a3-b5f7-d9f34f02dfcc", "process_id": "79030", "command": [ "/bin/sh", "-lc", "sed -n '2040,2115p' /app/solution/train_current_model.py\nsed -n '1500,1788p' /app/solution/train_current_model.py\nsed -n '1200,1510p' /app/solution/train_current_model.py" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "read", "cmd": "sed -n '2040,2115p' /app/solution/train_current_model.py", "name": "train_current_model.py", "path": "/app/solution/train_current_model.py" }, { "type": "read", "cmd": "sed -n '1500,1788p' /app/solution/train_current_model.py", "name": "train_current_model.py", "path": "/app/solution/train_current_model.py" }, { "type": "read", "cmd": "sed -n '1200,1510p' /app/solution/train_current_model.py", "name": "train_current_model.py", "path": "/app/solution/train_current_model.py" } ], "source": "unified_exec_startup", "status": "completed", "stdout": " validation_fraction=args.validation_fraction,\n max_train_targets_per_user=args.max_train_targets_per_user,\n max_validation_targets_per_user=args.max_validation_targets_per_user,\n early_stopping_patience=args.early_stopping_patience,\n early_stopping_min_delta=args.early_stopping_min_delta,\n seed=args.seed,\n )\n torch.set_num_threads(args.threads)\n torch.set_num_interop_threads(1)\n seed_everything(config.seed)\n device = torch.device(args.device)\n if device.type == \"mps\" and not torch.backends.mps.is_available():\n parser.error(\"MPS was requested but is unavailable\")\n\n started = time.monotonic()\n prepared, eligibility = load_or_prepare(\n public,\n args.stage,\n config,\n args.prepared_cache.resolve() if args.prepared_cache else None,\n )\n target_partition = chronological_target_partition(\n prepared,\n config.validation_fraction,\n config.max_train_targets_per_user,\n config.max_validation_targets_per_user,\n )\n if args.prepare_only:\n print(\n json.dumps(\n {\n \"stage\": args.stage,\n \"sequence_users\": len(prepared.user_ids),\n \"sequence_tokens\": len(prepared.sequence_items),\n \"next_item_targets\": prepared.target_count,\n \"selected_training_targets\": int(\n target_partition.training_counts.sum()\n ),\n \"selected_validation_targets\": int(\n target_partition.validation_counts.sum()\n ),\n \"discarded_old_targets\": int(\n target_partition.window_starts.sum()\n ),\n \"target_users\": len(eligibility.uids),\n \"runtime_seconds\": round(time.monotonic() - started, 3),\n \"config\": asdict(config),\n },\n indent=2,\n )\n )\n return 0\n\n model, training_history = train_model(\n public,\n args.stage,\n prepared,\n config,\n device,\n args.checkpoint_dir.resolve() if args.checkpoint_dir else None,\n args.resume_checkpoint.resolve() if args.resume_checkpoint else None,\n )\n ranking, embedding_diagnostics = rank_candidates(\n model,\n prepared,\n eligibility,\n config,\n device,\n args.ranking_batch_size,\n )\n write_ranking(ranking, args.output.resolve())\n print(\n json.dumps(\n {\n \"baseline\": \"yambda-windowed-sequence-two-tower-base\",\n \"stage\": args.stage,\n diagnostics: dict[str, float],\n optimizer: torch.optim.Optimizer,\n generator: torch.Generator,\n) -> Path:\n checkpoint_dir.mkdir(parents=True, exist_ok=True)\n path = checkpoint_dir / f\"epoch-{epoch:02d}.pt\"\n torch.save(\n {\n \"epoch\": epoch,\n \"config\": asdict(config),\n \"diagnostics\": diagnostics,\n \"model_state\": model_state,\n \"optimizer_state\": optimizer.state_dict(),\n \"generator_state\": generator.get_state(),\n },\n path,\n )\n log(f\"saved epoch checkpoint: {path}\")\n return path\n\n\ndef train_model(\n public: Path,\n stage: str,\n prepared: SequenceData,\n config: Config,\n device: torch.device,\n checkpoint_dir: Path | None,\n resume_checkpoint: Path | None,\n) -> tuple[base.SequentialTwoTower, list[dict[str, float]]]:\n partition = chronological_target_partition(\n prepared,\n config.validation_fraction,\n config.max_train_targets_per_user,\n config.max_validation_targets_per_user,\n )\n training_counts = partition.training_counts\n validation_counts = partition.validation_counts\n log(\n f\"chronological BCE window: train={int(training_counts.sum()):,} \"\n f\"validation={int(validation_counts.sum()):,} \"\n f\"selected={partition.selected_count:,} \"\n f\"discarded_old_targets={int(partition.window_starts.sum()):,} \"\n f\"all={prepared.target_count:,}\"\n )\n model = base.SequentialTwoTower(prepared.item_features, model_config(config)).to(\n device\n )\n optimizer = torch.optim.AdamW(\n model.parameters(), lr=config.learning_rate, weight_decay=config.weight_decay\n )\n generator = torch.Generator(device=\"cpu\").manual_seed(config.seed + 10_000)\n item_correction = build_item_correction(\n prepared,\n config,\n partition.window_starts,\n training_counts,\n )\n target_weights = build_target_weights(\n public,\n stage,\n prepared,\n partition.history_training_counts,\n config,\n )\n known_positive_packed = (\n build_known_positive_filter(prepared, partition.history_training_counts)\n if config.negative_filter_scope == \"full-training-history\"\n else None\n )\n history: list[dict[str, float]] = []\n best_loss = float(\"inf\")\n best_epoch = 0\n best_state: dict[str, torch.Tensor] | None = None\n epochs_without_improvement = 0\n start_epoch = 0\n\n if resume_checkpoint is not None:\n payload = torch.load(\n resume_checkpoint, map_location=device, weights_only=False\n )\n stored_config = payload.get(\"config\", {})\n architecture_fields = (\n \"bptt_steps\",\n \"embedding_dim\",\n \"hidden_dim\",\n \"user_hidden_1\",\n \"user_hidden_2\",\n \"item_hidden_1\",\n \"item_hidden_2\",\n )\n mismatches = {\n name: (stored_config.get(name), getattr(config, name))\n for name in architecture_fields\n if stored_config.get(name) != getattr(config, name)\n }\n if mismatches:\n raise RuntimeError(\n f\"resume checkpoint architecture differs: {mismatches}\"\n )\n stored_filter_scope = stored_config.get(\n \"negative_filter_scope\", \"last-100\"\n )\n stored_target_weighting = stored_config.get(\n \"target_weighting\", \"uniform\"\n )\n stored_weight_values = (\n stored_config.get(\"ordinary_target_weight\", 1.0),\n stored_config.get(\"organic_strong_target_weight\", 1.0),\n stored_config.get(\"organic_like_target_weight\", 3.0),\n stored_config.get(\"discovery_horizon_days\", 7),\n )\n current_weight_values = (\n config.ordinary_target_weight,\n config.organic_strong_target_weight,\n config.organic_like_target_weight,\n config.discovery_horizon_days,\n )\n stored_window_values = (\n stored_config.get(\"validation_fraction\", 0.10),\n stored_config.get(\"max_train_targets_per_user\", 0),\n stored_config.get(\"max_validation_targets_per_user\", 0),\n )\n current_window_values = (\n config.validation_fraction,\n config.max_train_targets_per_user,\n config.max_validation_targets_per_user,\n )\n objective_changed = (\n stored_filter_scope != config.negative_filter_scope\n or stored_target_weighting != config.target_weighting\n or stored_weight_values != current_weight_values\n or stored_window_values != current_window_values\n )\n model.load_state_dict(payload[\"model_state\"])\n if \"optimizer_state\" in payload and not objective_changed:\n optimizer.load_state_dict(payload[\"optimizer_state\"])\n for group in optimizer.param_groups:\n group[\"lr\"] = config.learning_rate\n group[\"weight_decay\"] = config.weight_decay\n log(f\"restored optimizer state from {resume_checkpoint}\")\n else:\n log(\n f\"using a fresh AdamW optimizer after loading {resume_checkpoint}\"\n )\n if \"generator_state\" in payload and not objective_changed:\n generator.set_state(payload[\"generator_state\"])\n start_epoch = int(payload[\"epoch\"])\n if start_epoch >= config.epochs:\n raise RuntimeError(\n f\"resume epoch {start_epoch} must be below --epochs {config.epochs}\"\n )\n prior = {\n str(name): float(value)\n for name, value in payload.get(\"diagnostics\", {}).items()\n }\n if objective_changed:\n prior.pop(\"validation_loss\", None)\n log(\n \"BCE objective changed: filter \"\n f\"{stored_filter_scope}->{config.negative_filter_scope}, \"\n \"target weighting \"\n f\"{stored_target_weighting}->{config.target_weighting}, \"\n f\"target window {stored_window_values}->{current_window_values}; \"\n \"recomputing the resume checkpoint's validation BCE\"\n )\n if \"validation_loss\" not in prior:\n prior.update(\n evaluate_validation_bce(\n model,\n prepared,\n config,\n target_weights,\n partition.window_starts,\n training_counts,\n validation_counts,\n item_correction,\n known_positive_packed,\n device,\n )\n )\n history.append(prior)\n best_loss = float(prior[\"validation_loss\"])\n best_epoch = start_epoch\n best_state = snapshot_model(model)\n log(\n f\"resumed validation-BCE training: epoch={start_epoch} \"\n f\"loss={best_loss:.6f}\"\n )\n\n for epoch in range(start_epoch + 1, config.epochs + 1):\n diagnostics = run_training_epoch(\n model,\n optimizer,\n prepared,\n config,\n target_weights,\n partition.window_starts,\n training_counts,\n item_correction,\n known_positive_packed,\n generator,\n device,\n epoch,\n )\n validation = evaluate_validation_bce(\n model,\n prepared,\n config,\n target_weights,\n partition.window_starts,\n training_counts,\n validation_counts,\n item_correction,\n known_positive_packed,\n device,\n )\n diagnostics.update(validation)\n history.append(diagnostics)\n current_state = snapshot_model(model)\n if checkpoint_dir is not None:\n save_epoch_checkpoint(\n checkpoint_dir,\n epoch,\n current_state,\n config,\n diagnostics,\n optimizer,\n generator,\n )\n improved = (\n diagnostics[\"validation_loss\"]\n < best_loss - config.early_stopping_min_delta\n )\n if improved:\n best_loss = diagnostics[\"validation_loss\"]\n best_epoch = epoch\n best_state = current_state\n epochs_without_improvement = 0\n if checkpoint_dir is not None:\n torch.save(\n {\n \"epoch\": epoch,\n \"config\": asdict(config),\n \"diagnostics\": diagnostics,\n \"model_state\": current_state,\n \"optimizer_state\": optimizer.state_dict(),\n \"generator_state\": generator.get_state(),\n },\n checkpoint_dir / \"best.pt\",\n )\n else:\n epochs_without_improvement += 1\n log(\n f\"epoch={epoch}/{config.epochs} train_loss={diagnostics['loss']:.5f} \"\n f\"validation_loss={diagnostics['validation_loss']:.5f} \"\n f\"pair_accuracy={diagnostics['pair_accuracy']:.3f} \"\n f\"train_targets={int(diagnostics['targets']):,} \"\n f\"validation_targets={int(diagnostics['validation_targets']):,} \"\n f\"train_seconds={diagnostics['seconds']:.1f} \"\n f\"validation_seconds={diagnostics['validation_seconds']:.1f}\"\n )\n release_accelerator_cache(device)\n if epochs_without_improvement >= config.early_stopping_patience:\n log(\n f\"early stopping after epoch={epoch}; \"\n f\"best_epoch={best_epoch} best_validation_loss={best_loss:.6f}\"\n )\n break\n\n if best_state is None:\n raise RuntimeError(\"training did not produce a valid checkpoint\")\n model.load_state_dict(best_state)\n model.to(device).eval()\n log(\n f\"restored best validation-BCE checkpoint: epoch={best_epoch} \"\n f\"loss={best_loss:.6f}\"\n )\n return model, history\n\n\n@torch.inference_mode()\ndef inference_queries(\n model: base.SequentialTwoTower,\n prepared: SequenceData,\n config: Config,\n device: torch.device,\n) -> torch.Tensor:\n sequence_rows = prepared.inference_user_rows\n torch.mps.empty_cache()\n\n\ndef run_training_epoch(\n model: base.SequentialTwoTower,\n optimizer: torch.optim.Optimizer,\n prepared: SequenceData,\n config: Config,\n target_weights: np.ndarray,\n window_starts: np.ndarray,\n target_counts: np.ndarray,\n item_correction: torch.Tensor,\n known_positive_packed: np.ndarray | None,\n generator: torch.Generator,\n device: torch.device,\n epoch: int,\n) -> dict[str, float]:\n model.train()\n train_users = np.flatnonzero(target_counts > 0)\n sorted_users = train_users[\n np.argsort(target_counts[train_users], kind=\"stable\")\n ]\n groups = [\n sorted_users[start : start + config.user_batch_size]\n for start in range(0, len(sorted_users), config.user_batch_size)\n ]\n group_order = torch.randperm(len(groups), generator=generator).numpy()\n negative_count = config.uniform_negatives + config.inbatch_negatives\n total_loss = 0.0\n total_targets = 0\n total_weight = 0.0\n total_positive = 0.0\n total_negative = 0.0\n total_pair_correct = 0.0\n batches = 0\n started = time.monotonic()\n\n for group_number in group_order:\n users = groups[int(group_number)]\n group_counts = target_counts[users]\n hidden = torch.zeros(\n 1, len(users), config.embedding_dim, device=device\n )\n max_targets = int(group_counts.max())\n for start in range(0, max_targets, config.bptt_steps):\n steps = min(config.bptt_steps, max_targets - start)\n block = build_dense_block(\n prepared,\n target_weights,\n users,\n window_starts[users],\n group_counts,\n np.zeros(len(users), dtype=np.int64),\n start,\n steps,\n config,\n )\n sequence_outputs, _ = event_sequence(\n model,\n torch.from_numpy(block[0].astype(np.int64)).to(device),\n torch.from_numpy(block[1].astype(np.float32)).to(device),\n hidden,\n )\n hidden = torch.stack(\n [\n sequence_outputs[row, count - 1]\n if count\n else hidden[0, row]\n for row, count in enumerate(block[7])\n ]\n ).unsqueeze(0).detach()\n flat_scored = block[5].reshape(-1)\n if not np.any(flat_scored):\n continue\n mask = torch.from_numpy(flat_scored).to(device)\n sequence_vectors = sequence_outputs.reshape(\n -1, config.embedding_dim\n )[mask]\n flat_states = block[3].reshape(-1)[flat_scored]\n positive_items = torch.from_numpy(\n block[4].reshape(-1)[flat_scored].astype(np.int64)\n ).to(device)\n known_history = torch.from_numpy(\n block[6].reshape(-1, config.bptt_steps)[flat_scored].astype(\n np.int64\n )\n ).to(device)\n liked_items = torch.from_numpy(\n prepared.liked_states[flat_states].astype(np.int64)\n ).to(device)\n flat_user_rows = np.broadcast_to(\n users[:, None], block[5].shape\n ).reshape(-1)[flat_scored]\n negatives, negative_weights = draw_training_negatives(\n positive_items,\n known_history,\n liked_items,\n flat_user_rows,\n known_positive_packed,\n prepared,\n config,\n item_correction,\n generator,\n device,\n )\n optimizer.zero_grad(set_to_none=True)\n statistics, sequence_gradient = score_target_microbatches(\n model,\n prepared,\n config,\n sequence_vectors,\n flat_states,\n block[2].reshape(-1, 4)[flat_scored].astype(np.float32),\n positive_items,\n negatives,\n negative_weights,\n block[8].reshape(-1)[flat_scored],\n device,\n backward=True,\n )\n if sequence_gradient is None:\n raise RuntimeError(\"training block omitted its sequence gradient\")\n sequence_vectors.backward(sequence_gradient)\n nn.utils.clip_grad_norm_(model.parameters(), config.gradient_clip)\n optimizer.step()\n\n count = len(positive_items)\n total_loss += statistics[\"loss_sum\"]\n total_targets += count\n total_weight += statistics[\"weight_sum\"]\n total_positive += statistics[\"positive_sum\"]\n total_negative += statistics[\"negative_sum\"]\n total_pair_correct += statistics[\"pair_correct\"]\n batches += 1\n if batches % 25 == 0:\n release_accelerator_cache(device)\n if batches % 250 == 0:\n batch_bce = statistics[\"loss_sum\"] / max(\n statistics[\"weight_sum\"], 1.0e-12\n )\n running_bce = total_loss / max(total_weight, 1.0e-12)\n log(\n f\"epoch={epoch}/{config.epochs} batches={batches:,} \"\n f\"targets={total_targets:,} \"\n f\"batch_bce={batch_bce:.6f} \"\n f\"running_bce={running_bce:.6f} \"\n f\"elapsed={time.monotonic() - started:.1f}s\"\n )\n\n return {\n \"epoch\": float(epoch),\n \"loss\": total_loss / max(total_weight, 1.0e-12),\n \"positive_logit\": total_positive / max(total_targets, 1),\n \"negative_logit\": total_negative / max(total_targets * negative_count, 1),\n \"pair_accuracy\": total_pair_correct\n / max(total_targets * negative_count, 1),\n \"targets\": float(total_targets),\n \"target_weight_sum\": total_weight,\n \"batches\": float(batches),\n \"seconds\": time.monotonic() - started,\n }\n\n\n@torch.no_grad()\ndef evaluate_validation_bce(\n model: base.SequentialTwoTower,\n prepared: SequenceData,\n config: Config,\n target_weights: np.ndarray,\n window_starts: np.ndarray,\n training_counts: np.ndarray,\n validation_counts: np.ndarray,\n item_correction: torch.Tensor,\n known_positive_packed: np.ndarray | None,\n device: torch.device,\n) -> dict[str, float]:\n \"\"\"Measure deterministic BCE on each user's chronological target suffix.\"\"\"\n model.eval()\n totals = training_counts + validation_counts\n users_with_validation = np.flatnonzero(validation_counts > 0)\n ordered = users_with_validation[\n np.argsort(totals[users_with_validation], kind=\"stable\")\n ]\n groups = [\n ordered[start : start + config.user_batch_size]\n for start in range(0, len(ordered), config.user_batch_size)\n ]\n generator = torch.Generator(device=\"cpu\").manual_seed(config.seed + 20_000)\n total_loss = 0.0\n total_targets = 0\n total_weight = 0.0\n started = time.monotonic()\n\n for group_number, users in enumerate(groups, start=1):\n group_totals = totals[users]\n group_starts = training_counts[users]\n hidden = torch.zeros(\n 1, len(users), config.embedding_dim, device=device\n )\n max_targets = int(group_totals.max())\n for start in range(0, max_targets, config.bptt_steps):\n steps = min(config.bptt_steps, max_targets - start)\n block = build_dense_block(\n prepared,\n target_weights,\n users,\n window_starts[users],\n group_totals,\n group_starts,\n start,\n steps,\n config,\n )\n sequence_outputs, _ = event_sequence(\n model,\n torch.from_numpy(block[0].astype(np.int64)).to(device),\n torch.from_numpy(block[1].astype(np.float32)).to(device),\n hidden,\n )\n hidden = torch.stack(\n [\n sequence_outputs[row, count - 1]\n if count\n else hidden[0, row]\n for row, count in enumerate(block[7])\n ]\n ).unsqueeze(0)\n flat_scored = block[5].reshape(-1)\n if not np.any(flat_scored):\n continue\n mask = torch.from_numpy(flat_scored).to(device)\n sequence_vectors = sequence_outputs.reshape(\n -1, config.embedding_dim\n )[mask]\n flat_states = block[3].reshape(-1)[flat_scored]\n positive_items = torch.from_numpy(\n block[4].reshape(-1)[flat_scored].astype(np.int64)\n ).to(device)\n known_history = torch.from_numpy(\n block[6].reshape(-1, config.bptt_steps)[flat_scored].astype(\n np.int64\n )\n ).to(device)\n liked_items = torch.from_numpy(\n prepared.liked_states[flat_states].astype(np.int64)\n ).to(device)\n flat_user_rows = np.broadcast_to(\n users[:, None], block[5].shape\n ).reshape(-1)[flat_scored]\n negatives, negative_weights = draw_training_negatives(\n positive_items,\n known_history,\n liked_items,\n flat_user_rows,\n known_positive_packed,\n prepared,\n config,\n item_correction,\n generator,\n device,\n )\n statistics, _ = score_target_microbatches(\n model,\n prepared,\n config,\n sequence_vectors,\n flat_states,\n block[2].reshape(-1, 4)[flat_scored].astype(np.float32),\n positive_items,\n negatives,\n negative_weights,\n block[8].reshape(-1)[flat_scored],\n device,\n backward=False,\n )\n total_loss += statistics[\"loss_sum\"]\n total_targets += len(positive_items)\n total_weight += statistics[\"weight_sum\"]\n if group_number % 25 == 0:\n release_accelerator_cache(device)\n return {\n \"validation_loss\": total_loss / max(total_weight, 1.0e-12),\n \"validation_targets\": float(total_targets),\n \"validation_target_weight_sum\": total_weight,\n \"validation_seconds\": time.monotonic() - started,\n }\n\n\ndef snapshot_model(model: nn.Module) -> dict[str, torch.Tensor]:\n return {\n name: value.detach().cpu().clone()\n for name, value in model.state_dict().items()\n }\n\n\ndef save_epoch_checkpoint(\n checkpoint_dir: Path,\n epoch: int,\n model_state: dict[str, torch.Tensor],\n config: Config,\n diagnostics: dict[str, float],\n optimizer: torch.optim.Optimizer,\n generator: torch.Generator,\n) -> Path:\n checkpoint_dir.mkdir(parents=True, exist_ok=True)\n path = checkpoint_dir / f\"epoch-{epoch:02d}.pt\"\n torch.save(\n {\n \"epoch\": epoch,\n \"config\": asdict(config),\n \"diagnostics\": diagnostics,\n", "stderr": "", "aggregated_output": " validation_fraction=args.validation_fraction,\n max_train_targets_per_user=args.max_train_targets_per_user,\n max_validation_targets_per_user=args.max_validation_targets_per_user,\n early_stopping_patience=args.early_stopping_patience,\n early_stopping_min_delta=args.early_stopping_min_delta,\n seed=args.seed,\n )\n torch.set_num_threads(args.threads)\n torch.set_num_interop_threads(1)\n seed_everything(config.seed)\n device = torch.device(args.device)\n if device.type == \"mps\" and not torch.backends.mps.is_available():\n parser.error(\"MPS was requested but is unavailable\")\n\n started = time.monotonic()\n prepared, eligibility = load_or_prepare(\n public,\n args.stage,\n config,\n args.prepared_cache.resolve() if args.prepared_cache else None,\n )\n target_partition = chronological_target_partition(\n prepared,\n config.validation_fraction,\n config.max_train_targets_per_user,\n config.max_validation_targets_per_user,\n )\n if args.prepare_only:\n print(\n json.dumps(\n {\n \"stage\": args.stage,\n \"sequence_users\": len(prepared.user_ids),\n \"sequence_tokens\": len(prepared.sequence_items),\n \"next_item_targets\": prepared.target_count,\n \"selected_training_targets\": int(\n target_partition.training_counts.sum()\n ),\n \"selected_validation_targets\": int(\n target_partition.validation_counts.sum()\n ),\n \"discarded_old_targets\": int(\n target_partition.window_starts.sum()\n ),\n \"target_users\": len(eligibility.uids),\n \"runtime_seconds\": round(time.monotonic() - started, 3),\n \"config\": asdict(config),\n },\n indent=2,\n )\n )\n return 0\n\n model, training_history = train_model(\n public,\n args.stage,\n prepared,\n config,\n device,\n args.checkpoint_dir.resolve() if args.checkpoint_dir else None,\n args.resume_checkpoint.resolve() if args.resume_checkpoint else None,\n )\n ranking, embedding_diagnostics = rank_candidates(\n model,\n prepared,\n eligibility,\n config,\n device,\n args.ranking_batch_size,\n )\n write_ranking(ranking, args.output.resolve())\n print(\n json.dumps(\n {\n \"baseline\": \"yambda-windowed-sequence-two-tower-base\",\n \"stage\": args.stage,\n diagnostics: dict[str, float],\n optimizer: torch.optim.Optimizer,\n generator: torch.Generator,\n) -> Path:\n checkpoint_dir.mkdir(parents=True, exist_ok=True)\n path = checkpoint_dir / f\"epoch-{epoch:02d}.pt\"\n torch.save(\n {\n \"epoch\": epoch,\n \"config\": asdict(config),\n \"diagnostics\": diagnostics,\n \"model_state\": model_state,\n \"optimizer_state\": optimizer.state_dict(),\n \"generator_state\": generator.get_state(),\n },\n path,\n )\n log(f\"saved epoch checkpoint: {path}\")\n return path\n\n\ndef train_model(\n public: Path,\n stage: str,\n prepared: SequenceData,\n config: Config,\n device: torch.device,\n checkpoint_dir: Path | None,\n resume_checkpoint: Path | None,\n) -> tuple[base.SequentialTwoTower, list[dict[str, float]]]:\n partition = chronological_target_partition(\n prepared,\n config.validation_fraction,\n config.max_train_targets_per_user,\n config.max_validation_targets_per_user,\n )\n training_counts = partition.training_counts\n validation_counts = partition.validation_counts\n log(\n f\"chronological BCE window: train={int(training_counts.sum()):,} \"\n f\"validation={int(validation_counts.sum()):,} \"\n f\"selected={partition.selected_count:,} \"\n f\"discarded_old_targets={int(partition.window_starts.sum()):,} \"\n f\"all={prepared.target_count:,}\"\n )\n model = base.SequentialTwoTower(prepared.item_features, model_config(config)).to(\n device\n )\n optimizer = torch.optim.AdamW(\n model.parameters(), lr=config.learning_rate, weight_decay=config.weight_decay\n )\n generator = torch.Generator(device=\"cpu\").manual_seed(config.seed + 10_000)\n item_correction = build_item_correction(\n prepared,\n config,\n partition.window_starts,\n training_counts,\n )\n target_weights = build_target_weights(\n public,\n stage,\n prepared,\n partition.history_training_counts,\n config,\n )\n known_positive_packed = (\n build_known_positive_filter(prepared, partition.history_training_counts)\n if config.negative_filter_scope == \"full-training-history\"\n else None\n )\n history: list[dict[str, float]] = []\n best_loss = float(\"inf\")\n best_epoch = 0\n best_state: dict[str, torch.Tensor] | None = None\n epochs_without_improvement = 0\n start_epoch = 0\n\n if resume_checkpoint is not None:\n payload = torch.load(\n resume_checkpoint, map_location=device, weights_only=False\n )\n stored_config = payload.get(\"config\", {})\n architecture_fields = (\n \"bptt_steps\",\n \"embedding_dim\",\n \"hidden_dim\",\n \"user_hidden_1\",\n \"user_hidden_2\",\n \"item_hidden_1\",\n \"item_hidden_2\",\n )\n mismatches = {\n name: (stored_config.get(name), getattr(config, name))\n for name in architecture_fields\n if stored_config.get(name) != getattr(config, name)\n }\n if mismatches:\n raise RuntimeError(\n f\"resume checkpoint architecture differs: {mismatches}\"\n )\n stored_filter_scope = stored_config.get(\n \"negative_filter_scope\", \"last-100\"\n )\n stored_target_weighting = stored_config.get(\n \"target_weighting\", \"uniform\"\n )\n stored_weight_values = (\n stored_config.get(\"ordinary_target_weight\", 1.0),\n stored_config.get(\"organic_strong_target_weight\", 1.0),\n stored_config.get(\"organic_like_target_weight\", 3.0),\n stored_config.get(\"discovery_horizon_days\", 7),\n )\n current_weight_values = (\n config.ordinary_target_weight,\n config.organic_strong_target_weight,\n config.organic_like_target_weight,\n config.discovery_horizon_days,\n )\n stored_window_values = (\n stored_config.get(\"validation_fraction\", 0.10),\n stored_config.get(\"max_train_targets_per_user\", 0),\n stored_config.get(\"max_validation_targets_per_user\", 0),\n )\n current_window_values = (\n config.validation_fraction,\n config.max_train_targets_per_user,\n config.max_validation_targets_per_user,\n )\n objective_changed = (\n stored_filter_scope != config.negative_filter_scope\n or stored_target_weighting != config.target_weighting\n or stored_weight_values != current_weight_values\n or stored_window_values != current_window_values\n )\n model.load_state_dict(payload[\"model_state\"])\n if \"optimizer_state\" in payload and not objective_changed:\n optimizer.load_state_dict(payload[\"optimizer_state\"])\n for group in optimizer.param_groups:\n group[\"lr\"] = config.learning_rate\n group[\"weight_decay\"] = config.weight_decay\n log(f\"restored optimizer state from {resume_checkpoint}\")\n else:\n log(\n f\"using a fresh AdamW optimizer after loading {resume_checkpoint}\"\n )\n if \"generator_state\" in payload and not objective_changed:\n generator.set_state(payload[\"generator_state\"])\n start_epoch = int(payload[\"epoch\"])\n if start_epoch >= config.epochs:\n raise RuntimeError(\n f\"resume epoch {start_epoch} must be below --epochs {config.epochs}\"\n )\n prior = {\n str(name): float(value)\n for name, value in payload.get(\"diagnostics\", {}).items()\n }\n if objective_changed:\n prior.pop(\"validation_loss\", None)\n log(\n \"BCE objective changed: filter \"\n f\"{stored_filter_scope}->{config.negative_filter_scope}, \"\n \"target weighting \"\n f\"{stored_target_weighting}->{config.target_weighting}, \"\n f\"target window {stored_window_values}->{current_window_values}; \"\n \"recomputing the resume checkpoint's validation BCE\"\n )\n if \"validation_loss\" not in prior:\n prior.update(\n evaluate_validation_bce(\n model,\n prepared,\n config,\n target_weights,\n partition.window_starts,\n training_counts,\n validation_counts,\n item_correction,\n known_positive_packed,\n device,\n )\n )\n history.append(prior)\n best_loss = float(prior[\"validation_loss\"])\n best_epoch = start_epoch\n best_state = snapshot_model(model)\n log(\n f\"resumed validation-BCE training: epoch={start_epoch} \"\n f\"loss={best_loss:.6f}\"\n )\n\n for epoch in range(start_epoch + 1, config.epochs + 1):\n diagnostics = run_training_epoch(\n model,\n optimizer,\n prepared,\n config,\n target_weights,\n partition.window_starts,\n training_counts,\n item_correction,\n known_positive_packed,\n generator,\n device,\n epoch,\n )\n validation = evaluate_validation_bce(\n model,\n prepared,\n config,\n target_weights,\n partition.window_starts,\n training_counts,\n validation_counts,\n item_correction,\n known_positive_packed,\n device,\n )\n diagnostics.update(validation)\n history.append(diagnostics)\n current_state = snapshot_model(model)\n if checkpoint_dir is not None:\n save_epoch_checkpoint(\n checkpoint_dir,\n epoch,\n current_state,\n config,\n diagnostics,\n optimizer,\n generator,\n )\n improved = (\n diagnostics[\"validation_loss\"]\n < best_loss - config.early_stopping_min_delta\n )\n if improved:\n best_loss = diagnostics[\"validation_loss\"]\n best_epoch = epoch\n best_state = current_state\n epochs_without_improvement = 0\n if checkpoint_dir is not None:\n torch.save(\n {\n \"epoch\": epoch,\n \"config\": asdict(config),\n \"diagnostics\": diagnostics,\n \"model_state\": current_state,\n \"optimizer_state\": optimizer.state_dict(),\n \"generator_state\": generator.get_state(),\n },\n checkpoint_dir / \"best.pt\",\n )\n else:\n epochs_without_improvement += 1\n log(\n f\"epoch={epoch}/{config.epochs} train_loss={diagnostics['loss']:.5f} \"\n f\"validation_loss={diagnostics['validation_loss']:.5f} \"\n f\"pair_accuracy={diagnostics['pair_accuracy']:.3f} \"\n f\"train_targets={int(diagnostics['targets']):,} \"\n f\"validation_targets={int(diagnostics['validation_targets']):,} \"\n f\"train_seconds={diagnostics['seconds']:.1f} \"\n f\"validation_seconds={diagnostics['validation_seconds']:.1f}\"\n )\n release_accelerator_cache(device)\n if epochs_without_improvement >= config.early_stopping_patience:\n log(\n f\"early stopping after epoch={epoch}; \"\n f\"best_epoch={best_epoch} best_validation_loss={best_loss:.6f}\"\n )\n break\n\n if best_state is None:\n raise RuntimeError(\"training did not produce a valid checkpoint\")\n model.load_state_dict(best_state)\n model.to(device).eval()\n log(\n f\"restored best validation-BCE checkpoint: epoch={best_epoch} \"\n f\"loss={best_loss:.6f}\"\n )\n return model, history\n\n\n@torch.inference_mode()\ndef inference_queries(\n model: base.SequentialTwoTower,\n prepared: SequenceData,\n config: Config,\n device: torch.device,\n) -> torch.Tensor:\n sequence_rows = prepared.inference_user_rows\n torch.mps.empty_cache()\n\n\ndef run_training_epoch(\n model: base.SequentialTwoTower,\n optimizer: torch.optim.Optimizer,\n prepared: SequenceData,\n config: Config,\n target_weights: np.ndarray,\n window_starts: np.ndarray,\n target_counts: np.ndarray,\n item_correction: torch.Tensor,\n known_positive_packed: np.ndarray | None,\n generator: torch.Generator,\n device: torch.device,\n epoch: int,\n) -> dict[str, float]:\n model.train()\n train_users = np.flatnonzero(target_counts > 0)\n sorted_users = train_users[\n np.argsort(target_counts[train_users], kind=\"stable\")\n ]\n groups = [\n sorted_users[start : start + config.user_batch_size]\n for start in range(0, len(sorted_users), config.user_batch_size)\n ]\n group_order = torch.randperm(len(groups), generator=generator).numpy()\n negative_count = config.uniform_negatives + config.inbatch_negatives\n total_loss = 0.0\n total_targets = 0\n total_weight = 0.0\n total_positive = 0.0\n total_negative = 0.0\n total_pair_correct = 0.0\n batches = 0\n started = time.monotonic()\n\n for group_number in group_order:\n users = groups[int(group_number)]\n group_counts = target_counts[users]\n hidden = torch.zeros(\n 1, len(users), config.embedding_dim, device=device\n )\n max_targets = int(group_counts.max())\n for start in range(0, max_targets, config.bptt_steps):\n steps = min(config.bptt_steps, max_targets - start)\n block = build_dense_block(\n prepared,\n target_weights,\n users,\n window_starts[users],\n group_counts,\n np.zeros(len(users), dtype=np.int64),\n start,\n steps,\n config,\n )\n sequence_outputs, _ = event_sequence(\n model,\n torch.from_numpy(block[0].astype(np.int64)).to(device),\n torch.from_numpy(block[1].astype(np.float32)).to(device),\n hidden,\n )\n hidden = torch.stack(\n [\n sequence_outputs[row, count - 1]\n if count\n else hidden[0, row]\n for row, count in enumerate(block[7])\n ]\n ).unsqueeze(0).detach()\n flat_scored = block[5].reshape(-1)\n if not np.any(flat_scored):\n continue\n mask = torch.from_numpy(flat_scored).to(device)\n sequence_vectors = sequence_outputs.reshape(\n -1, config.embedding_dim\n )[mask]\n flat_states = block[3].reshape(-1)[flat_scored]\n positive_items = torch.from_numpy(\n block[4].reshape(-1)[flat_scored].astype(np.int64)\n ).to(device)\n known_history = torch.from_numpy(\n block[6].reshape(-1, config.bptt_steps)[flat_scored].astype(\n np.int64\n )\n ).to(device)\n liked_items = torch.from_numpy(\n prepared.liked_states[flat_states].astype(np.int64)\n ).to(device)\n flat_user_rows = np.broadcast_to(\n users[:, None], block[5].shape\n ).reshape(-1)[flat_scored]\n negatives, negative_weights = draw_training_negatives(\n positive_items,\n known_history,\n liked_items,\n flat_user_rows,\n known_positive_packed,\n prepared,\n config,\n item_correction,\n generator,\n device,\n )\n optimizer.zero_grad(set_to_none=True)\n statistics, sequence_gradient = score_target_microbatches(\n model,\n prepared,\n config,\n sequence_vectors,\n flat_states,\n block[2].reshape(-1, 4)[flat_scored].astype(np.float32),\n positive_items,\n negatives,\n negative_weights,\n block[8].reshape(-1)[flat_scored],\n device,\n backward=True,\n )\n if sequence_gradient is None:\n raise RuntimeError(\"training block omitted its sequence gradient\")\n sequence_vectors.backward(sequence_gradient)\n nn.utils.clip_grad_norm_(model.parameters(), config.gradient_clip)\n optimizer.step()\n\n count = len(positive_items)\n total_loss += statistics[\"loss_sum\"]\n total_targets += count\n total_weight += statistics[\"weight_sum\"]\n total_positive += statistics[\"positive_sum\"]\n total_negative += statistics[\"negative_sum\"]\n total_pair_correct += statistics[\"pair_correct\"]\n batches += 1\n if batches % 25 == 0:\n release_accelerator_cache(device)\n if batches % 250 == 0:\n batch_bce = statistics[\"loss_sum\"] / max(\n statistics[\"weight_sum\"], 1.0e-12\n )\n running_bce = total_loss / max(total_weight, 1.0e-12)\n log(\n f\"epoch={epoch}/{config.epochs} batches={batches:,} \"\n f\"targets={total_targets:,} \"\n f\"batch_bce={batch_bce:.6f} \"\n f\"running_bce={running_bce:.6f} \"\n f\"elapsed={time.monotonic() - started:.1f}s\"\n )\n\n return {\n \"epoch\": float(epoch),\n \"loss\": total_loss / max(total_weight, 1.0e-12),\n \"positive_logit\": total_positive / max(total_targets, 1),\n \"negative_logit\": total_negative / max(total_targets * negative_count, 1),\n \"pair_accuracy\": total_pair_correct\n / max(total_targets * negative_count, 1),\n \"targets\": float(total_targets),\n \"target_weight_sum\": total_weight,\n \"batches\": float(batches),\n \"seconds\": time.monotonic() - started,\n }\n\n\n@torch.no_grad()\ndef evaluate_validation_bce(\n model: base.SequentialTwoTower,\n prepared: SequenceData,\n config: Config,\n target_weights: np.ndarray,\n window_starts: np.ndarray,\n training_counts: np.ndarray,\n validation_counts: np.ndarray,\n item_correction: torch.Tensor,\n known_positive_packed: np.ndarray | None,\n device: torch.device,\n) -> dict[str, float]:\n \"\"\"Measure deterministic BCE on each user's chronological target suffix.\"\"\"\n model.eval()\n totals = training_counts + validation_counts\n users_with_validation = np.flatnonzero(validation_counts > 0)\n ordered = users_with_validation[\n np.argsort(totals[users_with_validation], kind=\"stable\")\n ]\n groups = [\n ordered[start : start + config.user_batch_size]\n for start in range(0, len(ordered), config.user_batch_size)\n ]\n generator = torch.Generator(device=\"cpu\").manual_seed(config.seed + 20_000)\n total_loss = 0.0\n total_targets = 0\n total_weight = 0.0\n started = time.monotonic()\n\n for group_number, users in enumerate(groups, start=1):\n group_totals = totals[users]\n group_starts = training_counts[users]\n hidden = torch.zeros(\n 1, len(users), config.embedding_dim, device=device\n )\n max_targets = int(group_totals.max())\n for start in range(0, max_targets, config.bptt_steps):\n steps = min(config.bptt_steps, max_targets - start)\n block = build_dense_block(\n prepared,\n target_weights,\n users,\n window_starts[users],\n group_totals,\n group_starts,\n start,\n steps,\n config,\n )\n sequence_outputs, _ = event_sequence(\n model,\n torch.from_numpy(block[0].astype(np.int64)).to(device),\n torch.from_numpy(block[1].astype(np.float32)).to(device),\n hidden,\n )\n hidden = torch.stack(\n [\n sequence_outputs[row, count - 1]\n if count\n else hidden[0, row]\n for row, count in enumerate(block[7])\n ]\n ).unsqueeze(0)\n flat_scored = block[5].reshape(-1)\n if not np.any(flat_scored):\n continue\n mask = torch.from_numpy(flat_scored).to(device)\n sequence_vectors = sequence_outputs.reshape(\n -1, config.embedding_dim\n )[mask]\n flat_states = block[3].reshape(-1)[flat_scored]\n positive_items = torch.from_numpy(\n block[4].reshape(-1)[flat_scored].astype(np.int64)\n ).to(device)\n known_history = torch.from_numpy(\n block[6].reshape(-1, config.bptt_steps)[flat_scored].astype(\n np.int64\n )\n ).to(device)\n liked_items = torch.from_numpy(\n prepared.liked_states[flat_states].astype(np.int64)\n ).to(device)\n flat_user_rows = np.broadcast_to(\n users[:, None], block[5].shape\n ).reshape(-1)[flat_scored]\n negatives, negative_weights = draw_training_negatives(\n positive_items,\n known_history,\n liked_items,\n flat_user_rows,\n known_positive_packed,\n prepared,\n config,\n item_correction,\n generator,\n device,\n )\n statistics, _ = score_target_microbatches(\n model,\n prepared,\n config,\n sequence_vectors,\n flat_states,\n block[2].reshape(-1, 4)[flat_scored].astype(np.float32),\n positive_items,\n negatives,\n negative_weights,\n block[8].reshape(-1)[flat_scored],\n device,\n backward=False,\n )\n total_loss += statistics[\"loss_sum\"]\n total_targets += len(positive_items)\n total_weight += statistics[\"weight_sum\"]\n if group_number % 25 == 0:\n release_accelerator_cache(device)\n return {\n \"validation_loss\": total_loss / max(total_weight, 1.0e-12),\n \"validation_targets\": float(total_targets),\n \"validation_target_weight_sum\": total_weight,\n \"validation_seconds\": time.monotonic() - started,\n }\n\n\ndef snapshot_model(model: nn.Module) -> dict[str, torch.Tensor]:\n return {\n name: value.detach().cpu().clone()\n for name, value in model.state_dict().items()\n }\n\n\ndef save_epoch_checkpoint(\n checkpoint_dir: Path,\n epoch: int,\n model_state: dict[str, torch.Tensor],\n config: Config,\n diagnostics: dict[str, float],\n optimizer: torch.optim.Optimizer,\n generator: torch.Generator,\n) -> Path:\n checkpoint_dir.mkdir(parents=True, exist_ok=True)\n path = checkpoint_dir / f\"epoch-{epoch:02d}.pt\"\n torch.save(\n {\n \"epoch\": epoch,\n \"config\": asdict(config),\n \"diagnostics\": diagnostics,\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 3741 }, "formatted_output": " validation_fraction=args.validation_fraction,\n max_train_targets_per_user=args.max_train_targets_per_user,\n max_validation_targets_per_user=args.max_validation_targets_per_user,\n early_stopping_patience=args.early_stopping_patience,\n early_stopping_min_delta=args.early_stopping_min_delta,\n seed=args.seed,\n )\n torch.set_num_threads(args.threads)\n torch.set_num_interop_threads(1)\n seed_everything(config.seed)\n device = torch.device(args.device)\n if device.type == \"mps\" and not torch.backends.mps.is_available():\n parser.error(\"MPS was requested but is unavailable\")\n\n started = time.monotonic()\n prepared, eligibility = load_or_prepare(\n public,\n args.stage,\n config,\n args.prepared_cache.resolve() if args.prepared_cache else None,\n )\n target_partition = chronological_target_partition(\n prepared,\n config.validation_fraction,\n config.max_train_targets_per_user,\n config.max_validation_targets_per_user,\n )\n if args.prepare_only:\n print(\n json.dumps(\n {\n \"stage\": args.stage,\n \"sequence_users\": len(prepared.user_ids),\n \"sequence_tokens\": len(prepared.sequence_items),\n \"next_item_targets\": prepared.target_count,\n \"selected_training_targets\": int(\n target_partition.training_counts.sum()\n ),\n \"selected_validation_targets\": int(\n target_partition.validation_counts.sum()\n ),\n \"discarded_old_targets\": int(\n target_partition.window_starts.sum()\n ),\n \"target_users\": len(eligibility.uids),\n \"runtime_seconds\": round(time.monotonic() - started, 3),\n \"config\": asdict(config),\n },\n indent=2,\n )\n )\n return 0\n\n model, training_history = train_model(\n public,\n args.stage,\n prepared,\n config,\n device,\n args.checkpoint_dir.resolve() if args.checkpoint_dir else None,\n args.resume_checkpoint.resolve() if args.resume_checkpoint else None,\n )\n ranking, embedding_diagnostics = rank_candidates(\n model,\n prepared,\n eligibility,\n config,\n device,\n args.ranking_batch_size,\n )\n write_ranking(ranking, args.output.resolve())\n print(\n json.dumps(\n {\n \"baseline\": \"yambda-windowed-sequence-two-tower-base\",\n \"stage\": args.stage,\n diagnostics: dict[str, float],\n optimizer: torch.optim.Optimizer,\n generator: torch.Generator,\n) -> Path:\n checkpoint_dir.mkdir(parents=True, exist_ok=True)\n path = checkpoint_dir / f\"epoch-{epoch:02d}.pt\"\n torch.save(\n {\n \"epoch\": epoch,\n \"config\": asdict(config),\n \"diagnostics\": diagnostics,\n \"model_state\": model_state,\n \"optimizer_state\": optimizer.state_dict(),\n \"generator_state\": generator.get_state(),\n },\n path,\n )\n log(f\"saved epoch checkpoint: {path}\")\n return path\n\n\ndef train_model(\n public: Path,\n stage: str,\n prepared: SequenceData,\n config: Config,\n device: torch.device,\n checkpoint_dir: Path | None,\n resume_checkpoint: Path | None,\n) -> tuple[base.SequentialTwoTower, list[dict[str, float]]]:\n partition = chronological_target_partition(\n prepared,\n config.validation_fraction,\n config.max_train_targets_per_user,\n config.max_validation_targets_per_user,\n )\n training_counts = partition.training_counts\n validation_counts = partition.validation_counts\n log(\n f\"chronological BCE window: train={int(training_counts.sum()):,} \"\n f\"validation={int(validation_counts.sum()):,} \"\n f\"selected={partition.selected_count:,} \"\n f\"discarded_old_targets={int(partition.window_starts.sum()):,} \"\n f\"all={prepared.target_count:,}\"\n )\n model = base.SequentialTwoTower(prepared.item_features, model_config(config)).to(\n device\n )\n optimizer = torch.optim.AdamW(\n model.parameters(), lr=config.learning_rate, weight_decay=config.weight_decay\n )\n generator = torch.Generator(device=\"cpu\").manual_seed(config.seed + 10_000)\n item_correction = build_item_correction(\n prepared,\n config,\n partition.window_starts,\n training_counts,\n )\n target_weights = build_target_weights(\n public,\n stage,\n prepared,\n partition.history_training_counts,\n config,\n )\n known_positive_packed = (\n build_known_positive_filter(prepared, partition.history_training_counts)\n if config.negative_filter_scope == \"full-training-history\"\n else None\n )\n history: list[dict[str, float]] = []\n best_loss = float(\"inf\")\n best_epoch = 0\n best_state: dict[str, torch.Tensor] | None = None\n epochs_without_improvement = 0\n start_epoch = 0\n\n if resume_checkpoint is not None:\n payload = torch.load(\n resume_checkpoint, map_location=device, weights_only=False\n )\n stored_config = payload.get(\"config\", {})\n architecture_fields = (\n \"bptt_steps\",\n \"embedding_dim\",\n \"hidden_dim\",\n \"user_hidden_1\",\n \"user_hidden_2\",\n \"item_hidden_1\",\n \"item_hidden_2\",\n )\n mismatches = {\n name: (stored_config.get(name), getattr(config, name))\n for name in architecture_fields\n if stored_config.get(name) != getattr(config, name)\n }\n if mismatches:\n raise RuntimeError(\n f\"resume checkpoint architecture differs: {mismatches}\"\n )\n stored_filter_scope = stored_config.get(\n \"negative_filter_scope\", \"last-100\"\n )\n stored_target_weighting = stored_config.get(\n \"target_weighting\", \"uniform\"\n )\n stored_weight_values = (\n stored_config.get(\"ordinary_target_weight\", 1.0),\n stored_config.get(\"organic_strong_target_weight\", 1.0),\n stored_config.get(\"organic_like_target_weight\", 3.0),\n stored_config.get(\"discovery_horizon_days\", 7),\n )\n current_weight_values = (\n config.ordinary_target_weight,\n config.organic_strong_target_weight,\n config.organic_like_target_weight,\n config.discovery_horizon_days,\n )\n stored_window_values = (\n stored_config.get(\"validation_fraction\", 0.10),\n stored_config.get(\"max_train_targets_per_user\", 0),\n stored_config.get(\"max_validation_targets_per_user\", 0),\n )\n current_window_values = (\n config.validation_fraction,\n config.max_train_targets_per_user,\n config.max_validation_targets_per_user,\n )\n objective_changed = (\n stored_filter_scope != config.negative_filter_scope\n or stored_target_weighting != config.target_weighting\n or stored_weight_values != current_weight_values\n or stored_window_values != current_window_values\n )\n model.load_state_dict(payload[\"model_state\"])\n if \"optimizer_state\" in payload and not objective_changed:\n optimizer.load_state_dict(payload[\"optimizer_state\"])\n for group in optimizer.param_groups:\n group[\"lr\"] = config.learning_rate\n group[\"weight_decay\"] = config.weight_decay\n log(f\"restored optimizer state from {resume_checkpoint}\")\n else:\n log(\n f\"using a fresh AdamW optimizer after loading {resume_checkpoint}\"\n )\n if \"generator_state\" in payload and not objective_changed:\n generator.set_state(payload[\"generator_state\"])\n start_epoch = int(payload[\"epoch\"])\n if start_epoch >= config.epochs:\n raise RuntimeError(\n f\"resume epoch {start_epoch} must be below --epochs {config.epochs}\"\n )\n prior = {\n str(name): float(value)\n for name, value in payload.get(\"diagnostics\", {}).items()\n }\n if objective_changed:\n prior.pop(\"validation_loss\", None)\n log(\n \"BCE objective changed: filter \"\n f\"{stored_filter_scope}->{config.negative_filter_scope}, \"\n \"target weighting \"\n f\"{stored_target_weighting}->{config.target_weighting}, \"\n f\"target window {stored_window_values}->{current_window_values}; \"\n \"recomputing the resume checkpoint's validation BCE\"\n )\n if \"validation_loss\" not in prior:\n prior.update(\n evaluate_validation_bce(\n model,\n prepared,\n config,\n target_weights,\n partition.window_starts,\n training_counts,\n validation_counts,\n item_correction,\n known_positive_packed,\n device,\n )\n )\n history.append(prior)\n best_loss = float(prior[\"validation_loss\"])\n best_epoch = start_epoch\n best_state = snapshot_model(model)\n log(\n f\"resumed validation-BCE training: epoch={start_epoch} \"\n f\"loss={best_loss:.6f}\"\n )\n\n for epoch in range(start_epoch + 1, config.epochs + 1):\n diagnostics = run_training_epoch(\n model,\n optimizer,\n prepared,\n config,\n target_weights,\n partition.window_starts,\n training_counts,\n item_correction,\n known_positive_packed,\n generator,\n device,\n epoch,\n )\n validation = evaluate_validation_bce(\n model,\n prepared,\n config,\n target_weights,\n partition.window_starts,\n training_counts,\n validation_counts,\n item_correction,\n known_positive_packed,\n device,\n )\n diagnostics.update(validation)\n history.append(diagnostics)\n current_state = snapshot_model(model)\n if checkpoint_dir is not None:\n save_epoch_checkpoint(\n checkpoint_dir,\n epoch,\n current_state,\n config,\n diagnostics,\n optimizer,\n generator,\n )\n improved = (\n diagnostics[\"validation_loss\"]\n < best_loss - config.early_stopping_min_delta\n )\n if improved:\n best_loss = diagnostics[\"validation_loss\"]\n best_epoch = epoch\n best_state = current_state\n epochs_without_improvement = 0\n if checkpoint_dir is not None:\n torch.save(\n {\n \"epoch\": epoch,\n \"config\": asdict(config),\n \"diagnostics\": diagnostics,\n \"model_state\": current_state,\n \"optimizer_state\": optimizer.state_dict(),\n \"generator_state\": generator.get_state(),\n },\n checkpoint_dir / \"best.pt\",\n )\n else:\n epochs_without_improvement += 1\n log(\n f\"epoch={epoch}/{config.epochs} train_loss={diagnostics['loss']:.5f} \"\n f\"validation_loss={diagnostics['validation_loss']:.5f} \"\n f\"pair_accuracy={diagnostics['pair_accuracy']:.3f} \"\n f\"train_targets={int(diagnostics['targets']):,} \"\n f\"validation_targets={int(diagnostics['validation_targets']):,} \"\n f\"train_seconds={diagnostics['seconds']:.1f} \"\n f\"validation_seconds={diagnostics['validation_seconds']:.1f}\"\n )\n release_accelerator_cache(device)\n if epochs_without_improvement >= config.early_stopping_patience:\n log(\n f\"early stopping after epoch={epoch}; \"\n f\"best_epoch={best_epoch} best_validation_loss={best_loss:.6f}\"\n )\n break\n\n if best_state is None:\n raise RuntimeError(\"training did not produce a valid checkpoint\")\n model.load_state_dict(best_state)\n model.to(device).eval()\n log(\n f\"restored best validation-BCE checkpoint: epoch={best_epoch} \"\n f\"loss={best_loss:.6f}\"\n )\n return model, history\n\n\n@torch.inference_mode()\ndef inference_queries(\n model: base.SequentialTwoTower,\n prepared: SequenceData,\n config: Config,\n device: torch.device,\n) -> torch.Tensor:\n sequence_rows = prepared.inference_user_rows\n torch.mps.empty_cache()\n\n\ndef run_training_epoch(\n model: base.SequentialTwoTower,\n optimizer: torch.optim.Optimizer,\n prepared: SequenceData,\n config: Config,\n target_weights: np.ndarray,\n window_starts: np.ndarray,\n target_counts: np.ndarray,\n item_correction: torch.Tensor,\n known_positive_packed: np.ndarray | None,\n generator: torch.Generator,\n device: torch.device,\n epoch: int,\n) -> dict[str, float]:\n model.train()\n train_users = np.flatnonzero(target_counts > 0)\n sorted_users = train_users[\n np.argsort(target_counts[train_users], kind=\"stable\")\n ]\n groups = [\n sorted_users[start : start + config.user_batch_size]\n for start in range(0, len(sorted_users), config.user_batch_size)\n ]\n group_order = torch.randperm(len(groups), generator=generator).numpy()\n negative_count = config.uniform_negatives + config.inbatch_negatives\n total_loss = 0.0\n total_targets = 0\n total_weight = 0.0\n total_positive = 0.0\n total_negative = 0.0\n total_pair_correct = 0.0\n batches = 0\n started = time.monotonic()\n\n for group_number in group_order:\n users = groups[int(group_number)]\n group_counts = target_counts[users]\n hidden = torch.zeros(\n 1, len(users), config.embedding_dim, device=device\n )\n max_targets = int(group_counts.max())\n for start in range(0, max_targets, config.bptt_steps):\n steps = min(config.bptt_steps, max_targets - start)\n block = build_dense_block(\n prepared,\n target_weights,\n users,\n window_starts[users],\n group_counts,\n np.zeros(len(users), dtype=np.int64),\n start,\n steps,\n config,\n )\n sequence_outputs, _ = event_sequence(\n model,\n torch.from_numpy(block[0].astype(np.int64)).to(device),\n torch.from_numpy(block[1].astype(np.float32)).to(device),\n hidden,\n )\n hidden = torch.stack(\n [\n sequence_outputs[row, count - 1]\n if count\n else hidden[0, row]\n for row, count in enumerate(block[7])\n ]\n ).unsqueeze(0).detach()\n flat_scored = block[5].reshape(-1)\n if not np.any(flat_scored):\n continue\n mask = torch.from_numpy(flat_scored).to(device)\n sequence_vectors = sequence_outputs.reshape(\n -1, config.embedding_dim\n )[mask]\n flat_states = block[3].reshape(-1)[flat_scored]\n positive_items = torch.from_numpy(\n block[4].reshape(-1)[flat_scored].astype(np.int64)\n ).to(device)\n known_history = torch.from_numpy(\n block[6].reshape(-1, config.bptt_steps)[flat_scored].astype(\n np.int64\n )\n ).to(device)\n liked_items = torch.from_numpy(\n prepared.liked_states[flat_states].astype(np.int64)\n ).to(device)\n flat_user_rows = np.broadcast_to(\n users[:, None], block[5].shape\n ).reshape(-1)[flat_scored]\n negatives, negative_weights = draw_training_negatives(\n positive_items,\n known_history,\n liked_items,\n flat_user_rows,\n known_positive_packed,\n prepared,\n config,\n item_correction,\n generator,\n device,\n )\n optimizer.zero_grad(set_to_none=True)\n statistics, sequence_gradient = score_target_microbatches(\n model,\n prepared,\n config,\n sequence_vectors,\n flat_states,\n block[2].reshape(-1, 4)[flat_scored].astype(np.float32),\n positive_items,\n negatives,\n negative_weights,\n block[8].reshape(-1)[flat_scored],\n device,\n backward=True,\n )\n if sequence_gradient is None:\n raise RuntimeError(\"training block omitted its sequence gradient\")\n sequence_vectors.backward(sequence_gradient)\n nn.utils.clip_grad_norm_(model.parameters(), config.gradient_clip)\n optimizer.step()\n\n count = len(positive_items)\n total_loss += statistics[\"loss_sum\"]\n total_targets += count\n total_weight += statistics[\"weight_sum\"]\n total_positive += statistics[\"positive_sum\"]\n total_negative += statistics[\"negative_sum\"]\n total_pair_correct += statistics[\"pair_correct\"]\n batches += 1\n if batches % 25 == 0:\n release_accelerator_cache(device)\n if batches % 250 == 0:\n batch_bce = statistics[\"loss_sum\"] / max(\n statistics[\"weight_sum\"], 1.0e-12\n )\n running_bce = total_loss / max(total_weight, 1.0e-12)\n log(\n f\"epoch={epoch}/{config.epochs} batches={batches:,} \"\n f\"targets={total_targets:,} \"\n f\"batch_bce={batch_bce:.6f} \"\n f\"running_bce={running_bce:.6f} \"\n f\"elapsed={time.monotonic() - started:.1f}s\"\n )\n\n return {\n \"epoch\": float(epoch),\n \"loss\": total_loss / max(total_weight, 1.0e-12),\n \"positive_logit\": total_positive / max(total_targets, 1),\n \"negative_logit\": total_negative / max(total_targets * negative_count, 1),\n \"pair_accuracy\": total_pair_correct\n / max(total_targets * negative_count, 1),\n \"targets\": float(total_targets),\n \"target_weight_sum\": total_weight,\n \"batches\": float(batches),\n \"seconds\": time.monotonic() - started,\n }\n\n\n@torch.no_grad()\ndef evaluate_validation_bce(\n model: base.SequentialTwoTower,\n prepared: SequenceData,\n config: Config,\n target_weights: np.ndarray,\n window_starts: np.ndarray,\n training_counts: np.ndarray,\n validation_counts: np.ndarray,\n item_correction: torch.Tensor,\n known_positive_packed: np.ndarray | None,\n device: torch.device,\n) -> dict[str, float]:\n \"\"\"Measure deterministic BCE on each user's chronological target suffix.\"\"\"\n model.eval()\n totals = training_counts + validation_counts\n users_with_validation = np.flatnonzero(validation_counts > 0)\n ordered = users_with_validation[\n np.argsort(totals[users_with_validation], kind=\"stable\")\n ]\n groups = [\n ordered[start : start + config.user_batch_size]\n for start in range(0, len(ordered), config.user_batch_size)\n ]\n generator = torch.Generator(device=\"cpu\").manual_seed(config.seed + 20_000)\n total_loss = 0.0\n total_targets = 0\n total_weight = 0.0\n started = time.monotonic()\n\n for group_number, users in enumerate(groups, start=1):\n group_totals = totals[users]\n group_starts = training_counts[users]\n hidden = torch.zeros(\n 1, len(users), config.embedding_dim, device=device\n )\n max_targets = int(group_totals.max())\n for start in range(0, max_targets, config.bptt_steps):\n steps = min(config.bptt_steps, max_targets - start)\n block = build_dense_block(\n prepared,\n target_weights,\n users,\n window_starts[users],\n group_totals,\n group_starts,\n start,\n steps,\n config,\n )\n sequence_outputs, _ = event_sequence(\n model,\n torch.from_numpy(block[0].astype(np.int64)).to(device),\n torch.from_numpy(block[1].astype(np.float32)).to(device),\n hidden,\n )\n hidden = torch.stack(\n [\n sequence_outputs[row, count - 1]\n if count\n else hidden[0, row]\n for row, count in enumerate(block[7])\n ]\n ).unsqueeze(0)\n flat_scored = block[5].reshape(-1)\n if not np.any(flat_scored):\n continue\n mask = torch.from_numpy(flat_scored).to(device)\n sequence_vectors = sequence_outputs.reshape(\n -1, config.embedding_dim\n )[mask]\n flat_states = block[3].reshape(-1)[flat_scored]\n positive_items = torch.from_numpy(\n block[4].reshape(-1)[flat_scored].astype(np.int64)\n ).to(device)\n known_history = torch.from_numpy(\n block[6].reshape(-1, config.bptt_steps)[flat_scored].astype(\n np.int64\n )\n ).to(device)\n liked_items = torch.from_numpy(\n prepared.liked_states[flat_states].astype(np.int64)\n ).to(device)\n flat_user_rows = np.broadcast_to(\n users[:, None], block[5].shape\n ).reshape(-1)[flat_scored]\n negatives, negative_weights = draw_training_negatives(\n positive_items,\n known_history,\n liked_items,\n flat_user_rows,\n known_positive_packed,\n prepared,\n config,\n item_correction,\n generator,\n device,\n )\n statistics, _ = score_target_microbatches(\n model,\n prepared,\n config,\n sequence_vectors,\n flat_states,\n block[2].reshape(-1, 4)[flat_scored].astype(np.float32),\n positive_items,\n negatives,\n negative_weights,\n block[8].reshape(-1)[flat_scored],\n device,\n backward=False,\n )\n total_loss += statistics[\"loss_sum\"]\n total_targets += len(positive_items)\n total_weight += statistics[\"weight_sum\"]\n if group_number % 25 == 0:\n release_accelerator_cache(device)\n return {\n \"validation_loss\": total_loss / max(total_weight, 1.0e-12),\n \"validation_targets\": float(total_targets),\n \"validation_target_weight_sum\": total_weight,\n \"validation_seconds\": time.monotonic() - started,\n }\n\n\ndef snapshot_model(model: nn.Module) -> dict[str, torch.Tensor]:\n return {\n name: value.detach().cpu().clone()\n for name, value in model.state_dict().items()\n }\n\n\ndef save_epoch_checkpoint(\n checkpoint_dir: Path,\n epoch: int,\n model_state: dict[str, torch.Tensor],\n config: Config,\n diagnostics: dict[str, float],\n optimizer: torch.optim.Optimizer,\n generator: torch.Generator,\n) -> Path:\n checkpoint_dir.mkdir(parents=True, exist_ok=True)\n path = checkpoint_dir / f\"epoch-{epoch:02d}.pt\"\n torch.save(\n {\n \"epoch\": epoch,\n \"config\": asdict(config),\n \"diagnostics\": diagnostics,\n" } } ## Step 11 · agent · 2026-09-24T22:49:20.494Z ### Thinking unavailable — no text recorded The baseline already avoids one common failure mode—sampling tracks from a user’s known positive history—but its 24 catalog-uniform negatives are overwhelmingly easy, while only eight in-batch negatives approximate exposed/popular confusions. I’m timing and scoring the exact baseline now so changes are measured against the real public gate. ### exec · 2026-09-24T22:49:23.749Z { "code": "const r = await tools.exec_command({\"cmd\":\"python /app/solution/train_current_model.py --stage validation --public-dir /task --prepared-cache /app/solution/validation_sequences.npz --checkpoint-dir /app/solution/baseline_checkpoints --output /app/solution/production_validation.parquet 2>&1 | tee /app/solution/baseline_validation.log\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":12000}); text(JSON.stringify(r));" } ### Tool output · 2026-09-24T22:49:23.965Z Script completed Wall time 0.2 seconds Output: {"chunk_id":"0f43ce","wall_time_seconds":0.046933045,"exit_code":0,"original_token_count":43,"output":"Traceback (most recent call last):\n File \"/app/solution/train_current_model.py\", line 28, in \n import numpy as np\nModuleNotFoundError: No module named 'numpy'\n"} ### Harness context { "type": "token_usage_record", "thread_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "session_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "root_turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "response_id": "resp_08539f19231b9e9f016ab5a8ef0f8087d291635750e3d081dc", "usage": { "input_tokens": 42776, "cached_input_tokens": 37632, "cache_write_input_tokens": 0, "output_tokens": 224, "reasoning_output_tokens": 39, "total_tokens": 43000 }, "turn_token_usage": { "input_tokens": 145009, "cached_input_tokens": 112768, "cache_write_input_tokens": 0, "output_tokens": 1033, "reasoning_output_tokens": 267, "total_tokens": 146042 }, "thread_token_usage": { "input_tokens": 145009, "cached_input_tokens": 112768, "cache_write_input_tokens": 0, "output_tokens": 1033, "reasoning_output_tokens": 267, "total_tokens": 146042 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-0eec587f-be2b-4c38-a07d-0152e64942af", "process_id": "5288", "command": [ "/bin/sh", "-lc", "python /app/solution/train_current_model.py --stage validation --public-dir /task --prepared-cache /app/solution/validation_sequences.npz --checkpoint-dir /app/solution/baseline_checkpoints --output /app/solution/production_validation.parquet 2>&1 | tee /app/solution/baseline_validation.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "python /app/solution/train_current_model.py --stage validation --public-dir /task --prepared-cache /app/solution/validation_sequences.npz --checkpoint-dir /app/solution/baseline_checkpoints --output /app/solution/production_validation.parquet 2>&1 | tee /app/solution/baseline_validation.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "Traceback (most recent call last):\n File \"/app/solution/train_current_model.py\", line 28, in \n import numpy as np\nModuleNotFoundError: No module named 'numpy'\n", "stderr": "", "aggregated_output": "Traceback (most recent call last):\n File \"/app/solution/train_current_model.py\", line 28, in \n import numpy as np\nModuleNotFoundError: No module named 'numpy'\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 46878014 }, "formatted_output": "Traceback (most recent call last):\n File \"/app/solution/train_current_model.py\", line 28, in \n import numpy as np\nModuleNotFoundError: No module named 'numpy'\n" } } ## Step 12 · agent · 2026-09-24T22:49:25.749Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T22:49:27.892Z { "code": "const r = await tools.exec_command({\"cmd\":\"command -v python; python --version; command -v python3; python3 --version; find / -path '*/site-packages/numpy' -type d 2>/dev/null | head -20; find / -type f -name python -perm -111 2>/dev/null | head -40; sed -n '1,160p' /task/requirements.txt\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":12000}); text(r.output);" } ### Tool output · 2026-09-24T22:49:28.756Z Script completed Wall time 0.9 seconds Output: /usr/local/bin/python Python 3.12.13 /usr/local/bin/python3 Python 3.12.13 /opt/task-venv/lib/python3.12/site-packages/numpy # Exact distribution inventory for the Yambda two-tower task family. # # This is an input to `uv pip sync`, not a list of minimum requirements. A # conforming task environment contains exactly these third-party Python # distributions and no others. Python itself is pinned by task.toml and both # Harbor image Dockerfiles. attrs==26.1.0 bleach==6.4.0 certifi==2026.7.22 chardet==7.6.0 charset-normalizer==3.5.1 fastjsonschema==2.22.2 filelock==3.32.3 fsspec==2026.7.0 idna==3.18 Jinja2==3.1.6 joblib==1.5.3 jsonschema==4.26.0 jsonschema-specifications==2025.9.1 jupyter-core==5.9.1 jupytext==1.19.5 kaggle==2.2.4 kagglesdk==0.1.37 markdown-it-py==4.2.0 MarkupSafe==3.0.3 mdit-py-plugins==0.6.1 mdurl==0.1.2 mpmath==1.3.0 nbformat==5.11.1 networkx==3.6.1 numpy==2.2.6 packaging==26.3 pandas==2.3.3 pillow==12.3.0 pip==26.0.1 platformdirs==4.11.3 protobuf==7.35.1 pyarrow==18.1.0 python-dateutil==2.9.0.post0 python-dotenv==1.2.3 python-slugify==8.0.4 pytz==2026.3.post1 PyYAML==6.0.3 referencing==0.37.0 reportlab==4.2.5 requests==2.34.2 rpds-py==2026.6.3 scikit-learn==1.7.2 scipy==1.15.3 setuptools==84.0.0 six==1.17.0 sympy==1.14.0 text-unidecode==1.3 threadpoolctl==3.6.0 torch==2.8.0 tqdm==4.70.0 traitlets==5.16.1 typing-extensions==4.16.0 tzdata==2026.3 urllib3==2.7.0 webencodings==0.6.1 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "session_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "root_turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "response_id": "resp_08539f19231b9e9f016ab5a8f42e2887d284092d3e83f84b2b", "usage": { "input_tokens": 43105, "cached_input_tokens": 42624, "cache_write_input_tokens": 0, "output_tokens": 180, "reasoning_output_tokens": 52, "total_tokens": 43285 }, "turn_token_usage": { "input_tokens": 188114, "cached_input_tokens": 155392, "cache_write_input_tokens": 0, "output_tokens": 1213, "reasoning_output_tokens": 319, "total_tokens": 189327 }, "thread_token_usage": { "input_tokens": 188114, "cached_input_tokens": 155392, "cache_write_input_tokens": 0, "output_tokens": 1213, "reasoning_output_tokens": 319, "total_tokens": 189327 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-9c38fe20-d287-4b89-8085-614daa8f7996", "process_id": "62506", "command": [ "/bin/sh", "-lc", "command -v python; python --version; command -v python3; python3 --version; find / -path '*/site-packages/numpy' -type d 2>/dev/null | head -20; find / -type f -name python -perm -111 2>/dev/null | head -40; sed -n '1,160p' /task/requirements.txt" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "command -v python; python --version; command -v python3; python3 --version; find / -path '*/site-packages/numpy' -type d 2>/dev/null | head -20; find / -type f -name python -perm -111 2>/dev/null | head -40; sed -n '1,160p' /task/requirements.txt" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "/opt/task-venv/lib/python3.12/site-packages/numpy\n# Exact distribution inventory for the Yambda two-tower task family.\n#\n# This is an input to `uv pip sync`, not a list of minimum requirements. A\n# conforming task environment contains exactly these third-party Python\n# distributions and no others. Python itself is pinned by task.toml and both\n# Harbor image Dockerfiles.\nattrs==26.1.0\nbleach==6.4.0\ncertifi==2026.7.22\nchardet==7.6.0\ncharset-normalizer==3.5.1\nfastjsonschema==2.22.2\nfilelock==3.32.3\nfsspec==2026.7.0\nidna==3.18\nJinja2==3.1.6\njoblib==1.5.3\njsonschema==4.26.0\njsonschema-specifications==2025.9.1\njupyter-core==5.9.1\njupytext==1.19.5\nkaggle==2.2.4\nkagglesdk==0.1.37\nmarkdown-it-py==4.2.0\nMarkupSafe==3.0.3\nmdit-py-plugins==0.6.1\nmdurl==0.1.2\nmpmath==1.3.0\nnbformat==5.11.1\nnetworkx==3.6.1\nnumpy==2.2.6\npackaging==26.3\npandas==2.3.3\npillow==12.3.0\npip==26.0.1\nplatformdirs==4.11.3\nprotobuf==7.35.1\npyarrow==18.1.0\npython-dateutil==2.9.0.post0\npython-dotenv==1.2.3\npython-slugify==8.0.4\npytz==2026.3.post1\nPyYAML==6.0.3\nreferencing==0.37.0\nreportlab==4.2.5\nrequests==2.34.2\nrpds-py==2026.6.3\nscikit-learn==1.7.2\nscipy==1.15.3\nsetuptools==84.0.0\nsix==1.17.0\nsympy==1.14.0\ntext-unidecode==1.3\nthreadpoolctl==3.6.0\ntorch==2.8.0\ntqdm==4.70.0\ntraitlets==5.16.1\ntyping-extensions==4.16.0\ntzdata==2026.3\nurllib3==2.7.0\nwebencodings==0.6.1\n", "stderr": "", "aggregated_output": "/opt/task-venv/lib/python3.12/site-packages/numpy\n# Exact distribution inventory for the Yambda two-tower task family.\n#\n# This is an input to `uv pip sync`, not a list of minimum requirements. A\n# conforming task environment contains exactly these third-party Python\n# distributions and no others. Python itself is pinned by task.toml and both\n# Harbor image Dockerfiles.\nattrs==26.1.0\nbleach==6.4.0\ncertifi==2026.7.22\nchardet==7.6.0\ncharset-normalizer==3.5.1\nfastjsonschema==2.22.2\nfilelock==3.32.3\nfsspec==2026.7.0\nidna==3.18\nJinja2==3.1.6\njoblib==1.5.3\njsonschema==4.26.0\njsonschema-specifications==2025.9.1\njupyter-core==5.9.1\njupytext==1.19.5\nkaggle==2.2.4\nkagglesdk==0.1.37\nmarkdown-it-py==4.2.0\nMarkupSafe==3.0.3\nmdit-py-plugins==0.6.1\nmdurl==0.1.2\nmpmath==1.3.0\nnbformat==5.11.1\nnetworkx==3.6.1\nnumpy==2.2.6\npackaging==26.3\npandas==2.3.3\npillow==12.3.0\npip==26.0.1\nplatformdirs==4.11.3\nprotobuf==7.35.1\npyarrow==18.1.0\npython-dateutil==2.9.0.post0\npython-dotenv==1.2.3\npython-slugify==8.0.4\npytz==2026.3.post1\nPyYAML==6.0.3\nreferencing==0.37.0\nreportlab==4.2.5\nrequests==2.34.2\nrpds-py==2026.6.3\nscikit-learn==1.7.2\nscipy==1.15.3\nsetuptools==84.0.0\nsix==1.17.0\nsympy==1.14.0\ntext-unidecode==1.3\nthreadpoolctl==3.6.0\ntorch==2.8.0\ntqdm==4.70.0\ntraitlets==5.16.1\ntyping-extensions==4.16.0\ntzdata==2026.3\nurllib3==2.7.0\nwebencodings==0.6.1\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 695340741 }, "formatted_output": "/opt/task-venv/lib/python3.12/site-packages/numpy\n# Exact distribution inventory for the Yambda two-tower task family.\n#\n# This is an input to `uv pip sync`, not a list of minimum requirements. A\n# conforming task environment contains exactly these third-party Python\n# distributions and no others. Python itself is pinned by task.toml and both\n# Harbor image Dockerfiles.\nattrs==26.1.0\nbleach==6.4.0\ncertifi==2026.7.22\nchardet==7.6.0\ncharset-normalizer==3.5.1\nfastjsonschema==2.22.2\nfilelock==3.32.3\nfsspec==2026.7.0\nidna==3.18\nJinja2==3.1.6\njoblib==1.5.3\njsonschema==4.26.0\njsonschema-specifications==2025.9.1\njupyter-core==5.9.1\njupytext==1.19.5\nkaggle==2.2.4\nkagglesdk==0.1.37\nmarkdown-it-py==4.2.0\nMarkupSafe==3.0.3\nmdit-py-plugins==0.6.1\nmdurl==0.1.2\nmpmath==1.3.0\nnbformat==5.11.1\nnetworkx==3.6.1\nnumpy==2.2.6\npackaging==26.3\npandas==2.3.3\npillow==12.3.0\npip==26.0.1\nplatformdirs==4.11.3\nprotobuf==7.35.1\npyarrow==18.1.0\npython-dateutil==2.9.0.post0\npython-dotenv==1.2.3\npython-slugify==8.0.4\npytz==2026.3.post1\nPyYAML==6.0.3\nreferencing==0.37.0\nreportlab==4.2.5\nrequests==2.34.2\nrpds-py==2026.6.3\nscikit-learn==1.7.2\nscipy==1.15.3\nsetuptools==84.0.0\nsix==1.17.0\nsympy==1.14.0\ntext-unidecode==1.3\nthreadpoolctl==3.6.0\ntorch==2.8.0\ntqdm==4.70.0\ntraitlets==5.16.1\ntyping-extensions==4.16.0\ntzdata==2026.3\nurllib3==2.7.0\nwebencodings==0.6.1\n" } } ## Step 13 · agent · 2026-09-24T22:49:29.966Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T22:49:31.825Z { "code": "const r = await tools.exec_command({\"cmd\":\"ls -la /opt/task-venv/bin | sed -n '1,80p'; /opt/task-venv/bin/python --version; /opt/task-venv/bin/python -c 'import numpy,pandas,torch; print(numpy.__version__,pandas.__version__,torch.__version__)'\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":5000}); text(r.output);" } ### Tool output · 2026-09-24T22:49:35.867Z Script completed Wall time 4.0 seconds Output: total 124 dr-xr-xr-x 1 root root 4096 Sep 24 22:44 . dr-xr-xr-x 1 root root 4096 Sep 24 22:44 .. -r--r--r-- 1 root root 9033 Sep 24 22:44 Activate.ps1 -r--r--r-- 1 root root 2158 Sep 24 22:44 activate -r--r--r-- 1 root root 920 Sep 24 22:44 activate.csh -r--r--r-- 1 root root 2195 Sep 24 22:44 activate.fish -r-xr-xr-x 1 root root 302 Sep 24 22:44 chardetect -r-xr-xr-x 1 root root 304 Sep 24 22:44 dotenv -r-xr-xr-x 1 root root 308 Sep 24 22:44 f2py -r-xr-xr-x 1 root root 299 Sep 24 22:44 idna -r-xr-xr-x 1 root root 297 Sep 24 22:44 isympy -r-xr-xr-x 1 root root 305 Sep 24 22:44 jsonschema -r-xr-xr-x 1 root root 311 Sep 24 22:44 jupyter -r-xr-xr-x 1 root root 311 Sep 24 22:44 jupyter-migrate -r-xr-xr-x 1 root root 316 Sep 24 22:44 jupyter-troubleshoot -r-xr-xr-x 1 root root 344 Sep 24 22:44 jupyter-trust -r-xr-xr-x 1 root root 311 Sep 24 22:44 jupytext -r-xr-xr-x 1 root root 315 Sep 24 22:44 jupytext-config -r-xr-xr-x 1 root root 301 Sep 24 22:44 kaggle -r-xr-xr-x 1 root root 312 Sep 24 22:44 markdown-it -r-xr-xr-x 1 root root 325 Sep 24 22:44 normalizer -r-xr-xr-x 1 root root 308 Sep 24 22:44 numpy-config -r-xr-xr-x 1 root root 313 Sep 24 22:44 pip -r-xr-xr-x 1 root root 313 Sep 24 22:44 pip3 -r-xr-xr-x 1 root root 313 Sep 24 22:44 pip3.12 lrwxrwxrwx 1 root root 21 Sep 24 22:44 python -> /usr/local/bin/python lrwxrwxrwx 1 root root 6 Sep 24 22:44 python3 -> python lrwxrwxrwx 1 root root 6 Sep 24 22:44 python3.12 -> python -r-xr-xr-x 1 root root 307 Sep 24 22:44 slugify -r-xr-xr-x 1 root root 321 Sep 24 22:44 torchfrtrace -r-xr-xr-x 1 root root 312 Sep 24 22:44 torchrun -r-xr-xr-x 1 root root 299 Sep 24 22:44 tqdm Python 3.12.13 2.2.6 2.3.3 2.8.0+cpu ### Harness context { "type": "token_usage_record", "thread_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "session_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "root_turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "response_id": "resp_08539f19231b9e9f016ab5a8f8ef8887d2ad665682d0109cfe", "usage": { "input_tokens": 43944, "cached_input_tokens": 42880, "cache_write_input_tokens": 0, "output_tokens": 129, "reasoning_output_tokens": 17, "total_tokens": 44073 }, "turn_token_usage": { "input_tokens": 232058, "cached_input_tokens": 198272, "cache_write_input_tokens": 0, "output_tokens": 1342, "reasoning_output_tokens": 336, "total_tokens": 233400 }, "thread_token_usage": { "input_tokens": 232058, "cached_input_tokens": 198272, "cache_write_input_tokens": 0, "output_tokens": 1342, "reasoning_output_tokens": 336, "total_tokens": 233400 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-7aca676a-b7ba-4b42-a4c9-54191ab3a1c8", "process_id": "55902", "command": [ "/bin/sh", "-lc", "ls -la /opt/task-venv/bin | sed -n '1,80p'; /opt/task-venv/bin/python --version; /opt/task-venv/bin/python -c 'import numpy,pandas,torch; print(numpy.__version__,pandas.__version__,torch.__version__)'" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "ls -la /opt/task-venv/bin | sed -n '1,80p'; /opt/task-venv/bin/python --version; /opt/task-venv/bin/python -c 'import numpy,pandas,torch; print(numpy.__version__,pandas.__version__,torch.__version__)'" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "2.2.6 2.3.3 2.8.0+cpu\n", "stderr": "", "aggregated_output": "2.2.6 2.3.3 2.8.0+cpu\n", "exit_code": 0, "duration": { "secs": 3, "nanos": 874673284 }, "formatted_output": "2.2.6 2.3.3 2.8.0+cpu\n" } } ## Step 14 · agent · 2026-09-24T22:49:39.095Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T22:49:41.113Z { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python /app/solution/train_current_model.py --stage validation --public-dir /task --prepared-cache /app/solution/validation_sequences.npz --checkpoint-dir /app/solution/baseline_checkpoints --output /app/solution/production_validation.parquet 2>&1 | tee /app/solution/baseline_validation.log\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":12000}); text(JSON.stringify(r));" } ### Tool output · 2026-09-24T22:49:42.279Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"9e1eb7","wall_time_seconds":1.001086503,"session_id":10359,"original_token_count":0,"output":""} ### Harness context { "type": "token_usage_record", "thread_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "session_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "root_turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "response_id": "resp_08539f19231b9e9f016ab5a9001bf887d29b1e5199a84d5dfd", "usage": { "input_tokens": 44886, "cached_input_tokens": 43776, "cache_write_input_tokens": 0, "output_tokens": 156, "reasoning_output_tokens": 34, "total_tokens": 45042 }, "turn_token_usage": { "input_tokens": 276944, "cached_input_tokens": 242048, "cache_write_input_tokens": 0, "output_tokens": 1498, "reasoning_output_tokens": 370, "total_tokens": 278442 }, "thread_token_usage": { "input_tokens": 276944, "cached_input_tokens": 242048, "cache_write_input_tokens": 0, "output_tokens": 1498, "reasoning_output_tokens": 370, "total_tokens": 278442 } } ## Step 15 · agent · 2026-09-24T22:49:43.452Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T22:49:44.140Z { "code": "const r = await tools.write_stdin({\"session_id\":10359,\"chars\":\"\",\"yield_time_ms\":10000,\"max_output_tokens\":12000}); text(r.output);\n" } ### Tool output · 2026-09-24T22:49:54.148Z Script completed Wall time 10.0 seconds Output: loaded explicit events: rows=720,270 users=8,220 prepared users=1,000 tokens=2,090,746 states=35,727 elapsed=4.8s ### Harness context { "type": "token_usage_record", "thread_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "session_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "root_turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "response_id": "resp_08539f19231b9e9f016ab5a90685dc87d290af20ca9bdb306a", "usage": { "input_tokens": 45101, "cached_input_tokens": 44672, "cache_write_input_tokens": 0, "output_tokens": 62, "reasoning_output_tokens": 15, "total_tokens": 45163 }, "turn_token_usage": { "input_tokens": 322045, "cached_input_tokens": 286720, "cache_write_input_tokens": 0, "output_tokens": 1560, "reasoning_output_tokens": 385, "total_tokens": 323605 }, "thread_token_usage": { "input_tokens": 322045, "cached_input_tokens": 286720, "cache_write_input_tokens": 0, "output_tokens": 1560, "reasoning_output_tokens": 385, "total_tokens": 323605 } } ## Step 16 · agent · 2026-09-24T22:49:55.420Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T22:49:56.118Z { "code": "const r = await tools.write_stdin({\"session_id\":10359,\"chars\":\"\",\"yield_time_ms\":20000,\"max_output_tokens\":12000}); text(r.output);\n" } ### Tool output · 2026-09-24T22:50:16.127Z Script completed Wall time 20.0 seconds Output: prepared users=2,000 tokens=4,301,266 states=71,242 elapsed=9.9s prepared users=3,000 tokens=6,486,146 states=109,471 elapsed=15.1s prepared users=4,000 tokens=8,793,043 states=148,210 elapsed=20.5s prepared users=5,000 tokens=10,992,205 states=182,596 elapsed=25.3s ### Harness context { "type": "token_usage_record", "thread_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "session_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "root_turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "response_id": "resp_08539f19231b9e9f016ab5a912534487d28f94c8149edb4f11", "usage": { "input_tokens": 45226, "cached_input_tokens": 44928, "cache_write_input_tokens": 0, "output_tokens": 65, "reasoning_output_tokens": 18, "total_tokens": 45291 }, "turn_token_usage": { "input_tokens": 367271, "cached_input_tokens": 331648, "cache_write_input_tokens": 0, "output_tokens": 1625, "reasoning_output_tokens": 403, "total_tokens": 368896 }, "thread_token_usage": { "input_tokens": 367271, "cached_input_tokens": 331648, "cache_write_input_tokens": 0, "output_tokens": 1625, "reasoning_output_tokens": 403, "total_tokens": 368896 } } ## Step 17 · agent · 2026-09-24T22:50:17.880Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T22:50:18.565Z { "code": "const r = await tools.write_stdin({\"session_id\":10359,\"chars\":\"\",\"yield_time_ms\":20000,\"max_output_tokens\":12000}); text(r.output);\n" } ### Tool output · 2026-09-24T22:50:38.580Z Script completed Wall time 20.0 seconds Output: prepared users=6,000 tokens=13,061,727 states=217,148 elapsed=30.1s prepared users=7,000 tokens=15,334,418 states=256,914 elapsed=35.6s prepared users=8,000 tokens=17,462,672 states=296,703 elapsed=40.9s prepared users=9,000 tokens=19,568,405 states=331,551 elapsed=45.7s target users without qualifying catalog sequence=7 prepared stage=validation sequence_users=9,135 tokens=20,056,329 next_item_targets=20,047,194 states=339,133 saved public-derived full-sequence cache: /app/solution/validation_sequences.npz chronological BCE window: train=1,052,953 validation=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "session_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "root_turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "response_id": "resp_08539f19231b9e9f016ab5a928569487d2a49108e03962bc7e", "usage": { "input_tokens": 45414, "cached_input_tokens": 45056, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 45467 }, "turn_token_usage": { "input_tokens": 412685, "cached_input_tokens": 376704, "cache_write_input_tokens": 0, "output_tokens": 1678, "reasoning_output_tokens": 409, "total_tokens": 414363 }, "thread_token_usage": { "input_tokens": 412685, "cached_input_tokens": 376704, "cache_write_input_tokens": 0, "output_tokens": 1678, "reasoning_output_tokens": 409, "total_tokens": 414363 } } ## Step 18 · agent · 2026-09-24T22:50:39.825Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T22:50:40.497Z { "code": "const r = await tools.write_stdin({\"session_id\":10359,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":12000}); text(r.output);\n" } ### Tool output · 2026-09-24T22:51:10.537Z Script completed Wall time 30.0 seconds Output: inbatch correction: min=0.250 mean_on_targets=1.001 max=4.000 target weighting=organic-strong min=0.250 mean=0.575 max=1.000 full-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "session_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "root_turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "response_id": "resp_08539f19231b9e9f016ab5a93ec38c87d297cff0886c6da8ae", "usage": { "input_tokens": 45690, "cached_input_tokens": 45184, "cache_write_input_tokens": 0, "output_tokens": 67, "reasoning_output_tokens": 20, "total_tokens": 45757 }, "turn_token_usage": { "input_tokens": 458375, "cached_input_tokens": 421888, "cache_write_input_tokens": 0, "output_tokens": 1745, "reasoning_output_tokens": 429, "total_tokens": 460120 }, "thread_token_usage": { "input_tokens": 458375, "cached_input_tokens": 421888, "cache_write_input_tokens": 0, "output_tokens": 1745, "reasoning_output_tokens": 429, "total_tokens": 460120 } } ## Step 19 · agent · 2026-09-24T22:51:11.586Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T22:51:12.281Z { "code": "const r = await tools.write_stdin({\"session_id\":10359,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":12000}); text(r.output);\n" } ### Tool output · 2026-09-24T22:51:42.307Z Script completed Wall time 30.0 seconds Output: ### Harness context { "type": "token_usage_record", "thread_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "session_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "root_turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "response_id": "resp_08539f19231b9e9f016ab5a95eb69487d2aede8b6b6ab870d8", "usage": { "input_tokens": 45852, "cached_input_tokens": 45568, "cache_write_input_tokens": 0, "output_tokens": 56, "reasoning_output_tokens": 9, "total_tokens": 45908 }, "turn_token_usage": { "input_tokens": 504227, "cached_input_tokens": 467456, "cache_write_input_tokens": 0, "output_tokens": 1801, "reasoning_output_tokens": 438, "total_tokens": 506028 }, "thread_token_usage": { "input_tokens": 504227, "cached_input_tokens": 467456, "cache_write_input_tokens": 0, "output_tokens": 1801, "reasoning_output_tokens": 438, "total_tokens": 506028 } } ## Step 20 · agent · 2026-09-24T22:51:43.827Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T22:51:44.503Z { "code": "const r = await tools.write_stdin({\"session_id\":10359,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":12000}); text(r.output);\n" } ### Tool output · 2026-09-24T22:52:14.550Z Script completed Wall time 30.0 seconds Output: ### Harness context { "type": "token_usage_record", "thread_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "session_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "root_turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "response_id": "resp_08539f19231b9e9f016ab5a97eadf887d2b2aa3f32f5c381d0", "usage": { "input_tokens": 45931, "cached_input_tokens": 45696, "cache_write_input_tokens": 0, "output_tokens": 71, "reasoning_output_tokens": 24, "total_tokens": 46002 }, "turn_token_usage": { "input_tokens": 550158, "cached_input_tokens": 513152, "cache_write_input_tokens": 0, "output_tokens": 1872, "reasoning_output_tokens": 462, "total_tokens": 552030 }, "thread_token_usage": { "input_tokens": 550158, "cached_input_tokens": 513152, "cache_write_input_tokens": 0, "output_tokens": 1872, "reasoning_output_tokens": 462, "total_tokens": 552030 } } ## Step 21 · agent · 2026-09-24T22:52:15.907Z ### Thinking unavailable — no text recorded Data preparation found about 1.05M retained training targets across 9,135 sequence users; the baseline discards older targets by design and holds out 130k chronological targets internally. The expensive reusable sequence cache is built, so subsequent objective experiments won’t repeat the 20M-event preprocessing pass. ## Step 22 · agent · 2026-09-24T22:52:17.354Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T22:52:18.057Z { "code": "const r = await tools.write_stdin({\"session_id\":10359,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":12000}); text(r.output);\n" } ### Tool output · 2026-09-24T22:52:48.072Z Script completed Wall time 30.0 seconds Output: ### Harness context { "type": "token_usage_record", "thread_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "session_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "root_turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "response_id": "resp_08539f19231b9e9f016ab5a99ecaec87d28ddac127f8b590d2", "usage": { "input_tokens": 46025, "cached_input_tokens": 45696, "cache_write_input_tokens": 0, "output_tokens": 138, "reasoning_output_tokens": 23, "total_tokens": 46163 }, "turn_token_usage": { "input_tokens": 596183, "cached_input_tokens": 558848, "cache_write_input_tokens": 0, "output_tokens": 2010, "reasoning_output_tokens": 485, "total_tokens": 598193 }, "thread_token_usage": { "input_tokens": 596183, "cached_input_tokens": 558848, "cache_write_input_tokens": 0, "output_tokens": 2010, "reasoning_output_tokens": 485, "total_tokens": 598193 } } ## Step 23 · agent · 2026-09-24T22:52:49.771Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T22:52:51.186Z { "code": "const r = await tools.exec_command({\"cmd\":\"ps -eo pid,etime,pcpu,pmem,rss,cmd | rg 'train_current_model|PID'; du -sh /app/solution/* 2>/dev/null | sort -h\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":5000}); text(r.output);" } ### Tool output · 2026-09-24T22:52:51.327Z Script completed Wall time 0.1 seconds Output: /bin/sh: 1: ps: not found 4.0K /app/solution/baseline_validation.log 16K /app/solution/two_tower_model.py 80K /app/solution/train_current_model.py 511M /app/solution/validation_sequences.npz ### Harness context { "type": "token_usage_record", "thread_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "session_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "root_turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "response_id": "resp_08539f19231b9e9f016ab5a9c045e087d291741240f8555d6e", "usage": { "input_tokens": 46186, "cached_input_tokens": 45824, "cache_write_input_tokens": 0, "output_tokens": 119, "reasoning_output_tokens": 30, "total_tokens": 46305 }, "turn_token_usage": { "input_tokens": 642369, "cached_input_tokens": 604672, "cache_write_input_tokens": 0, "output_tokens": 2129, "reasoning_output_tokens": 515, "total_tokens": 644498 }, "thread_token_usage": { "input_tokens": 642369, "cached_input_tokens": 604672, "cache_write_input_tokens": 0, "output_tokens": 2129, "reasoning_output_tokens": 515, "total_tokens": 644498 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-927a5e53-073f-4c78-a966-f45b524b44c5", "process_id": "90917", "command": [ "/bin/sh", "-lc", "ps -eo pid,etime,pcpu,pmem,rss,cmd | rg 'train_current_model|PID'; du -sh /app/solution/* 2>/dev/null | sort -h" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "ps -eo pid,etime,pcpu,pmem,rss,cmd | rg 'train_current_model|PID'; du -sh /app/solution/* 2>/dev/null | sort -h" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "/bin/sh: 1: ps: not found\n4.0K\t/app/solution/baseline_validation.log\n16K\t/app/solution/two_tower_model.py\n80K\t/app/solution/train_current_model.py\n511M\t/app/solution/validation_sequences.npz\n", "stderr": "", "aggregated_output": "/bin/sh: 1: ps: not found\n4.0K\t/app/solution/baseline_validation.log\n16K\t/app/solution/two_tower_model.py\n80K\t/app/solution/train_current_model.py\n511M\t/app/solution/validation_sequences.npz\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 2230 }, "formatted_output": "/bin/sh: 1: ps: not found\n4.0K\t/app/solution/baseline_validation.log\n16K\t/app/solution/two_tower_model.py\n80K\t/app/solution/train_current_model.py\n511M\t/app/solution/validation_sequences.npz\n" } } ## Step 24 · agent · 2026-09-24T22:52:53.131Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T22:52:54.784Z { "code": "const r = await tools.exec_command({\"cmd\":\"sed -n '1,320p' /task/evaluate_public.py; find /task -maxdepth 2 -type f -not -path '/task/data/*' -not -path '/task/current_model/*' -printf '%p\\\\n'\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":30000}); text(r.output);" } ### Tool output · 2026-09-24T22:52:54.909Z Script completed Wall time 0.1 seconds Output: #!/usr/bin/env python3 """Score a validation-stage ranking against public discovery labels. ``build_task.py`` publishes this module with the solver-visible task files. It loads target users, validation eligibility, and validation-window events from the adjacent ``data`` directory, then evaluates one ranking supplied on the command line. Stage-independent mechanics come from the adjacent public ``ranking_evaluation.py`` module. The builder installs the same checked-in module in the private judge, so public and private scoring mechanics cannot drift. A relevant candidate receives grade 3 for an organic like or grade 1 for an organic listen with at least 80 percent completion; a like takes precedence when both events exist. Before scoring, the ranking must contain exactly 100 distinct eligible items for every target user, with integer-valued ``uid``, ``item_id``, and ``rank`` columns and ranks 1 through 100 exactly once per user. Extra columns are ignored. Quality is graded NDCG@10, averaged only over users who have at least one eligible relevant item in the validation window. The evaluator prints a JSON object containing the model path and scores. It does not apply the task's pass threshold; its purpose is model development and selection. The private judge applies the threshold to the final submission. Usage:: python evaluate_public.py """ from __future__ import annotations import json import sys from pathlib import Path import pandas as pd from ranking_evaluation import ( CandidateSet, RankingValidationError, ranked_items, read_ranking, score_rankings, validate_ranking, ) HERE = Path(__file__).resolve().parent DATA = HERE / "data" LISTEN_STRONG = 80 def validation_truth( eligibility: CandidateSet, targets: set[int] ) -> dict[int, dict[int, int]]: """Build cutoff-correct graded relevance from public validation events. Only target-user events for eligible candidates are retained. Organic listens completed to at least 80 percent receive grade 1, and organic likes receive grade 3. Repeated user-item events are collapsed, and processing likes second ensures that grade 3 wins over grade 1. Args: eligibility: Validation-cutoff candidate membership service. targets: Complete set of target user IDs. Returns: A nested ``uid -> item_id -> grade`` mapping. Users without an eligible relevant event are omitted. """ listens = pd.read_parquet(DATA / "val_listens.parquet") likes = pd.read_parquet(DATA / "val_likes.parquet") strong = listens[ listens.uid.isin(targets) & (listens.is_organic == 1) & (listens.played_ratio_pct >= LISTEN_STRONG) ][["uid", "item_id"]].drop_duplicates() organic_likes = likes[ likes.uid.isin(targets) & (likes.is_organic == 1) ][["uid", "item_id"]].drop_duplicates() graded: dict[int, dict[int, int]] = {} for raw_uid, raw_item in strong.itertuples(index=False, name=None): uid, item = int(raw_uid), int(raw_item) if eligibility.is_eligible(uid, item): graded.setdefault(uid, {})[item] = 1 for raw_uid, raw_item in organic_likes.itertuples(index=False, name=None): uid, item = int(raw_uid), int(raw_item) if eligibility.is_eligible(uid, item): graded.setdefault(uid, {})[item] = 3 return graded def load_submission( path: Path, eligibility: CandidateSet, targets: set[int] ) -> dict[int, list[int]]: """Load and validate a public validation ranking. Files ending in ``.parquet`` are read as parquet; every other suffix is read as CSV. The required columns must be integer-valued, cover exactly the target-user set, contain 100 rows and ranks 1 through 100 per user, avoid duplicate user-item pairs, and contain only cutoff-eligible items. Args: path: Ranking file to read. eligibility: Validation-cutoff candidate membership service. targets: Exact set of users the ranking must cover. Returns: A ``uid -> ranked item IDs`` mapping ordered by ascending rank. Raises: ValueError: If the ranking violates the required schema or invariants. """ frame = validate_ranking(read_ranking(path), targets) if not all( eligibility.is_eligible(uid, item) for uid, item in frame[["uid", "item_id"]].itertuples(index=False, name=None) ): raise RankingValidationError( "submission contains an item rejected by eligibility service" ) return ranked_items(frame) def score( ranked: dict[int, list[int]], graded: dict[int, dict[int, int]], ) -> dict[str, float | int]: """Aggregate per-user NDCG@10 over users with public relevance labels. Args: ranked: Ranked item IDs for every target user. graded: Eligible validation relevance keyed by user and item. Returns: The six-decimal ``quality_ndcg@10`` value and number of scored users. """ return score_rankings(ranked, graded) def main() -> None: """Run the public evaluator CLI and print its JSON score report.""" targets_frame = pd.read_parquet(DATA / "target_users.parquet") targets = set(targets_frame.uid.astype(int)) eligibility = CandidateSet(DATA / "validation_eligible_candidates.npz") graded = validation_truth(eligibility, targets) if len(sys.argv) != 2: raise SystemExit( "usage: evaluate_public.py " ) path = Path(sys.argv[1]) ranked = load_submission(path, eligibility, targets) print(json.dumps({"model": str(path), "scores": score(ranked, graded)}, indent=2)) if __name__ == "__main__": main() /task/requirements.txt /task/evaluate_public.py /task/.keep /task/ranking_evaluation.py ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-9f4a8d9c-cb21-4bfd-9cfb-7d8b2652d2eb", "process_id": "13671", "command": [ "/bin/sh", "-lc", "sed -n '1,320p' /task/evaluate_public.py; find /task -maxdepth 2 -type f -not -path '/task/data/*' -not -path '/task/current_model/*' -printf '%p\\n'" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "read", "cmd": "sed -n '1,320p' /task/evaluate_public.py", "name": "evaluate_public.py", "path": "/task/evaluate_public.py" }, { "type": "search", "cmd": "find /task -maxdepth 2 -type f -not -path '/task/data/*' -not -path '/task/current_model/*' -printf \"%p\\\\n\"", "query": "/task/data/*", "path": "task" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "#!/usr/bin/env python3\n\"\"\"Score a validation-stage ranking against public discovery labels.\n\n``build_task.py`` publishes this module with the solver-visible task files. It\nloads target users, validation eligibility, and validation-window events from\nthe adjacent ``data`` directory, then evaluates one ranking supplied on the\ncommand line. Stage-independent mechanics come from the adjacent public\n``ranking_evaluation.py`` module. The builder installs the same checked-in\nmodule in the private judge, so public and private scoring mechanics cannot\ndrift. A relevant candidate receives grade 3 for an organic like or grade 1 for\nan organic listen with at least 80 percent completion; a like takes precedence\nwhen both events exist.\n\nBefore scoring, the ranking must contain exactly 100 distinct eligible items\nfor every target user, with integer-valued ``uid``, ``item_id``, and ``rank``\ncolumns and ranks 1 through 100 exactly once per user. Extra columns are\nignored. Quality is graded NDCG@10, averaged only over users who have at least\none eligible relevant item in the validation window.\n\nThe evaluator prints a JSON object containing the model path and scores. It\ndoes not apply the task's pass threshold; its purpose is model development and\nselection. The private judge applies the threshold to the final submission.\n\nUsage::\n\n python evaluate_public.py \n\"\"\"\nfrom __future__ import annotations\n\nimport json\nimport sys\nfrom pathlib import Path\n\nimport pandas as pd\n\nfrom ranking_evaluation import (\n CandidateSet,\n RankingValidationError,\n ranked_items,\n read_ranking,\n score_rankings,\n validate_ranking,\n)\n\n\nHERE = Path(__file__).resolve().parent\nDATA = HERE / \"data\"\nLISTEN_STRONG = 80\n\n\ndef validation_truth(\n eligibility: CandidateSet, targets: set[int]\n) -> dict[int, dict[int, int]]:\n \"\"\"Build cutoff-correct graded relevance from public validation events.\n\n Only target-user events for eligible candidates are retained. Organic\n listens completed to at least 80 percent receive grade 1, and organic likes\n receive grade 3. Repeated user-item events are collapsed, and processing\n likes second ensures that grade 3 wins over grade 1.\n\n Args:\n eligibility: Validation-cutoff candidate membership service.\n targets: Complete set of target user IDs.\n\n Returns:\n A nested ``uid -> item_id -> grade`` mapping. Users without an eligible\n relevant event are omitted.\n \"\"\"\n listens = pd.read_parquet(DATA / \"val_listens.parquet\")\n likes = pd.read_parquet(DATA / \"val_likes.parquet\")\n strong = listens[\n listens.uid.isin(targets)\n & (listens.is_organic == 1)\n & (listens.played_ratio_pct >= LISTEN_STRONG)\n ][[\"uid\", \"item_id\"]].drop_duplicates()\n organic_likes = likes[\n likes.uid.isin(targets) & (likes.is_organic == 1)\n ][[\"uid\", \"item_id\"]].drop_duplicates()\n\n graded: dict[int, dict[int, int]] = {}\n for raw_uid, raw_item in strong.itertuples(index=False, name=None):\n uid, item = int(raw_uid), int(raw_item)\n if eligibility.is_eligible(uid, item):\n graded.setdefault(uid, {})[item] = 1\n for raw_uid, raw_item in organic_likes.itertuples(index=False, name=None):\n uid, item = int(raw_uid), int(raw_item)\n if eligibility.is_eligible(uid, item):\n graded.setdefault(uid, {})[item] = 3\n return graded\n\n\ndef load_submission(\n path: Path, eligibility: CandidateSet, targets: set[int]\n) -> dict[int, list[int]]:\n \"\"\"Load and validate a public validation ranking.\n\n Files ending in ``.parquet`` are read as parquet; every other suffix is\n read as CSV. The required columns must be integer-valued, cover exactly the\n target-user set, contain 100 rows and ranks 1 through 100 per user, avoid\n duplicate user-item pairs, and contain only cutoff-eligible items.\n\n Args:\n path: Ranking file to read.\n eligibility: Validation-cutoff candidate membership service.\n targets: Exact set of users the ranking must cover.\n\n Returns:\n A ``uid -> ranked item IDs`` mapping ordered by ascending rank.\n\n Raises:\n ValueError: If the ranking violates the required schema or invariants.\n \"\"\"\n frame = validate_ranking(read_ranking(path), targets)\n if not all(\n eligibility.is_eligible(uid, item)\n for uid, item in frame[[\"uid\", \"item_id\"]].itertuples(index=False, name=None)\n ):\n raise RankingValidationError(\n \"submission contains an item rejected by eligibility service\"\n )\n return ranked_items(frame)\n\n\ndef score(\n ranked: dict[int, list[int]],\n graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n \"\"\"Aggregate per-user NDCG@10 over users with public relevance labels.\n\n Args:\n ranked: Ranked item IDs for every target user.\n graded: Eligible validation relevance keyed by user and item.\n\n Returns:\n The six-decimal ``quality_ndcg@10`` value and number of scored users.\n \"\"\"\n return score_rankings(ranked, graded)\n\n\ndef main() -> None:\n \"\"\"Run the public evaluator CLI and print its JSON score report.\"\"\"\n targets_frame = pd.read_parquet(DATA / \"target_users.parquet\")\n targets = set(targets_frame.uid.astype(int))\n eligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\n graded = validation_truth(eligibility, targets)\n\n if len(sys.argv) != 2:\n raise SystemExit(\n \"usage: evaluate_public.py \"\n )\n path = Path(sys.argv[1])\n ranked = load_submission(path, eligibility, targets)\n print(json.dumps({\"model\": str(path), \"scores\": score(ranked, graded)}, indent=2))\n\n\nif __name__ == \"__main__\":\n main()\n/task/requirements.txt\n/task/evaluate_public.py\n/task/.keep\n/task/ranking_evaluation.py\n", "stderr": "", "aggregated_output": "#!/usr/bin/env python3\n\"\"\"Score a validation-stage ranking against public discovery labels.\n\n``build_task.py`` publishes this module with the solver-visible task files. It\nloads target users, validation eligibility, and validation-window events from\nthe adjacent ``data`` directory, then evaluates one ranking supplied on the\ncommand line. Stage-independent mechanics come from the adjacent public\n``ranking_evaluation.py`` module. The builder installs the same checked-in\nmodule in the private judge, so public and private scoring mechanics cannot\ndrift. A relevant candidate receives grade 3 for an organic like or grade 1 for\nan organic listen with at least 80 percent completion; a like takes precedence\nwhen both events exist.\n\nBefore scoring, the ranking must contain exactly 100 distinct eligible items\nfor every target user, with integer-valued ``uid``, ``item_id``, and ``rank``\ncolumns and ranks 1 through 100 exactly once per user. Extra columns are\nignored. Quality is graded NDCG@10, averaged only over users who have at least\none eligible relevant item in the validation window.\n\nThe evaluator prints a JSON object containing the model path and scores. It\ndoes not apply the task's pass threshold; its purpose is model development and\nselection. The private judge applies the threshold to the final submission.\n\nUsage::\n\n python evaluate_public.py \n\"\"\"\nfrom __future__ import annotations\n\nimport json\nimport sys\nfrom pathlib import Path\n\nimport pandas as pd\n\nfrom ranking_evaluation import (\n CandidateSet,\n RankingValidationError,\n ranked_items,\n read_ranking,\n score_rankings,\n validate_ranking,\n)\n\n\nHERE = Path(__file__).resolve().parent\nDATA = HERE / \"data\"\nLISTEN_STRONG = 80\n\n\ndef validation_truth(\n eligibility: CandidateSet, targets: set[int]\n) -> dict[int, dict[int, int]]:\n \"\"\"Build cutoff-correct graded relevance from public validation events.\n\n Only target-user events for eligible candidates are retained. Organic\n listens completed to at least 80 percent receive grade 1, and organic likes\n receive grade 3. Repeated user-item events are collapsed, and processing\n likes second ensures that grade 3 wins over grade 1.\n\n Args:\n eligibility: Validation-cutoff candidate membership service.\n targets: Complete set of target user IDs.\n\n Returns:\n A nested ``uid -> item_id -> grade`` mapping. Users without an eligible\n relevant event are omitted.\n \"\"\"\n listens = pd.read_parquet(DATA / \"val_listens.parquet\")\n likes = pd.read_parquet(DATA / \"val_likes.parquet\")\n strong = listens[\n listens.uid.isin(targets)\n & (listens.is_organic == 1)\n & (listens.played_ratio_pct >= LISTEN_STRONG)\n ][[\"uid\", \"item_id\"]].drop_duplicates()\n organic_likes = likes[\n likes.uid.isin(targets) & (likes.is_organic == 1)\n ][[\"uid\", \"item_id\"]].drop_duplicates()\n\n graded: dict[int, dict[int, int]] = {}\n for raw_uid, raw_item in strong.itertuples(index=False, name=None):\n uid, item = int(raw_uid), int(raw_item)\n if eligibility.is_eligible(uid, item):\n graded.setdefault(uid, {})[item] = 1\n for raw_uid, raw_item in organic_likes.itertuples(index=False, name=None):\n uid, item = int(raw_uid), int(raw_item)\n if eligibility.is_eligible(uid, item):\n graded.setdefault(uid, {})[item] = 3\n return graded\n\n\ndef load_submission(\n path: Path, eligibility: CandidateSet, targets: set[int]\n) -> dict[int, list[int]]:\n \"\"\"Load and validate a public validation ranking.\n\n Files ending in ``.parquet`` are read as parquet; every other suffix is\n read as CSV. The required columns must be integer-valued, cover exactly the\n target-user set, contain 100 rows and ranks 1 through 100 per user, avoid\n duplicate user-item pairs, and contain only cutoff-eligible items.\n\n Args:\n path: Ranking file to read.\n eligibility: Validation-cutoff candidate membership service.\n targets: Exact set of users the ranking must cover.\n\n Returns:\n A ``uid -> ranked item IDs`` mapping ordered by ascending rank.\n\n Raises:\n ValueError: If the ranking violates the required schema or invariants.\n \"\"\"\n frame = validate_ranking(read_ranking(path), targets)\n if not all(\n eligibility.is_eligible(uid, item)\n for uid, item in frame[[\"uid\", \"item_id\"]].itertuples(index=False, name=None)\n ):\n raise RankingValidationError(\n \"submission contains an item rejected by eligibility service\"\n )\n return ranked_items(frame)\n\n\ndef score(\n ranked: dict[int, list[int]],\n graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n \"\"\"Aggregate per-user NDCG@10 over users with public relevance labels.\n\n Args:\n ranked: Ranked item IDs for every target user.\n graded: Eligible validation relevance keyed by user and item.\n\n Returns:\n The six-decimal ``quality_ndcg@10`` value and number of scored users.\n \"\"\"\n return score_rankings(ranked, graded)\n\n\ndef main() -> None:\n \"\"\"Run the public evaluator CLI and print its JSON score report.\"\"\"\n targets_frame = pd.read_parquet(DATA / \"target_users.parquet\")\n targets = set(targets_frame.uid.astype(int))\n eligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\n graded = validation_truth(eligibility, targets)\n\n if len(sys.argv) != 2:\n raise SystemExit(\n \"usage: evaluate_public.py \"\n )\n path = Path(sys.argv[1])\n ranked = load_submission(path, eligibility, targets)\n print(json.dumps({\"model\": str(path), \"scores\": score(ranked, graded)}, indent=2))\n\n\nif __name__ == \"__main__\":\n main()\n/task/requirements.txt\n/task/evaluate_public.py\n/task/.keep\n/task/ranking_evaluation.py\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 9350 }, "formatted_output": "#!/usr/bin/env python3\n\"\"\"Score a validation-stage ranking against public discovery labels.\n\n``build_task.py`` publishes this module with the solver-visible task files. It\nloads target users, validation eligibility, and validation-window events from\nthe adjacent ``data`` directory, then evaluates one ranking supplied on the\ncommand line. Stage-independent mechanics come from the adjacent public\n``ranking_evaluation.py`` module. The builder installs the same checked-in\nmodule in the private judge, so public and private scoring mechanics cannot\ndrift. A relevant candidate receives grade 3 for an organic like or grade 1 for\nan organic listen with at least 80 percent completion; a like takes precedence\nwhen both events exist.\n\nBefore scoring, the ranking must contain exactly 100 distinct eligible items\nfor every target user, with integer-valued ``uid``, ``item_id``, and ``rank``\ncolumns and ranks 1 through 100 exactly once per user. Extra columns are\nignored. Quality is graded NDCG@10, averaged only over users who have at least\none eligible relevant item in the validation window.\n\nThe evaluator prints a JSON object containing the model path and scores. It\ndoes not apply the task's pass threshold; its purpose is model development and\nselection. The private judge applies the threshold to the final submission.\n\nUsage::\n\n python evaluate_public.py \n\"\"\"\nfrom __future__ import annotations\n\nimport json\nimport sys\nfrom pathlib import Path\n\nimport pandas as pd\n\nfrom ranking_evaluation import (\n CandidateSet,\n RankingValidationError,\n ranked_items,\n read_ranking,\n score_rankings,\n validate_ranking,\n)\n\n\nHERE = Path(__file__).resolve().parent\nDATA = HERE / \"data\"\nLISTEN_STRONG = 80\n\n\ndef validation_truth(\n eligibility: CandidateSet, targets: set[int]\n) -> dict[int, dict[int, int]]:\n \"\"\"Build cutoff-correct graded relevance from public validation events.\n\n Only target-user events for eligible candidates are retained. Organic\n listens completed to at least 80 percent receive grade 1, and organic likes\n receive grade 3. Repeated user-item events are collapsed, and processing\n likes second ensures that grade 3 wins over grade 1.\n\n Args:\n eligibility: Validation-cutoff candidate membership service.\n targets: Complete set of target user IDs.\n\n Returns:\n A nested ``uid -> item_id -> grade`` mapping. Users without an eligible\n relevant event are omitted.\n \"\"\"\n listens = pd.read_parquet(DATA / \"val_listens.parquet\")\n likes = pd.read_parquet(DATA / \"val_likes.parquet\")\n strong = listens[\n listens.uid.isin(targets)\n & (listens.is_organic == 1)\n & (listens.played_ratio_pct >= LISTEN_STRONG)\n ][[\"uid\", \"item_id\"]].drop_duplicates()\n organic_likes = likes[\n likes.uid.isin(targets) & (likes.is_organic == 1)\n ][[\"uid\", \"item_id\"]].drop_duplicates()\n\n graded: dict[int, dict[int, int]] = {}\n for raw_uid, raw_item in strong.itertuples(index=False, name=None):\n uid, item = int(raw_uid), int(raw_item)\n if eligibility.is_eligible(uid, item):\n graded.setdefault(uid, {})[item] = 1\n for raw_uid, raw_item in organic_likes.itertuples(index=False, name=None):\n uid, item = int(raw_uid), int(raw_item)\n if eligibility.is_eligible(uid, item):\n graded.setdefault(uid, {})[item] = 3\n return graded\n\n\ndef load_submission(\n path: Path, eligibility: CandidateSet, targets: set[int]\n) -> dict[int, list[int]]:\n \"\"\"Load and validate a public validation ranking.\n\n Files ending in ``.parquet`` are read as parquet; every other suffix is\n read as CSV. The required columns must be integer-valued, cover exactly the\n target-user set, contain 100 rows and ranks 1 through 100 per user, avoid\n duplicate user-item pairs, and contain only cutoff-eligible items.\n\n Args:\n path: Ranking file to read.\n eligibility: Validation-cutoff candidate membership service.\n targets: Exact set of users the ranking must cover.\n\n Returns:\n A ``uid -> ranked item IDs`` mapping ordered by ascending rank.\n\n Raises:\n ValueError: If the ranking violates the required schema or invariants.\n \"\"\"\n frame = validate_ranking(read_ranking(path), targets)\n if not all(\n eligibility.is_eligible(uid, item)\n for uid, item in frame[[\"uid\", \"item_id\"]].itertuples(index=False, name=None)\n ):\n raise RankingValidationError(\n \"submission contains an item rejected by eligibility service\"\n )\n return ranked_items(frame)\n\n\ndef score(\n ranked: dict[int, list[int]],\n graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n \"\"\"Aggregate per-user NDCG@10 over users with public relevance labels.\n\n Args:\n ranked: Ranked item IDs for every target user.\n graded: Eligible validation relevance keyed by user and item.\n\n Returns:\n The six-decimal ``quality_ndcg@10`` value and number of scored users.\n \"\"\"\n return score_rankings(ranked, graded)\n\n\ndef main() -> None:\n \"\"\"Run the public evaluator CLI and print its JSON score report.\"\"\"\n targets_frame = pd.read_parquet(DATA / \"target_users.parquet\")\n targets = set(targets_frame.uid.astype(int))\n eligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\n graded = validation_truth(eligibility, targets)\n\n if len(sys.argv) != 2:\n raise SystemExit(\n \"usage: evaluate_public.py \"\n )\n path = Path(sys.argv[1])\n ranked = load_submission(path, eligibility, targets)\n print(json.dumps({\"model\": str(path), \"scores\": score(ranked, graded)}, indent=2))\n\n\nif __name__ == \"__main__\":\n main()\n/task/requirements.txt\n/task/evaluate_public.py\n/task/.keep\n/task/ranking_evaluation.py\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "session_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "root_turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "response_id": "resp_08539f19231b9e9f016ab5a9c38b0487d2937dc4f40695b9a5", "usage": { "input_tokens": 46389, "cached_input_tokens": 45952, "cache_write_input_tokens": 0, "output_tokens": 142, "reasoning_output_tokens": 42, "total_tokens": 46531 }, "turn_token_usage": { "input_tokens": 688758, "cached_input_tokens": 650624, "cache_write_input_tokens": 0, "output_tokens": 2271, "reasoning_output_tokens": 557, "total_tokens": 691029 }, "thread_token_usage": { "input_tokens": 688758, "cached_input_tokens": 650624, "cache_write_input_tokens": 0, "output_tokens": 2271, "reasoning_output_tokens": 557, "total_tokens": 691029 } } ## Step 25 · agent · 2026-09-24T22:52:57.453Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T22:52:58.125Z { "code": "const r = await tools.write_stdin({\"session_id\":10359,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":12000}); text(r.output);\n" } ### Tool output · 2026-09-24T22:53:28.151Z Script completed Wall time 30.0 seconds Output: ### Harness context { "type": "token_usage_record", "thread_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "session_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "root_turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "response_id": "resp_08539f19231b9e9f016ab5a9c72ddc87d284ed154d68f7a1a1", "usage": { "input_tokens": 47878, "cached_input_tokens": 46208, "cache_write_input_tokens": 0, "output_tokens": 117, "reasoning_output_tokens": 70, "total_tokens": 47995 }, "turn_token_usage": { "input_tokens": 736636, "cached_input_tokens": 696832, "cache_write_input_tokens": 0, "output_tokens": 2388, "reasoning_output_tokens": 627, "total_tokens": 739024 }, "thread_token_usage": { "input_tokens": 736636, "cached_input_tokens": 696832, "cache_write_input_tokens": 0, "output_tokens": 2388, "reasoning_output_tokens": 627, "total_tokens": 739024 } } ## Step 26 · agent · 2026-09-24T22:53:30.274Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T22:53:30.953Z { "code": "const r = await tools.write_stdin({\"session_id\":10359,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":12000}); text(r.output);\n" } ### Tool output · 2026-09-24T22:54:00.975Z Script completed Wall time 30.0 seconds Output: ### Harness context { "type": "token_usage_record", "thread_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "session_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "root_turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "response_id": "resp_08539f19231b9e9f016ab5a9e8546887d2a5c93d42a7642625", "usage": { "input_tokens": 48018, "cached_input_tokens": 47744, "cache_write_input_tokens": 0, "output_tokens": 110, "reasoning_output_tokens": 63, "total_tokens": 48128 }, "turn_token_usage": { "input_tokens": 784654, "cached_input_tokens": 744576, "cache_write_input_tokens": 0, "output_tokens": 2498, "reasoning_output_tokens": 690, "total_tokens": 787152 }, "thread_token_usage": { "input_tokens": 784654, "cached_input_tokens": 744576, "cache_write_input_tokens": 0, "output_tokens": 2498, "reasoning_output_tokens": 690, "total_tokens": 787152 } } ## Step 27 · agent · 2026-09-24T22:54:04.578Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T22:54:06.694Z { "code": "const r = await tools.exec_command({\"cmd\":\"for d in /proc/[0-9]*; do tr '\\\\0' ' ' < \\\"$d/cmdline\\\" 2>/dev/null | rg -q 'train_current_model.py' && { echo \\\"$d\\\"; tr '\\\\0' ' ' < \\\"$d/cmdline\\\"; echo; sed -n '1,25p' \\\"$d/status\\\"; }; done\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":5000}); text(r.output);" } ### Tool output · 2026-09-24T22:54:06.795Z Script completed Wall time 0.1 seconds Output: Warning: truncated output (original token count: 7453) Total output lines: 690 /proc/8778 bash -c set -o pipefail; if [ -s ~/.nvm/nvm.sh ]; then . ~/.nvm/nvm.sh; fi; codex exec --dangerously-bypass-approvals-and-sandbox --skip-git-repo-check --model gpt-5.6-sol --json --enable unified_exec -c model_reasoning_effort=max -- '# Improve a production music-discovery two-tower You have joined the recommendations team for a music streaming service. The "Made for You" shelf is served by a mature production two-tower model. Its chronological data protocol and indexed serving path are sound, but its offline retrieval quality no longer clears the team'"'"'s next launch gate. Your job is to investigate the production model, improve its retrieval quality, and ship a better ranking artifact. Use the implementation and public validation data to form and test your own diagnosis. The source used to reproduce the production model is included under `/task/current_model/`. You may copy it into `/app/solution/`, edit it, add tests, and improve its training objective, but keep the data protocol, two-tower serving contract, and evaluation boundary intact. ## Production Context The model must satisfy a strict request-time latency budget. Item vectors are precomputed offline. At request time, one user vector is computed from observed interaction history and compared with candidate item vectors by a batchable two-tower score. Do not introduce a cross-encoder, per-candidate sequence model, or user-ID lookup table. The current trainer is a mature baseline with chronological internal validation. Focus your investigation on how its training objective selects and uses negative examples. Diagnose limitations in the current negative-sampling strategy and improve it under the fixed CPU budget. Validate changes with the public backtest. The final approach must still train and rank within the supplied CPU environment while preserving the serving and data boundaries above. ## What You Have All public inputs are under `/task/data/`. A stage cutoff is the timestamp at which observed history and candidate eligibility are frozen for a ranking. The public validation stage cutoff is `24786800`, when `train_*` ends. A 30-minute gap precedes the seven-day `val_*` window. For the final submission, that validation window becomes observed history, and another 30-minute gap precedes the hidden seven-day outcome window. The package includes: - `/task/current_model/train_current_model.py`: the production training and ranking script. It can reproduce the public validation ranking and produce a hidden-test submission after refitting. - `/task/current_model/two_tower_model.py`: model primitives used by the production script. - `/task/evaluate_public.py`: the public validation evaluator for development. - `/task/data/validation_eligible_candidates.npz` and `/task/data/test_eligible_candidates.npz`: platform-owned candidate sets for the validation and hidden-test cutoffs. - `/task/data/validation_preference_state.parquet` and `/task/data/test_preference_state.parquet`: active explicit preference state at the corresponding cutoffs. - `/task/requirements.txt`: the exact offline Python distribution inventory. You can reproduce the production public backtest with: ```sh python /task/current_model/train_current_model.py \ --stage validation \ --public-dir /task \ --output /app/solution/production_validation.parquet python /task/evaluate_public.py /app/solution/production_validation.parquet ``` ## Data For each event family there is a `train_*.parquet` and `val_*.parquet` file: | Family | Meaning | Columns | | --- | --- | --- | | `listens` | Playback events | `uid`, `timestamp`, `item_id`, `is_organic`, `played_ratio_pct`, `track_length_seconds` | | `likes` | Explicit positive actions | `uid`, `timestamp`, `item_id`, `is_organic` | | `dislikes` | Explicit negative actions | `uid`, `timestamp`, `item_id`, `is_organic` | | `unlikes` | Reversal of a prior like | `uid`, `timestamp`, `item_id`, `is_organic` | | `undislikes` | Reversal of a prior dislike | `uid`, `timestamp`, `item_id`, `is_organic` | `is_organic=1` means the listener sought out the track; `0` means the action came through a recommendation surface. A listen is a conventional positive at 50% completion. The discovery-quality objective below uses the stronger 80% threshold. The two public windows have different roles: 1. For model development and selection, train only from `train_*` and score the subsequent `val_*` outcomes. 2. After selecting an improvement, refit it on `train_* + val_*`. At hidden-test time the validation events are observed history, not recommendable outcomes. Do not use `val_*` events as features when measuring a validation ranking. They may and should be used when refitting the final hidden-test ranker. Additional files under `/task/data/`: - `candidate_catalog.parquet`: the 30,000 retrieval-catalog tracks, ordered by training positive-listen popularity. - `target_users.parquet`: users with at least 20 positive listens in the 90 days before the training cutoff, selected without future labels. - `validation_preference_state.parquet`: active explicit state at the training cutoff, for validation ranking. - `test_preference_state.parquet`: active explicit state after validation, for the final hidden-test ranking. State is `liked` or `disliked`; a pair may have both active channels. - `validation_eligible_candidates.npz` and `test_eligible_candidates.npz`: cutoff-correct candidate sets for every target user. Each contains aligned arrays `uids`, `item_ids`, and `packed_eligible`. For user row `r`, recover the Boolean item mask with `np.unpackbits(packed_eligible[r], count=len(item_ids), bitorder="little")`. Validation candidates exclude all `train_*` encounters. Test candidates exclude all `train_*` and `val_*` encounters. Future test interactions are hidden and used only by the separate Harbor verifier. ## Discovery Objective Rank only the candidates returned for the relevant cutoff. Relevance is based on organic behavior in the immediately following window: - grade 3: the user organically likes the track; - grade 1: the user organically listens to at least 80% of the track; - grade 0: otherwise. If both positive events occur, grade 3 wins. Ranking quality is measured with graded NDCG@10. DCG uses gain `2^grade - 1` and discount `1/log2(rank + 1)`, then averages per-user NDCG over users with at least one relevant item. The public evaluator reports this same objective for development, but its score is not a separate acceptance stage. Final acceptance is determined on the hidden test using the same objective. The Yambda logs contain interactions, not impression propensities; treat the organic objective as a robust offline target, not as a causal estimate of online lift. ## Deliverable Submit one final ranking as `/app/solution/submission.parquet` or `/app/solution/submission.csv`. It must be a regular file, not a symbolic link. If both files exist, the verifier evaluates `submission.parquet` and ignores `submission.csv`. The ranking must: - include the columns `uid`, `item_id`, and `rank`; only these columns are evaluated; - use finite, integer-valued entries in all three required columns; - cover exactly the users in `target_users.parquet`, with no missing or extra users; - contain exactly 100 rows and 100 distinct items for every target user; - contain every integer rank from 1 through 100 exactly once per user; and - use only items present in that user'"'"'s `test_eligible_candidates.npz` candidate set. Also leave the improved implementation used to build the artifact at `/app/solution/train_current_model.py`, along with any local modules it imports. A concise `/app/solution/README.md` with the diagnosis, before-and-after public score, model change, and submission command is useful for the experiment record, but it is not an automated acceptance gate. ## Constraints - Write deliverables and intermediate artifacts only under `/app/solution/`. - Treat `/task/` and the installed Python environment as read-only. - Do not invoke nested agents or attempt to access verifier files, credentials, other trials, or host paths. - Do not install packages or use network access except for the model-provider connection managed by Harbor. - Do not alter cutoff definitions, target users, candidate eligibility, or relevance labels. - Improve the production pipeline in place. Do not substitute an unrelated model family or evade the indexed two-tower serving constraint. - The environment includes CPU PyTorch 2.8.0. No accelerator is available. - You have up to 60 minutes. Prioritize measured validation improvements, a correct final refit, and a valid submission over broad architecture search. - Keep the best valid submission saved throughout the attempt so it survives the deadline. ' 2>&1 &1 | tee /app/solution/baseline_validation.log Name: sh Umask: 0022 State: S (sleeping) Tgid: 9456 Ngid: 0 Pid: 9456 PPid: 9012 TracerPid: 0 Uid: 1000 1000 1000 1000 Gid: 1000 1000 1000 1000 FDSize: 64 Groups: 1000 NStgid: 9456 NSpid: 9456 NSpgid: 9456 NSsid: 9456 Kthread: 0 VmPeak: 2592 kB VmSize: 2592 kB VmLck: 0 kB VmPin: 0 kB VmHWM: 1624 kB VmRSS: 1624 kB RssAnon: 108 kB RssFile: 1516 kB /proc/9457 /opt/task-venv/bin/python /app/solution/train_current_model.py --stage validation --public-dir /task --prepared-cache /app/solution/validation_sequences.npz --checkpoint-dir /app/solution/baseline_checkpoints --output /app/solution/production_validation.parquet Name: python Umask: 0022 State: R (running) Tgid: 9457 Ngid: 0 Pid: 9457 PPid: 9456 TracerPid: 0 Uid: 1000 1000 1000 1000 Gid: 1000 1000 1000 1000 FDSize: 64 Groups: 1000 NStgid: 9457 NSpid: 9457 NSpgid: 9456 NSsid: 9456 Kthread: 0 VmPeak: 7714832 kB VmSize: 7204420 kB VmLck: 0 kB VmPin: 0 kB VmHWM: 5523036 kB VmRSS: 4969720 kB RssAnon: 4819208 kB RssFile: 150512 kB /proc/9562 /bin/sh -c for d in /proc/[0-9]*; do tr '\0' ' ' < "$d/cmdline" 2>/dev/null | rg -q 'train_current_model.py' && { echo "$d"; tr '\0' ' ' < "$d/cmdline"; echo; sed -n '1,25p' "$d/status"; }; done Name: sh Umask: 0022 State: S (sleeping) Tgid: 9562 Ngid: 0 Pid: 9562 PPid: 9012 TracerPid: 0 Uid: 1000 1000 1000 1000 Gid: 1000 1000 1000 1000 FDSize: 64 Groups: 1000 NStgid: 9562 NSpid: 9562 NSpgid: 9562 NSsid: 9562 Kthread: 0 VmPeak: 2592 kB VmSize: 2592 kB VmLck: 0 kB VmPin: 0 kB VmHWM: 1716 kB VmRSS: 1716 kB RssAnon: 120 kB RssFile: 1596 kB ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-ae5eba31-1789-4f06-9f24-3eb5feb6ad26", "process_id": "46743", "command": [ "/bin/sh", "-lc", "for d in /proc/[0-9]*; do tr '\\0' ' ' < \"$d/cmdline\" 2>/dev/null | rg -q 'train_current_model.py' && { echo \"$d\"; tr '\\0' ' ' < \"$d/cmdline\"; echo; sed -n '1,25p' \"$d/status\"; }; done" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "for d in /proc/[0-9]*; do tr '\\0' ' ' < \"$d/cmdline\" 2>/dev/null | rg -q 'train_current_model.py' && { echo \"$d\"; tr '\\0' ' ' < \"$d/cmdline\"; echo; sed -n '1,25p' \"$d/status\"; }; done" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "/proc/8778\nbash -c set -o pipefail; if [ -s ~/.nvm/nvm.sh ]; then . ~/.nvm/nvm.sh; fi; codex exec --dangerously-bypass-approvals-and-sandbox --skip-git-repo-check --model gpt-5.6-sol --json --enable unified_exec -c model_reasoning_effort=max -- '# Improve a production music-discovery two-tower\n\nYou have joined the recommendations team for a music streaming service. The\n\"Made for You\" shelf is served by a mature production two-tower model. Its\nchronological data protocol and indexed serving path are sound, but its offline\nretrieval quality no longer clears the team'\"'\"'s next launch gate.\n\nYour job is to investigate the production model, improve its retrieval quality,\nand ship a better ranking artifact. Use the implementation and public validation\ndata to form and test your own diagnosis.\n\nThe source used to reproduce the production model is included under\n`/task/current_model/`. You may copy it into `/app/solution/`, edit it, add\ntests, and improve its training objective, but keep the data protocol,\ntwo-tower serving contract, and evaluation boundary intact.\n\n## Production Context\n\nThe model must satisfy a strict request-time latency budget. Item vectors are\nprecomputed offline. At request time, one user vector is computed from observed\ninteraction history and compared with candidate item vectors by a batchable\ntwo-tower score. Do not introduce a cross-encoder, per-candidate sequence model,\nor user-ID lookup table.\n\nThe current trainer is a mature baseline with chronological internal\nvalidation. Focus your investigation on how its training objective selects and\nuses negative examples. Diagnose limitations in the current negative-sampling\nstrategy and improve it under the fixed CPU budget. Validate changes with the\npublic backtest. The final approach must still train and rank within the supplied\nCPU environment while preserving the serving and data boundaries above.\n\n## What You Have\n\nAll public inputs are under `/task/data/`. A stage cutoff is the timestamp at\nwhich observed history and candidate eligibility are frozen for a ranking. The\npublic validation stage cutoff is `24786800`, when `train_*` ends. A 30-minute\ngap precedes the seven-day `val_*` window. For the final submission, that\nvalidation window becomes observed history, and another 30-minute gap precedes\nthe hidden seven-day outcome window.\n\nThe package includes:\n\n- `/task/current_model/train_current_model.py`: the production training and\n ranking script. It can reproduce the public validation ranking and produce a\n hidden-test submission after refitting.\n- `/task/current_model/two_tower_model.py`: model primitives used by the\n production script.\n- `/task/evaluate_public.py`: the public validation evaluator for development.\n- `/task/data/validation_eligible_candidates.npz` and\n `/task/data/test_eligible_candidates.npz`: platform-owned candidate sets for\n the validation and hidden-test cutoffs.\n- `/task/data/validation_preference_state.parquet` and\n `/task/data/test_preference_state.parquet`: active explicit preference state\n at the corresponding cutoffs.\n- `/task/requirements.txt`: the exact offline Python distribution inventory.\n\nYou can reproduce the production public backtest with:\n\n```sh\npython /task/current_model/train_current_model.py \\\n --stage validation \\\n --public-dir /task \\\n --output /app/solution/production_validation.parquet\npython /task/evaluate_public.py /app/solution/production_validation.parquet\n```\n\n## Data\n\nFor each event family there is a `train_*.parquet` and `val_*.parquet` file:\n\n| Family | Meaning | Columns |\n| --- | --- | --- |\n| `listens` | Playback events | `uid`, `timestamp`, `item_id`, `is_organic`, `played_ratio_pct`, `track_length_seconds` |\n| `likes` | Explicit positive actions | `uid`, `timestamp`, `item_id`, `is_organic` |\n| `dislikes` | Explicit negative actions | `uid`, `timestamp`, `item_id`, `is_organic` |\n| `unlikes` | Reversal of a prior like | `uid`, `timestamp`, `item_id`, `is_organic` |\n| `undislikes` | Reversal of a prior dislike | `uid`, `timestamp`, `item_id`, `is_organic` |\n\n`is_organic=1` means the listener sought out the track; `0` means the action came\nthrough a recommendation surface. A listen is a conventional positive at 50%\ncompletion. The discovery-quality objective below uses the stronger 80%\nthreshold.\n\nThe two public windows have different roles:\n\n1. For model development and selection, train only from `train_*` and score the\n subsequent `val_*` outcomes.\n2. After selecting an improvement, refit it on `train_* + val_*`. At hidden-test\n time the validation events are observed history, not recommendable outcomes.\n\nDo not use `val_*` events as features when measuring a validation ranking. They\nmay and should be used when refitting the final hidden-test ranker.\n\nAdditional files under `/task/data/`:\n\n- `candidate_catalog.parquet`: the 30,000 retrieval-catalog tracks, ordered by\n training positive-listen popularity.\n- `target_users.parquet`: users with at least 20 positive listens in the 90 days\n before the training cutoff, selected without future labels.\n- `validation_preference_state.parquet`: active explicit state at the training\n cutoff, for validation ranking.\n- `test_preference_state.parquet`: active explicit state after validation, for\n the final hidden-test ranking. State is `liked` or `disliked`; a pair may have\n both active channels.\n- `validation_eligible_candidates.npz` and `test_eligible_candidates.npz`:\n cutoff-correct candidate sets for every target user. Each contains aligned\n arrays `uids`, `item_ids`, and `packed_eligible`. For user row `r`, recover\n the Boolean item mask with `np.unpackbits(packed_eligible[r],\n count=len(item_ids), bitorder=\"little\")`.\n\nValidation candidates exclude all `train_*` encounters. Test candidates exclude\nall `train_*` and `val_*` encounters. Future test interactions are hidden and\nused only by the separate Harbor verifier.\n\n## Discovery Objective\n\nRank only the candidates returned for the relevant cutoff. Relevance is based\non organic behavior in the immediately following window:\n\n- grade 3: the user organically likes the track;\n- grade 1: the user organically listens to at least 80% of the track;\n- grade 0: otherwise.\n\nIf both positive events occur, grade 3 wins. Ranking quality is measured with\ngraded NDCG@10. DCG uses gain `2^grade - 1` and discount\n`1/log2(rank + 1)`, then averages per-user NDCG over users with at least one\nrelevant item.\n\nThe public evaluator reports this same objective for development, but its score\nis not a separate acceptance stage. Final acceptance is determined on the\nhidden test using the same objective. The Yambda logs contain interactions, not\nimpression propensities; treat the organic objective as a robust offline target,\nnot as a causal estimate of online lift.\n\n## Deliverable\n\nSubmit one final ranking as `/app/solution/submission.parquet` or\n`/app/solution/submission.csv`. It must be a regular file, not a symbolic link.\nIf both files exist, the verifier evaluates `submission.parquet` and ignores\n`submission.csv`.\n\nThe ranking must:\n\n- include the columns `uid`, `item_id`, and `rank`; only these columns are\n evaluated;\n- use finite, integer-valued entries in all three required columns;\n- cover exactly the users in `target_users.parquet`, with no missing or extra\n users;\n- contain exactly 100 rows and 100 distinct items for every target user;\n- contain every integer rank from 1 through 100 exactly once per user; and\n- use only items present in that user'\"'\"'s `test_eligible_candidates.npz`\n candidate set.\n\nAlso leave the improved implementation used to build the artifact at\n`/app/solution/train_current_model.py`, along with any local modules it imports.\nA concise `/app/solution/README.md` with the diagnosis, before-and-after public\nscore, model change, and submission command is useful for the experiment record,\nbut it is not an automated acceptance gate.\n\n## Constraints\n\n- Write deliverables and intermediate artifacts only under `/app/solution/`.\n- Treat `/task/` and the installed Python environment as read-only.\n- Do not invoke nested agents or attempt to access verifier files, credentials,\n other trials, or host paths.\n- Do not install packages or use network access except for the model-provider\n connection managed by Harbor.\n- Do not alter cutoff definitions, target users, candidate eligibility, or\n relevance labels.\n- Improve the production pipeline in place. Do not substitute an unrelated\n model family or evade the indexed two-tower serving constraint.\n- The environment includes CPU PyTorch 2.8.0. No accelerator is available.\n- You have up to 60 minutes. Prioritize measured validation improvements, a\n correct final refit, and a valid submission over broad architecture search.\n- Keep the best valid submission saved throughout the attempt so it survives\n the deadline.\n' 2>&1 &1 | tee /app/solution/baseline_validation.log \nName:\tsh\nUmask:\t0022\nState:\tS (sleeping)\nTgid:\t9456\nNgid:\t0\nPid:\t9456\nPPid:\t9012\nTracerPid:\t0\nUid:\t1000\t1000\t1000\t1000\nGid:\t1000\t1000\t1000\t1000\nFDSize:\t64\nGroups:\t1000 \nNStgid:\t9456\nNSpid:\t9456\nNSpgid:\t9456\nNSsid:\t9456\nKthread:\t0\nVmPeak:\t 2592 kB\nVmSize:\t 2592 kB\nVmLck:\t 0 kB\nVmPin:\t 0 kB\nVmHWM:\t 1624 kB\nVmRSS:\t 1624 kB\nRssAnon:\t 108 kB\nRssFile:\t 1516 kB\n/proc/9457\n/opt/task-venv/bin/python /app/solution/train_current_model.py --stage validation --public-dir /task --prepared-cache /app/solution/validation_sequences.npz --checkpoint-dir /app/solution/baseline_checkpoints --output /app/solution/production_validation.parquet \nName:\tpython\nUmask:\t0022\nState:\tR (running)\nTgid:\t9457\nNgid:\t0\nPid:\t9457\nPPid:\t9456\nTracerPid:\t0\nUid:\t1000\t1000\t1000\t1000\nGid:\t1000\t1000\t1000\t1000\nFDSize:\t64\nGroups:\t1000 \nNStgid:\t9457\nNSpid:\t9457\nNSpgid:\t9456\nNSsid:\t9456\nKthread:\t0\nVmPeak:\t 7714832 kB\nVmSize:\t 7204420 kB\nVmLck:\t 0 kB\nVmPin:\t 0 kB\nVmHWM:\t 5523036 kB\nVmRSS:\t 4969720 kB\nRssAnon:\t 4819208 kB\nRssFile:\t 150512 kB\n/proc/9562\n/bin/sh -c for d in /proc/[0-9]*; do tr '\\0' ' ' < \"$d/cmdline\" 2>/dev/null | rg -q 'train_current_model.py' && { echo \"$d\"; tr '\\0' ' ' < \"$d/cmdline\"; echo; sed -n '1,25p' \"$d/status\"; }; done \nName:\tsh\nUmask:\t0022\nState:\tS (sleeping)\nTgid:\t9562\nNgid:\t0\nPid:\t9562\nPPid:\t9012\nTracerPid:\t0\nUid:\t1000\t1000\t1000\t1000\nGid:\t1000\t1000\t1000\t1000\nFDSize:\t64\nGroups:\t1000 \nNStgid:\t9562\nNSpid:\t9562\nNSpgid:\t9562\nNSsid:\t9562\nKthread:\t0\nVmPeak:\t 2592 kB\nVmSize:\t 2592 kB\nVmLck:\t 0 kB\nVmPin:\t 0 kB\nVmHWM:\t 1716 kB\nVmRSS:\t 1716 kB\nRssAnon:\t 120 kB\nRssFile:\t 1596 kB\n", "stderr": "", "aggregated_output": "/proc/8778\nbash -c set -o pipefail; if [ -s ~/.nvm/nvm.sh ]; then . ~/.nvm/nvm.sh; fi; codex exec --dangerously-bypass-approvals-and-sandbox --skip-git-repo-check --model gpt-5.6-sol --json --enable unified_exec -c model_reasoning_effort=max -- '# Improve a production music-discovery two-tower\n\nYou have joined the recommendations team for a music streaming service. The\n\"Made for You\" shelf is served by a mature production two-tower model. Its\nchronological data protocol and indexed serving path are sound, but its offline\nretrieval quality no longer clears the team'\"'\"'s next launch gate.\n\nYour job is to investigate the production model, improve its retrieval quality,\nand ship a better ranking artifact. Use the implementation and public validation\ndata to form and test your own diagnosis.\n\nThe source used to reproduce the production model is included under\n`/task/current_model/`. You may copy it into `/app/solution/`, edit it, add\ntests, and improve its training objective, but keep the data protocol,\ntwo-tower serving contract, and evaluation boundary intact.\n\n## Production Context\n\nThe model must satisfy a strict request-time latency budget. Item vectors are\nprecomputed offline. At request time, one user vector is computed from observed\ninteraction history and compared with candidate item vectors by a batchable\ntwo-tower score. Do not introduce a cross-encoder, per-candidate sequence model,\nor user-ID lookup table.\n\nThe current trainer is a mature baseline with chronological internal\nvalidation. Focus your investigation on how its training objective selects and\nuses negative examples. Diagnose limitations in the current negative-sampling\nstrategy and improve it under the fixed CPU budget. Validate changes with the\npublic backtest. The final approach must still train and rank within the supplied\nCPU environment while preserving the serving and data boundaries above.\n\n## What You Have\n\nAll public inputs are under `/task/data/`. A stage cutoff is the timestamp at\nwhich observed history and candidate eligibility are frozen for a ranking. The\npublic validation stage cutoff is `24786800`, when `train_*` ends. A 30-minute\ngap precedes the seven-day `val_*` window. For the final submission, that\nvalidation window becomes observed history, and another 30-minute gap precedes\nthe hidden seven-day outcome window.\n\nThe package includes:\n\n- `/task/current_model/train_current_model.py`: the production training and\n ranking script. It can reproduce the public validation ranking and produce a\n hidden-test submission after refitting.\n- `/task/current_model/two_tower_model.py`: model primitives used by the\n production script.\n- `/task/evaluate_public.py`: the public validation evaluator for development.\n- `/task/data/validation_eligible_candidates.npz` and\n `/task/data/test_eligible_candidates.npz`: platform-owned candidate sets for\n the validation and hidden-test cutoffs.\n- `/task/data/validation_preference_state.parquet` and\n `/task/data/test_preference_state.parquet`: active explicit preference state\n at the corresponding cutoffs.\n- `/task/requirements.txt`: the exact offline Python distribution inventory.\n\nYou can reproduce the production public backtest with:\n\n```sh\npython /task/current_model/train_current_model.py \\\n --stage validation \\\n --public-dir /task \\\n --output /app/solution/production_validation.parquet\npython /task/evaluate_public.py /app/solution/production_validation.parquet\n```\n\n## Data\n\nFor each event family there is a `train_*.parquet` and `val_*.parquet` file:\n\n| Family | Meaning | Columns |\n| --- | --- | --- |\n| `listens` | Playback events | `uid`, `timestamp`, `item_id`, `is_organic`, `played_ratio_pct`, `track_length_seconds` |\n| `likes` | Explicit positive actions | `uid`, `timestamp`, `item_id`, `is_organic` |\n| `dislikes` | Explicit negative actions | `uid`, `timestamp`, `item_id`, `is_organic` |\n| `unlikes` | Reversal of a prior like | `uid`, `timestamp`, `item_id`, `is_organic` |\n| `undislikes` | Reversal of a prior dislike | `uid`, `timestamp`, `item_id`, `is_organic` |\n\n`is_organic=1` means the listener sought out the track; `0` means the action came\nthrough a recommendation surface. A listen is a conventional positive at 50%\ncompletion. The discovery-quality objective below uses the stronger 80%\nthreshold.\n\nThe two public windows have different roles:\n\n1. For model development and selection, train only from `train_*` and score the\n subsequent `val_*` outcomes.\n2. After selecting an improvement, refit it on `train_* + val_*`. At hidden-test\n time the validation events are observed history, not recommendable outcomes.\n\nDo not use `val_*` events as features when measuring a validation ranking. They\nmay and should be used when refitting the final hidden-test ranker.\n\nAdditional files under `/task/data/`:\n\n- `candidate_catalog.parquet`: the 30,000 retrieval-catalog tracks, ordered by\n training positive-listen popularity.\n- `target_users.parquet`: users with at least 20 positive listens in the 90 days\n before the training cutoff, selected without future labels.\n- `validation_preference_state.parquet`: active explicit state at the training\n cutoff, for validation ranking.\n- `test_preference_state.parquet`: active explicit state after validation, for\n the final hidden-test ranking. State is `liked` or `disliked`; a pair may have\n both active channels.\n- `validation_eligible_candidates.npz` and `test_eligible_candidates.npz`:\n cutoff-correct candidate sets for every target user. Each contains aligned\n arrays `uids`, `item_ids`, and `packed_eligible`. For user row `r`, recover\n the Boolean item mask with `np.unpackbits(packed_eligible[r],\n count=len(item_ids), bitorder=\"little\")`.\n\nValidation candidates exclude all `train_*` encounters. Test candidates exclude\nall `train_*` and `val_*` encounters. Future test interactions are hidden and\nused only by the separate Harbor verifier.\n\n## Discovery Objective\n\nRank only the candidates returned for the relevant cutoff. Relevance is based\non organic behavior in the immediately following window:\n\n- grade 3: the user organically likes the track;\n- grade 1: the user organically listens to at least 80% of the track;\n- grade 0: otherwise.\n\nIf both positive events occur, grade 3 wins. Ranking quality is measured with\ngraded NDCG@10. DCG uses gain `2^grade - 1` and discount\n`1/log2(rank + 1)`, then averages per-user NDCG over users with at least one\nrelevant item.\n\nThe public evaluator reports this same objective for development, but its score\nis not a separate acceptance stage. Final acceptance is determined on the\nhidden test using the same objective. The Yambda logs contain interactions, not\nimpression propensities; treat the organic objective as a robust offline target,\nnot as a causal estimate of online lift.\n\n## Deliverable\n\nSubmit one final ranking as `/app/solution/submission.parquet` or\n`/app/solution/submission.csv`. It must be a regular file, not a symbolic link.\nIf both files exist, the verifier evaluates `submission.parquet` and ignores\n`submission.csv`.\n\nThe ranking must:\n\n- include the columns `uid`, `item_id`, and `rank`; only these columns are\n evaluated;\n- use finite, integer-valued entries in all three required columns;\n- cover exactly the users in `target_users.parquet`, with no missing or extra\n users;\n- contain exactly 100 rows and 100 distinct items for every target user;\n- contain every integer rank from 1 through 100 exactly once per user; and\n- use only items present in that user'\"'\"'s `test_eligible_candidates.npz`\n candidate set.\n\nAlso leave the improved implementation used to build the artifact at\n`/app/solution/train_current_model.py`, along with any local modules it imports.\nA concise `/app/solution/README.md` with the diagnosis, before-and-after public\nscore, model change, and submission command is useful for the experiment record,\nbut it is not an automated acceptance gate.\n\n## Constraints\n\n- Write deliverables and intermediate artifacts only under `/app/solution/`.\n- Treat `/task/` and the installed Python environment as read-only.\n- Do not invoke nested agents or attempt to access verifier files, credentials,\n other trials, or host paths.\n- Do not install packages or use network access except for the model-provider\n connection managed by Harbor.\n- Do not alter cutoff definitions, target users, candidate eligibility, or\n relevance labels.\n- Improve the production pipeline in place. Do not substitute an unrelated\n model family or evade the indexed two-tower serving constraint.\n- The environment includes CPU PyTorch 2.8.0. No accelerator is available.\n- You have up to 60 minutes. Prioritize measured validation improvements, a\n correct final refit, and a valid submission over broad architecture search.\n- Keep the best valid submission saved throughout the attempt so it survives\n the deadline.\n' 2>&1 &1 | tee /app/solution/baseline_validation.log \nName:\tsh\nUmask:\t0022\nState:\tS (sleeping)\nTgid:\t9456\nNgid:\t0\nPid:\t9456\nPPid:\t9012\nTracerPid:\t0\nUid:\t1000\t1000\t1000\t1000\nGid:\t1000\t1000\t1000\t1000\nFDSize:\t64\nGroups:\t1000 \nNStgid:\t9456\nNSpid:\t9456\nNSpgid:\t9456\nNSsid:\t9456\nKthread:\t0\nVmPeak:\t 2592 kB\nVmSize:\t 2592 kB\nVmLck:\t 0 kB\nVmPin:\t 0 kB\nVmHWM:\t 1624 kB\nVmRSS:\t 1624 kB\nRssAnon:\t 108 kB\nRssFile:\t 1516 kB\n/proc/9457\n/opt/task-venv/bin/python /app/solution/train_current_model.py --stage validation --public-dir /task --prepared-cache /app/solution/validation_sequences.npz --checkpoint-dir /app/solution/baseline_checkpoints --output /app/solution/production_validation.parquet \nName:\tpython\nUmask:\t0022\nState:\tR (running)\nTgid:\t9457\nNgid:\t0\nPid:\t9457\nPPid:\t9456\nTracerPid:\t0\nUid:\t1000\t1000\t1000\t1000\nGid:\t1000\t1000\t1000\t1000\nFDSize:\t64\nGroups:\t1000 \nNStgid:\t9457\nNSpid:\t9457\nNSpgid:\t9456\nNSsid:\t9456\nKthread:\t0\nVmPeak:\t 7714832 kB\nVmSize:\t 7204420 kB\nVmLck:\t 0 kB\nVmPin:\t 0 kB\nVmHWM:\t 5523036 kB\nVmRSS:\t 4969720 kB\nRssAnon:\t 4819208 kB\nRssFile:\t 150512 kB\n/proc/9562\n/bin/sh -c for d in /proc/[0-9]*; do tr '\\0' ' ' < \"$d/cmdline\" 2>/dev/null | rg -q 'train_current_model.py' && { echo \"$d\"; tr '\\0' ' ' < \"$d/cmdline\"; echo; sed -n '1,25p' \"$d/status\"; }; done \nName:\tsh\nUmask:\t0022\nState:\tS (sleeping)\nTgid:\t9562\nNgid:\t0\nPid:\t9562\nPPid:\t9012\nTracerPid:\t0\nUid:\t1000\t1000\t1000\t1000\nGid:\t1000\t1000\t1000\t1000\nFDSize:\t64\nGroups:\t1000 \nNStgid:\t9562\nNSpid:\t9562\nNSpgid:\t9562\nNSsid:\t9562\nKthread:\t0\nVmPeak:\t 2592 kB\nVmSize:\t 2592 kB\nVmLck:\t 0 kB\nVmPin:\t 0 kB\nVmHWM:\t 1716 kB\nVmRSS:\t 1716 kB\nRssAnon:\t 120 kB\nRssFile:\t 1596 kB\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 2310 }, "formatted_output": "/proc/8778\nbash -c set -o pipefail; if [ -s ~/.nvm/nvm.sh ]; then . ~/.nvm/nvm.sh; fi; codex exec --dangerously-bypass-approvals-and-sandbox --skip-git-repo-check --model gpt-5.6-sol --json --enable unified_exec -c model_reasoning_effort=max -- '# Improve a production music-discovery two-tower\n\nYou have joined the recommendations team for a music streaming service. The\n\"Made for You\" shelf is served by a mature production two-tower model. Its\nchronological data protocol and indexed serving path are sound, but its offline\nretrieval quality no longer clears the team'\"'\"'s next launch gate.\n\nYour job is to investigate the production model, improve its retrieval quality,\nand ship a better ranking artifact. Use the implementation and public validation\ndata to form and test your own diagnosis.\n\nThe source used to reproduce the production model is included under\n`/task/current_model/`. You may copy it into `/app/solution/`, edit it, add\ntests, and improve its training objective, but keep the data protocol,\ntwo-tower serving contract, and evaluation boundary intact.\n\n## Production Context\n\nThe model must satisfy a strict request-time latency budget. Item vectors are\nprecomputed offline. At request time, one user vector is computed from observed\ninteraction history and compared with candidate item vectors by a batchable\ntwo-tower score. Do not introduce a cross-encoder, per-candidate sequence model,\nor user-ID lookup table.\n\nThe current trainer is a mature baseline with chronological internal\nvalidation. Focus your investigation on how its training objective selects and\nuses negative examples. Diagnose limitations in the current negative-sampling\nstrategy and improve it under the fixed CPU budget. Validate changes with the\npublic backtest. The final approach must still train and rank within the supplied\nCPU environment while preserving the serving and data boundaries above.\n\n## What You Have\n\nAll public inputs are under `/task/data/`. A stage cutoff is the timestamp at\nwhich observed history and candidate eligibility are frozen for a ranking. The\npublic validation stage cutoff is `24786800`, when `train_*` ends. A 30-minute\ngap precedes the seven-day `val_*` window. For the final submission, that\nvalidation window becomes observed history, and another 30-minute gap precedes\nthe hidden seven-day outcome window.\n\nThe package includes:\n\n- `/task/current_model/train_current_model.py`: the production training and\n ranking script. It can reproduce the public validation ranking and produce a\n hidden-test submission after refitting.\n- `/task/current_model/two_tower_model.py`: model primitives used by the\n production script.\n- `/task/evaluate_public.py`: the public validation evaluator for development.\n- `/task/data/validation_eligible_candidates.npz` and\n `/task/data/test_eligible_candidates.npz`: platform-owned candidate sets for\n the validation and hidden-test cutoffs.\n- `/task/data/validation_preference_state.parquet` and\n `/task/data/test_preference_state.parquet`: active explicit preference state\n at the corresponding cutoffs.\n- `/task/requirements.txt`: the exact offline Python distribution inventory.\n\nYou can reproduce the production public backtest with:\n\n```sh\npython /task/current_model/train_current_model.py \\\n --stage validation \\\n --public-dir /task \\\n --output /app/solution/production_validation.parquet\npython /task/evaluate_public.py /app/solution/production_validation.parquet\n```\n\n## Data\n\nFor each event family there is a `train_*.parquet` and `val_*.parquet` file:\n\n| Family | Meaning | Columns |\n| --- | --- | --- |\n| `listens` | Playback events | `uid`, `timestamp`, `item_id`, `is_organic`, `played_ratio_pct`, `track_length_seconds` |\n| `likes` | Explicit positive actions | `uid`, `timestamp`, `item_id`, `is_organic` |\n| `dislikes` | Explicit negative actions | `uid`, `timestamp`, `item_id`, `is_organic` |\n| `unlikes` | Reversal of a prior like | `uid`, `timestamp`, `item_id`, `is_organic` |\n| `undislikes` | Reversal of a prior dislike | `uid`, `timestamp`, `item_id`, `is_organic` |\n\n`is_organic=1` means the listener sought out the track; `0` means the action came\nthrough a recommendation surface. A listen is a conventional positive at 50%\ncompletion. The discovery-quality objective below uses the stronger 80%\nthreshold.\n\nThe two public windows have different roles:\n\n1. For model development and selection, train only from `train_*` and score the\n subsequent `val_*` outcomes.\n2. After selecting an improvement, refit it on `train_* + val_*`. At hidden-test\n time the validation events are observed history, not recommendable outcomes.\n\nDo not use `val_*` events as features when measuring a validation ranking. They\nmay and should be used when refitting the final hidden-test ranker.\n\nAdditional files under `/task/data/`:\n\n- `candidate_catalog.parquet`: the 30,000 retrieval-catalog tracks, ordered by\n training positive-listen popularity.\n- `target_users.parquet`: users with at least 20 positive listens in the 90 days\n before the training cutoff, selected without future labels.\n- `validation_preference_state.parquet`: active explicit state at the training\n cutoff, for validation ranking.\n- `test_preference_state.parquet`: active explicit state after validation, for\n the final hidden-test ranking. State is `liked` or `disliked`; a pair may have\n both active channels.\n- `validation_eligible_candidates.npz` and `test_eligible_candidates.npz`:\n cutoff-correct candidate sets for every target user. Each contains aligned\n arrays `uids`, `item_ids`, and `packed_eligible`. For user row `r`, recover\n the Boolean item mask with `np.unpackbits(packed_eligible[r],\n count=len(item_ids), bitorder=\"little\")`.\n\nValidation candidates exclude all `train_*` encounters. Test candidates exclude\nall `train_*` and `val_*` encounters. Future test interactions are hidden and\nused only by the separate Harbor verifier.\n\n## Discovery Objective\n\nRank only the candidates returned for the relevant cutoff. Relevance is based\non organic behavior in the immediately following window:\n\n- grade 3: the user organically likes the track;\n- grade 1: the user organically listens to at least 80% of the track;\n- grade 0: otherwise.\n\nIf both positive events occur, grade 3 wins. Ranking quality is measured with\ngraded NDCG@10. DCG uses gain `2^grade - 1` and discount\n`1/log2(rank + 1)`, then averages per-user NDCG over users with at least one\nrelevant item.\n\nThe public evaluator reports this same objective for development, but its score\nis not a separate acceptance stage. Final acceptance is determined on the\nhidden test using the same objective. The Yambda logs contain interactions, not\nimpression propensities; treat the organic objective as a robust offline target,\nnot as a causal estimate of online lift.\n\n## Deliverable\n\nSubmit one final ranking as `/app/solution/submission.parquet` or\n`/app/solution/submission.csv`. It must be a regular file, not a symbolic link.\nIf both files exist, the verifier evaluates `submission.parquet` and ignores\n`submission.csv`.\n\nThe ranking must:\n\n- include the columns `uid`, `item_id`, and `rank`; only these columns are\n evaluated;\n- use finite, integer-valued entries in all three required columns;\n- cover exactly the users in `target_users.parquet`, with no missing or extra\n users;\n- contain exactly 100 rows and 100 distinct items for every target user;\n- contain every integer rank from 1 through 100 exactly once per user; and\n- use only items present in that user'\"'\"'s `test_eligible_candidates.npz`\n candidate set.\n\nAlso leave the improved implementation used to build the artifact at\n`/app/solution/train_current_model.py`, along with any local modules it imports.\nA concise `/app/solution/README.md` with the diagnosis, before-and-after public\nscore, model change, and submission command is useful for the experiment record,\nbut it is not an automated acceptance gate.\n\n## Constraints\n\n- Write deliverables and intermediate artifacts only under `/app/solution/`.\n- Treat `/task/` and the installed Python environment as read-only.\n- Do not invoke nested agents or attempt to access verifier files, credentials,\n other trials, or host paths.\n- Do not install packages or use network access except for the model-provider\n connection managed by Harbor.\n- Do not alter cutoff definitions, target users, candidate eligibility, or\n relevance labels.\n- Improve the production pipeline in place. Do not substitute an unrelated\n model family or evade the indexed two-tower serving constraint.\n- The environment includes CPU PyTorch 2.8.0. No accelerator is available.\n- You have up to 60 minutes. Prioritize measured validation improvements, a\n correct final refit, and a valid submission over broad architecture search.\n- Keep the best valid submission saved throughout the attempt so it survives\n the deadline.\n' 2>&1 &1 | tee /app/solution/baseline_validation.log \nName:\tsh\nUmask:\t0022\nState:\tS (sleeping)\nTgid:\t9456\nNgid:\t0\nPid:\t9456\nPPid:\t9012\nTracerPid:\t0\nUid:\t1000\t1000\t1000\t1000\nGid:\t1000\t1000\t1000\t1000\nFDSize:\t64\nGroups:\t1000 \nNStgid:\t9456\nNSpid:\t9456\nNSpgid:\t9456\nNSsid:\t9456\nKthread:\t0\nVmPeak:\t 2592 kB\nVmSize:\t 2592 kB\nVmLck:\t 0 kB\nVmPin:\t 0 kB\nVmHWM:\t 1624 kB\nVmRSS:\t 1624 kB\nRssAnon:\t 108 kB\nRssFile:\t 1516 kB\n/proc/9457\n/opt/task-venv/bin/python /app/solution/train_current_model.py --stage validation --public-dir /task --prepared-cache /app/solution/validation_sequences.npz --checkpoint-dir /app/solution/baseline_checkpoints --output /app/solution/production_validation.parquet \nName:\tpython\nUmask:\t0022\nState:\tR (running)\nTgid:\t9457\nNgid:\t0\nPid:\t9457\nPPid:\t9456\nTracerPid:\t0\nUid:\t1000\t1000\t1000\t1000\nGid:\t1000\t1000\t1000\t1000\nFDSize:\t64\nGroups:\t1000 \nNStgid:\t9457\nNSpid:\t9457\nNSpgid:\t9456\nNSsid:\t9456\nKthread:\t0\nVmPeak:\t 7714832 kB\nVmSize:\t 7204420 kB\nVmLck:\t 0 kB\nVmPin:\t 0 kB\nVmHWM:\t 5523036 kB\nVmRSS:\t 4969720 kB\nRssAnon:\t 4819208 kB\nRssFile:\t 150512 kB\n/proc/9562\n/bin/sh -c for d in /proc/[0-9]*; do tr '\\0' ' ' < \"$d/cmdline\" 2>/dev/null | rg -q 'train_current_model.py' && { echo \"$d\"; tr '\\0' ' ' < \"$d/cmdline\"; echo; sed -n '1,25p' \"$d/status\"; }; done \nName:\tsh\nUmask:\t0022\nState:\tS (sleeping)\nTgid:\t9562\nNgid:\t0\nPid:\t9562\nPPid:\t9012\nTracerPid:\t0\nUid:\t1000\t1000\t1000\t1000\nGid:\t1000\t1000\t1000\t1000\nFDSize:\t64\nGroups:\t1000 \nNStgid:\t9562\nNSpid:\t9562\nNSpgid:\t9562\nNSsid:\t9562\nKthread:\t0\nVmPeak:\t 2592 kB\nVmSize:\t 2592 kB\nVmLck:\t 0 kB\nVmPin:\t 0 kB\nVmHWM:\t 1716 kB\nVmRSS:\t 1716 kB\nRssAnon:\t 120 kB\nRssFile:\t 1596 kB\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "session_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "root_turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "response_id": "resp_08539f19231b9e9f016ab5aa0944fc87d28b45831ea460e848", "usage": { "input_tokens": 48151, "cached_input_tokens": 47872, "cache_write_input_tokens": 0, "output_tokens": 225, "reasoning_output_tokens": 99, "total_tokens": 48376 }, "turn_token_usage": { "input_tokens": 832805, "cached_input_tokens": 792448, "cache_write_input_tokens": 0, "output_tokens": 2723, "reasoning_output_tokens": 789, "total_tokens": 835528 }, "thread_token_usage": { "input_tokens": 832805, "cached_input_tokens": 792448, "cache_write_input_tokens": 0, "output_tokens": 2723, "reasoning_output_tokens": 789, "total_tokens": 835528 } } ## Step 28 · agent · 2026-09-24T22:54:18.376Z ### Thinking unavailable — no text recorded ## Step 29 · agent · 2026-09-24T22:54:26.238Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T22:54:35.817Z { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python - <<'PY'\\nimport numpy as np, pandas as pd\\nD='/task/data'\\ncat=pd.read_parquet(f'{D}/candidate_catalog.parquet').sort_values('popularity_rank').reset_index(drop=True)\\nidx=pd.Index(cat.item_id)\\ntarg=set(pd.read_parquet(f'{D}/target_users.parquet').uid.astype(int))\\na=np.load(f'{D}/validation_eligible_candidates.npz')\\nuids=a['uids']; packed=a['packed_eligible']; row={int(u):i for i,u in enumerate(uids)}\\nls=pd.read_parquet(f'{D}/val_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct'])\\nlk=pd.read_parquet(f'{D}/val_likes.parquet',columns=['uid','item_id','is_organic'])\\nrel=pd.concat([ls[(ls.uid.isin(targ))&(ls.is_organic==1)&(ls.played_ratio_pct>=80)][['uid','item_id']].assign(grade=1),lk[(lk.uid.isin(targ))&(lk.is_organic==1)][['uid','item_id']].assign(grade=3)]).sort_values('grade').drop_duplicates(['uid','item_id'],keep='last')\\nci=idx.get_indexer(rel.item_id); ur=np.array([row.get(int(u),-1) for u in rel.uid]); valid=(ci>=0)&(ur>=0); eligible=np.zeros(len(rel),bool); eligible[valid]=((packed[ur[valid],ci[valid]>>3]>>(ci[valid]&7))&1).astype(bool)\\nr=rel[eligible].copy(); r['poprank']=cat.popularity_rank.to_numpy()[ci[eligible]]\\nprint('targets',len(targ),'relevant users',r.uid.nunique(),'pairs',len(r),'grades',r.grade.value_counts().to_dict())\\nprint('pop ranks quantiles all',r.poprank.quantile([0,.01,.05,.1,.25,.5,.75,.9,.95,.99,1]).to_dict())\\nfor g,x in r.groupby('grade'): print('grade',g,'n',len(x),'users',x.uid.nunique(),'rank q',x.poprank.quantile([.1,.25,.5,.75,.9]).to_dict())\\nprint('eligible per user bits mean',np.unpackbits(packed,axis=1,count=len(idx),bitorder='little').sum(1).mean())\\nprint('catalog head stats')\\nprint(cat.head()[['item_id','positive_listens','organic_positive_listens','popularity_rank']])\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":5000}); text(r.output);" } ### Tool output · 2026-09-24T22:54:39.301Z Script completed Wall time 3.4 seconds Output: targets 8456 relevant users 4263 pairs 37769 grades {1: 33352, 3: 4417} pop ranks quantiles all {0.0: 1.0, 0.01: 22.0, 0.05: 174.0, 0.1: 428.0, 0.25: 1733.0, 0.5: 5622.0, 0.75: 13374.0, 0.9: 21680.200000000004, 0.95: 25693.599999999984, 0.99: 29101.0, 1.0: 30000.0} grade 1 n 33352 users 3816 rank q {0.1: 408.0, 0.25: 1693.75, 0.5: 5545.0, 0.75: 13274.0, 0.9: 21656.0} grade 3 n 4417 users 1431 rank q {0.1: 618.0, 0.25: 2107.0, 0.5: 6371.0, 0.75: 14181.0, 0.9: 21801.8} eligible per user bits mean 29067.306291390727 catalog head stats item_id positive_listens organic_positive_listens popularity_rank 0 5862961 26615 20620 1 1 6901374 25536 17347 2 2 5635052 24691 17194 3 3 3542184 24075 17699 4 4 9378983 24036 18195 5 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "session_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "root_turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "response_id": "resp_08539f19231b9e9f016ab5aa0f0cc487d28dc67e43959be678", "usage": { "input_tokens": 53615, "cached_input_tokens": 48000, "cache_write_input_tokens": 0, "output_tokens": 1558, "reasoning_output_tokens": 932, "total_tokens": 55173 }, "turn_token_usage": { "input_tokens": 886420, "cached_input_tokens": 840448, "cache_write_input_tokens": 0, "output_tokens": 4281, "reasoning_output_tokens": 1721, "total_tokens": 890701 }, "thread_token_usage": { "input_tokens": 886420, "cached_input_tokens": 840448, "cache_write_input_tokens": 0, "output_tokens": 4281, "reasoning_output_tokens": 1721, "total_tokens": 890701 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-8efa44a1-e422-4e12-9e27-f56c9dd59da1", "process_id": "46021", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\nimport numpy as np, pandas as pd\nD='/task/data'\ncat=pd.read_parquet(f'{D}/candidate_catalog.parquet').sort_values('popularity_rank').reset_index(drop=True)\nidx=pd.Index(cat.item_id)\ntarg=set(pd.read_parquet(f'{D}/target_users.parquet').uid.astype(int))\na=np.load(f'{D}/validation_eligible_candidates.npz')\nuids=a['uids']; packed=a['packed_eligible']; row={int(u):i for i,u in enumerate(uids)}\nls=pd.read_parquet(f'{D}/val_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct'])\nlk=pd.read_parquet(f'{D}/val_likes.parquet',columns=['uid','item_id','is_organic'])\nrel=pd.concat([ls[(ls.uid.isin(targ))&(ls.is_organic==1)&(ls.played_ratio_pct>=80)][['uid','item_id']].assign(grade=1),lk[(lk.uid.isin(targ))&(lk.is_organic==1)][['uid','item_id']].assign(grade=3)]).sort_values('grade').drop_duplicates(['uid','item_id'],keep='last')\nci=idx.get_indexer(rel.item_id); ur=np.array([row.get(int(u),-1) for u in rel.uid]); valid=(ci>=0)&(ur>=0); eligible=np.zeros(len(rel),bool); eligible[valid]=((packed[ur[valid],ci[valid]>>3]>>(ci[valid]&7))&1).astype(bool)\nr=rel[eligible].copy(); r['poprank']=cat.popularity_rank.to_numpy()[ci[eligible]]\nprint('targets',len(targ),'relevant users',r.uid.nunique(),'pairs',len(r),'grades',r.grade.value_counts().to_dict())\nprint('pop ranks quantiles all',r.poprank.quantile([0,.01,.05,.1,.25,.5,.75,.9,.95,.99,1]).to_dict())\nfor g,x in r.groupby('grade'): print('grade',g,'n',len(x),'users',x.uid.nunique(),'rank q',x.poprank.quantile([.1,.25,.5,.75,.9]).to_dict())\nprint('eligible per user bits mean',np.unpackbits(packed,axis=1,count=len(idx),bitorder='little').sum(1).mean())\nprint('catalog head stats')\nprint(cat.head()[['item_id','positive_listens','organic_positive_listens','popularity_rank']])\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport numpy as np, pandas as pd\nD='/task/data'\ncat=pd.read_parquet(f'{D}/candidate_catalog.parquet').sort_values('popularity_rank').reset_index(drop=True)\nidx=pd.Index(cat.item_id)\ntarg=set(pd.read_parquet(f'{D}/target_users.parquet').uid.astype(int))\na=np.load(f'{D}/validation_eligible_candidates.npz')\nuids=a['uids']; packed=a['packed_eligible']; row={int(u):i for i,u in enumerate(uids)}\nls=pd.read_parquet(f'{D}/val_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct'])\nlk=pd.read_parquet(f'{D}/val_likes.parquet',columns=['uid','item_id','is_organic'])\nrel=pd.concat([ls[(ls.uid.isin(targ))&(ls.is_organic==1)&(ls.played_ratio_pct>=80)][['uid','item_id']].assign(grade=1),lk[(lk.uid.isin(targ))&(lk.is_organic==1)][['uid','item_id']].assign(grade=3)]).sort_values('grade').drop_duplicates(['uid','item_id'],keep='last')\nci=idx.get_indexer(rel.item_id); ur=np.array([row.get(int(u),-1) for u in rel.uid]); valid=(ci>=0)&(ur>=0); eligible=np.zeros(len(rel),bool); eligible[valid]=((packed[ur[valid],ci[valid]>>3]>>(ci[valid]&7))&1).astype(bool)\nr=rel[eligible].copy(); r['poprank']=cat.popularity_rank.to_numpy()[ci[eligible]]\nprint('targets',len(targ),'relevant users',r.uid.nunique(),'pairs',len(r),'grades',r.grade.value_counts().to_dict())\nprint('pop ranks quantiles all',r.poprank.quantile([0,.01,.05,.1,.25,.5,.75,.9,.95,.99,1]).to_dict())\nfor g,x in r.groupby('grade'): print('grade',g,'n',len(x),'users',x.uid.nunique(),'rank q',x.poprank.quantile([.1,.25,.5,.75,.9]).to_dict())\nprint('eligible per user bits mean',np.unpackbits(packed,axis=1,count=len(idx),bitorder='little').sum(1).mean())\nprint('catalog head stats')\nprint(cat.head()[['item_id','positive_listens','organic_positive_listens','popularity_rank']])\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "targets 8456 relevant users 4263 pairs 37769 grades {1: 33352, 3: 4417}\npop ranks quantiles all {0.0: 1.0, 0.01: 22.0, 0.05: 174.0, 0.1: 428.0, 0.25: 1733.0, 0.5: 5622.0, 0.75: 13374.0, 0.9: 21680.200000000004, 0.95: 25693.599999999984, 0.99: 29101.0, 1.0: 30000.0}\ngrade 1 n 33352 users 3816 rank q {0.1: 408.0, 0.25: 1693.75, 0.5: 5545.0, 0.75: 13274.0, 0.9: 21656.0}\ngrade 3 n 4417 users 1431 rank q {0.1: 618.0, 0.25: 2107.0, 0.5: 6371.0, 0.75: 14181.0, 0.9: 21801.8}\neligible per user bits mean 29067.306291390727\ncatalog head stats\n item_id positive_listens organic_positive_listens popularity_rank\n0 5862961 26615 20620 1\n1 6901374 25536 17347 2\n2 5635052 24691 17194 3\n3 3542184 24075 17699 4\n4 9378983 24036 18195 5\n", "stderr": "", "aggregated_output": "targets 8456 relevant users 4263 pairs 37769 grades {1: 33352, 3: 4417}\npop ranks quantiles all {0.0: 1.0, 0.01: 22.0, 0.05: 174.0, 0.1: 428.0, 0.25: 1733.0, 0.5: 5622.0, 0.75: 13374.0, 0.9: 21680.200000000004, 0.95: 25693.599999999984, 0.99: 29101.0, 1.0: 30000.0}\ngrade 1 n 33352 users 3816 rank q {0.1: 408.0, 0.25: 1693.75, 0.5: 5545.0, 0.75: 13274.0, 0.9: 21656.0}\ngrade 3 n 4417 users 1431 rank q {0.1: 618.0, 0.25: 2107.0, 0.5: 6371.0, 0.75: 14181.0, 0.9: 21801.8}\neligible per user bits mean 29067.306291390727\ncatalog head stats\n item_id positive_listens organic_positive_listens popularity_rank\n0 5862961 26615 20620 1\n1 6901374 25536 17347 2\n2 5635052 24691 17194 3\n3 3542184 24075 17699 4\n4 9378983 24036 18195 5\n", "exit_code": 0, "duration": { "secs": 3, "nanos": 228355810 }, "formatted_output": "targets 8456 relevant users 4263 pairs 37769 grades {1: 33352, 3: 4417}\npop ranks quantiles all {0.0: 1.0, 0.01: 22.0, 0.05: 174.0, 0.1: 428.0, 0.25: 1733.0, 0.5: 5622.0, 0.75: 13374.0, 0.9: 21680.200000000004, 0.95: 25693.599999999984, 0.99: 29101.0, 1.0: 30000.0}\ngrade 1 n 33352 users 3816 rank q {0.1: 408.0, 0.25: 1693.75, 0.5: 5545.0, 0.75: 13274.0, 0.9: 21656.0}\ngrade 3 n 4417 users 1431 rank q {0.1: 618.0, 0.25: 2107.0, 0.5: 6371.0, 0.75: 14181.0, 0.9: 21801.8}\neligible per user bits mean 29067.306291390727\ncatalog head stats\n item_id positive_listens organic_positive_listens popularity_rank\n0 5862961 26615 20620 1\n1 6901374 25536 17347 2\n2 5635052 24691 17194 3\n3 3542184 24075 17699 4\n4 9378983 24036 18195 5\n" } } ## Step 30 · agent · 2026-09-24T22:54:42.452Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T22:54:47.364Z { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python - <<'PY'\\nimport numpy as np,pandas as pd\\nD='/task/data'; a=np.load(f'{D}/validation_eligible_candidates.npz'); ids=a['item_ids']; uids=a['uids']; p=a['packed_eligible']; rows=[]\\nfor i,u in enumerate(uids):\\n e=np.unpackbits(p[i],count=len(ids),bitorder='little').astype(bool)\\n cols=np.flatnonzero(e)[:100]\\n rows.extend((int(u),int(ids[c]),j+1) for j,c in enumerate(cols))\\npd.DataFrame(rows,columns=['uid','item_id','rank']).to_parquet('/app/solution/pop_validation.parquet',index=False)\\nPY\\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/pop_validation.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":5000}); text(r.output);" } ### Tool output · 2026-09-24T22:54:56.167Z Script completed Wall time 8.8 seconds Output: { "model": "/app/solution/pop_validation.parquet", "scores": { "quality_ndcg@10": 0.01071, "quality_users": 4263 } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "session_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "root_turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "response_id": "resp_08539f19231b9e9f016ab5aa2f7a0887d2802e90d113064d0f", "usage": { "input_tokens": 55610, "cached_input_tokens": 53376, "cache_write_input_tokens": 0, "output_tokens": 259, "reasoning_output_tokens": 29, "total_tokens": 55869 }, "turn_token_usage": { "input_tokens": 942030, "cached_input_tokens": 893824, "cache_write_input_tokens": 0, "output_tokens": 4540, "reasoning_output_tokens": 1750, "total_tokens": 946570 }, "thread_token_usage": { "input_tokens": 942030, "cached_input_tokens": 893824, "cache_write_input_tokens": 0, "output_tokens": 4540, "reasoning_output_tokens": 1750, "total_tokens": 946570 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-0826b897-91f2-4da8-84d8-3042c740db7b", "process_id": "8766", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\nimport numpy as np,pandas as pd\nD='/task/data'; a=np.load(f'{D}/validation_eligible_candidates.npz'); ids=a['item_ids']; uids=a['uids']; p=a['packed_eligible']; rows=[]\nfor i,u in enumerate(uids):\n e=np.unpackbits(p[i],count=len(ids),bitorder='little').astype(bool)\n cols=np.flatnonzero(e)[:100]\n rows.extend((int(u),int(ids[c]),j+1) for j,c in enumerate(cols))\npd.DataFrame(rows,columns=['uid','item_id','rank']).to_parquet('/app/solution/pop_validation.parquet',index=False)\nPY\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/pop_validation.parquet" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport numpy as np,pandas as pd\nD='/task/data'; a=np.load(f'{D}/validation_eligible_candidates.npz'); ids=a['item_ids']; uids=a['uids']; p=a['packed_eligible']; rows=[]\nfor i,u in enumerate(uids):\n e=np.unpackbits(p[i],count=len(ids),bitorder='little').astype(bool)\n cols=np.flatnonzero(e)[:100]\n rows.extend((int(u),int(ids[c]),j+1) for j,c in enumerate(cols))\npd.DataFrame(rows,columns=['uid','item_id','rank']).to_parquet('/app/solution/pop_validation.parquet',index=False)\nPY\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/pop_validation.parquet" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{\n \"model\": \"/app/solution/pop_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.01071,\n \"quality_users\": 4263\n }\n}\n", "stderr": "", "aggregated_output": "{\n \"model\": \"/app/solution/pop_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.01071,\n \"quality_users\": 4263\n }\n}\n", "exit_code": 0, "duration": { "secs": 8, "nanos": 599214846 }, "formatted_output": "{\n \"model\": \"/app/solution/pop_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.01071,\n \"quality_users\": 4263\n }\n}\n" } } ## Step 31 · agent · 2026-09-24T22:54:58.305Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T22:54:59.160Z { "code": "const r = await tools.write_stdin({\"session_id\":10359,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":12000}); text(r.output);\n" } ### Tool output · 2026-09-24T22:55:29.186Z Script completed Wall time 30.0 seconds Output: epoch=1/1 batches=250 targets=987,417 batch_bce=0.547529 running_bce=0.541614 elapsed=201.9s saved epoch checkpoint: /app/solution/baseline_checkpoints/epoch-01.pt epoch=1/1 train_loss=0.53984 validation_loss=0.49664 pair_accuracy=0.755 train_targets=1,052,953 validation_targets=130,103 train_seconds=215.0 validation_seconds=17.5 restored best validation-BCE checkpoint: epoch=1 loss=0.496636 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "session_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "root_turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "response_id": "resp_08539f19231b9e9f016ab5aa4075e487d2aa7dca3d4a120d1e", "usage": { "input_tokens": 55937, "cached_input_tokens": 55424, "cache_write_input_tokens": 0, "output_tokens": 85, "reasoning_output_tokens": 38, "total_tokens": 56022 }, "turn_token_usage": { "input_tokens": 997967, "cached_input_tokens": 949248, "cache_write_input_tokens": 0, "output_tokens": 4625, "reasoning_output_tokens": 1788, "total_tokens": 1002592 }, "thread_token_usage": { "input_tokens": 997967, "cached_input_tokens": 949248, "cache_write_input_tokens": 0, "output_tokens": 4625, "reasoning_output_tokens": 1788, "total_tokens": 1002592 } } ## Step 32 · agent · 2026-09-24T22:55:30.253Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T22:55:30.950Z { "code": "const r = await tools.write_stdin({\"session_id\":10359,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":12000}); text(r.output);\n" } ### Tool output · 2026-09-24T22:56:00.972Z Script completed Wall time 30.0 seconds Output: ### Harness context { "type": "token_usage_record", "thread_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "session_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "root_turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "response_id": "resp_08539f19231b9e9f016ab5aa61650c87d2b513a11174c6b969", "usage": { "input_tokens": 56170, "cached_input_tokens": 55808, "cache_write_input_tokens": 0, "output_tokens": 55, "reasoning_output_tokens": 8, "total_tokens": 56225 }, "turn_token_usage": { "input_tokens": 1054137, "cached_input_tokens": 1005056, "cache_write_input_tokens": 0, "output_tokens": 4680, "reasoning_output_tokens": 1796, "total_tokens": 1058817 }, "thread_token_usage": { "input_tokens": 1054137, "cached_input_tokens": 1005056, "cache_write_input_tokens": 0, "output_tokens": 4680, "reasoning_output_tokens": 1796, "total_tokens": 1058817 } } ## Step 33 · agent · 2026-09-24T22:56:02.283Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T22:56:02.948Z { "code": "const r = await tools.write_stdin({\"session_id\":10359,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":12000}); text(r.output);\n" } ### Tool output · 2026-09-24T22:56:05.606Z Script completed Wall time 2.6 seconds Output: ranked users=1,024/8,456 ranked users=2,048/8,456 ranked users=3,072/8,456 ranked users=4,096/8,456 ranked users=5,120/8,456 ranked users=6,144/8,456 ranked users=7,168/8,456 ranked users=8,192/8,456 ranked users=8,456/8,456 { "baseline": "yambda-windowed-sequence-two-tower-base", "stage": "validation", "output": "/app/solution/production_validation.parquet", "rows": 845600, "users": 8456, "runtime_seconds": 379.97, "public_inputs_only": true, "architecture": { "family": "pure-windowed-sequence-two-tower", "sequence_training": "every-adjacent-target-within-recent-chronological-window", "sequence_encoder": "one-layer-GRU-dense-timestep-loss", "bptt_hidden_state": "carried-and-detached", "model_selection": "chronological-validation-BCE-early-stopping", "loss_execution": "gradient-accumulated-microbatches", "user_id_embedding": false, "user_mlp": "582-512-256-192", "item_mlp": "196-512-256-192", "normalization": "none", "score": "raw-dot-product", "objective": "sample-weighted-BCE-at-every-timestep", "negative_sampling": "24-uniform-plus-8-propensity-corrected-inbatch", "false_negative_filter": "full-training-history", "target_weighting": "organic-strong" }, "config": { "bptt_steps": 100, "user_batch_size": 64, "embedding_dim": 192, "hidden_dim": 192, "user_hidden_1": 512, "user_hidden_2": 256, "item_hidden_1": 512, "item_hidden_2": 256, "dropout": 0.05, "epochs": 1, "learning_rate": 0.001, "weight_decay": 1e-05, "gradient_clip": 2.0, "loss_microbatch_size": 65536, "uniform_negatives": 24, "inbatch_negatives": 8, "inbatch_correction_power": 0.5, "inbatch_correction_min": 0.25, "inbatch_correction_max": 4.0, "negative_filter_scope": "full-training-history", "target_weighting": "organic-strong", "ordinary_target_weight": 0.25, "organic_strong_target_weight": 1.0, "organic_like_target_weight": 3.0, "discovery_horizon_days": 7, "validation_fraction": 0.1, "max_train_targets_per_user": 128, "max_validation_targets_per_user": 16, "early_stopping_patience": 1, "early_stopping_min_delta": 0.0001, "seed": 29 }, "sequence_tokens": 20056329, "all_next_item_targets": 20047194, "selected_training_targets": 1052953, "selected_validation_targets": 130103, "discarded_old_targets": 18864138, "training_history": [ { "epoch": 1.0, "loss": 0.5398380466771012, "positive_logit": 0.7427799607756806, "negative_logit": -0.6222247067485194, "pair_accuracy": 0.7547733315257186, "targets": 1052953.0, "target_weight_sum": 632551.0, "batches": 266.0, "seconds": 215.029519907, "validation_loss": 0.49663621772498157, "validation_targets": 130103.0, "validation_target_weight_sum": 78769.25, "validation_seconds": 17.519004035999956 } ], "embedding_diagnostics": { "query_norm_mean": 1.6030172109603882, "query_norm_std": 0.7697497606277466, "item_norm_mean": 1.7570279836654663, "item_norm_std": 0.8279485702514648 }, "torch_version": "2.8.0+cpu", "device": "cpu" } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "session_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "root_turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "response_id": "resp_08539f19231b9e9f016ab5aa81371c87d2a27f640581bdfbbf", "usage": { "input_tokens": 56248, "cached_input_tokens": 55936, "cache_write_input_tokens": 0, "output_tokens": 64, "reasoning_output_tokens": 17, "total_tokens": 56312 }, "turn_token_usage": { "input_tokens": 1110385, "cached_input_tokens": 1060992, "cache_write_input_tokens": 0, "output_tokens": 4744, "reasoning_output_tokens": 1813, "total_tokens": 1115129 }, "thread_token_usage": { "input_tokens": 1110385, "cached_input_tokens": 1060992, "cache_write_input_tokens": 0, "output_tokens": 4744, "reasoning_output_tokens": 1813, "total_tokens": 1115129 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-2a1f8979-bef4-47dd-a285-a6368c856a04", "process_id": "10359", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python /app/solution/train_current_model.py --stage validation --public-dir /task --prepared-cache /app/solution/validation_sequences.npz --checkpoint-dir /app/solution/baseline_checkpoints --output /app/solution/production_validation.parquet 2>&1 | tee /app/solution/baseline_validation.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python /app/solution/train_current_model.py --stage validation --public-dir /task --prepared-cache /app/solution/validation_sequences.npz --checkpoint-dir /app/solution/baseline_checkpoints --output /app/solution/production_validation.parquet 2>&1 | tee /app/solution/baseline_validation.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "loaded explicit events: rows=720,270 users=8,220\nprepared users=1,000 tokens=2,090,746 states=35,727 elapsed=4.8s\nprepared users=2,000 tokens=4,301,266 states=71,242 elapsed=9.9s\nprepared users=3,000 tokens=6,486,146 states=109,471 elapsed=15.1s\nprepared users=4,000 tokens=8,793,043 states=148,210 elapsed=20.5s\nprepared users=5,000 tokens=10,992,205 states=182,596 elapsed=25.3s\nprepared users=6,000 tokens=13,061,727 states=217,148 elapsed=30.1s\nprepared users=7,000 tokens=15,334,418 states=256,914 elapsed=35.6s\nprepared users=8,000 tokens=17,462,672 states=296,703 elapsed=40.9s\nprepared users=9,000 tokens=19,568,405 states=331,551 elapsed=45.7s\ntarget users without qualifying catalog sequence=7\nprepared stage=validation sequence_users=9,135 tokens=20,056,329 next_item_targets=20,047,194 states=339,133\nsaved public-derived full-sequence cache: /app/solution/validation_sequences.npz\nchronological BCE window: train=1,052,953 validation=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194\ninbatch correction: min=0.250 mean_on_targets=1.001 max=4.000\ntarget weighting=organic-strong min=0.250 mean=0.575 max=1.000\nfull-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250\nepoch=1/1 batches=250 targets=987,417 batch_bce=0.547529 running_bce=0.541614 elapsed=201.9s\nsaved epoch checkpoint: /app/solution/baseline_checkpoints/epoch-01.pt\nepoch=1/1 train_loss=0.53984 validation_loss=0.49664 pair_accuracy=0.755 train_targets=1,052,953 validation_targets=130,103 train_seconds=215.0 validation_seconds=17.5\nrestored best validation-BCE checkpoint: epoch=1 loss=0.496636\nranked users=1,024/8,456\nranked users=2,048/8,456\nranked users=3,072/8,456\nranked users=4,096/8,456\nranked users=5,120/8,456\nranked users=6,144/8,456\nranked users=7,168/8,456\nranked users=8,192/8,456\nranked users=8,456/8,456\n{\n \"baseline\": \"yambda-windowed-sequence-two-tower-base\",\n \"stage\": \"validation\",\n \"output\": \"/app/solution/production_validation.parquet\",\n \"rows\": 845600,\n \"users\": 8456,\n \"runtime_seconds\": 379.97,\n \"public_inputs_only\": true,\n \"architecture\": {\n \"family\": \"pure-windowed-sequence-two-tower\",\n \"sequence_training\": \"every-adjacent-target-within-recent-chronological-window\",\n \"sequence_encoder\": \"one-layer-GRU-dense-timestep-loss\",\n \"bptt_hidden_state\": \"carried-and-detached\",\n \"model_selection\": \"chronological-validation-BCE-early-stopping\",\n \"loss_execution\": \"gradient-accumulated-microbatches\",\n \"user_id_embedding\": false,\n \"user_mlp\": \"582-512-256-192\",\n \"item_mlp\": \"196-512-256-192\",\n \"normalization\": \"none\",\n \"score\": \"raw-dot-product\",\n \"objective\": \"sample-weighted-BCE-at-every-timestep\",\n \"negative_sampling\": \"24-uniform-plus-8-propensity-corrected-inbatch\",\n \"false_negative_filter\": \"full-training-history\",\n \"target_weighting\": \"organic-strong\"\n },\n \"config\": {\n \"bptt_steps\": 100,\n \"user_batch_size\": 64,\n \"embedding_dim\": 192,\n \"hidden_dim\": 192,\n \"user_hidden_1\": 512,\n \"user_hidden_2\": 256,\n \"item_hidden_1\": 512,\n \"item_hidden_2\": 256,\n \"dropout\": 0.05,\n \"epochs\": 1,\n \"learning_rate\": 0.001,\n \"weight_decay\": 1e-05,\n \"gradient_clip\": 2.0,\n \"loss_microbatch_size\": 65536,\n \"uniform_negatives\": 24,\n \"inbatch_negatives\": 8,\n \"inbatch_correction_power\": 0.5,\n \"inbatch_correction_min\": 0.25,\n \"inbatch_correction_max\": 4.0,\n \"negative_filter_scope\": \"full-training-history\",\n \"target_weighting\": \"organic-strong\",\n \"ordinary_target_weight\": 0.25,\n \"organic_strong_target_weight\": 1.0,\n \"organic_like_target_weight\": 3.0,\n \"discovery_horizon_days\": 7,\n \"validation_fraction\": 0.1,\n \"max_train_targets_per_user\": 128,\n \"max_validation_targets_per_user\": 16,\n \"early_stopping_patience\": 1,\n \"early_stopping_min_delta\": 0.0001,\n \"seed\": 29\n },\n \"sequence_tokens\": 20056329,\n \"all_next_item_targets\": 20047194,\n \"selected_training_targets\": 1052953,\n \"selected_validation_targets\": 130103,\n \"discarded_old_targets\": 18864138,\n \"training_history\": [\n {\n \"epoch\": 1.0,\n \"loss\": 0.5398380466771012,\n \"positive_logit\": 0.7427799607756806,\n \"negative_logit\": -0.6222247067485194,\n \"pair_accuracy\": 0.7547733315257186,\n \"targets\": 1052953.0,\n \"target_weight_sum\": 632551.0,\n \"batches\": 266.0,\n \"seconds\": 215.029519907,\n \"validation_loss\": 0.49663621772498157,\n \"validation_targets\": 130103.0,\n \"validation_target_weight_sum\": 78769.25,\n \"validation_seconds\": 17.519004035999956\n }\n ],\n \"embedding_diagnostics\": {\n \"query_norm_mean\": 1.6030172109603882,\n \"query_norm_std\": 0.7697497606277466,\n \"item_norm_mean\": 1.7570279836654663,\n \"item_norm_std\": 0.8279485702514648\n },\n \"torch_version\": \"2.8.0+cpu\",\n \"device\": \"cpu\"\n}\n", "stderr": "", "aggregated_output": "loaded explicit events: rows=720,270 users=8,220\nprepared users=1,000 tokens=2,090,746 states=35,727 elapsed=4.8s\nprepared users=2,000 tokens=4,301,266 states=71,242 elapsed=9.9s\nprepared users=3,000 tokens=6,486,146 states=109,471 elapsed=15.1s\nprepared users=4,000 tokens=8,793,043 states=148,210 elapsed=20.5s\nprepared users=5,000 tokens=10,992,205 states=182,596 elapsed=25.3s\nprepared users=6,000 tokens=13,061,727 states=217,148 elapsed=30.1s\nprepared users=7,000 tokens=15,334,418 states=256,914 elapsed=35.6s\nprepared users=8,000 tokens=17,462,672 states=296,703 elapsed=40.9s\nprepared users=9,000 tokens=19,568,405 states=331,551 elapsed=45.7s\ntarget users without qualifying catalog sequence=7\nprepared stage=validation sequence_users=9,135 tokens=20,056,329 next_item_targets=20,047,194 states=339,133\nsaved public-derived full-sequence cache: /app/solution/validation_sequences.npz\nchronological BCE window: train=1,052,953 validation=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194\ninbatch correction: min=0.250 mean_on_targets=1.001 max=4.000\ntarget weighting=organic-strong min=0.250 mean=0.575 max=1.000\nfull-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250\nepoch=1/1 batches=250 targets=987,417 batch_bce=0.547529 running_bce=0.541614 elapsed=201.9s\nsaved epoch checkpoint: /app/solution/baseline_checkpoints/epoch-01.pt\nepoch=1/1 train_loss=0.53984 validation_loss=0.49664 pair_accuracy=0.755 train_targets=1,052,953 validation_targets=130,103 train_seconds=215.0 validation_seconds=17.5\nrestored best validation-BCE checkpoint: epoch=1 loss=0.496636\nranked users=1,024/8,456\nranked users=2,048/8,456\nranked users=3,072/8,456\nranked users=4,096/8,456\nranked users=5,120/8,456\nranked users=6,144/8,456\nranked users=7,168/8,456\nranked users=8,192/8,456\nranked users=8,456/8,456\n{\n \"baseline\": \"yambda-windowed-sequence-two-tower-base\",\n \"stage\": \"validation\",\n \"output\": \"/app/solution/production_validation.parquet\",\n \"rows\": 845600,\n \"users\": 8456,\n \"runtime_seconds\": 379.97,\n \"public_inputs_only\": true,\n \"architecture\": {\n \"family\": \"pure-windowed-sequence-two-tower\",\n \"sequence_training\": \"every-adjacent-target-within-recent-chronological-window\",\n \"sequence_encoder\": \"one-layer-GRU-dense-timestep-loss\",\n \"bptt_hidden_state\": \"carried-and-detached\",\n \"model_selection\": \"chronological-validation-BCE-early-stopping\",\n \"loss_execution\": \"gradient-accumulated-microbatches\",\n \"user_id_embedding\": false,\n \"user_mlp\": \"582-512-256-192\",\n \"item_mlp\": \"196-512-256-192\",\n \"normalization\": \"none\",\n \"score\": \"raw-dot-product\",\n \"objective\": \"sample-weighted-BCE-at-every-timestep\",\n \"negative_sampling\": \"24-uniform-plus-8-propensity-corrected-inbatch\",\n \"false_negative_filter\": \"full-training-history\",\n \"target_weighting\": \"organic-strong\"\n },\n \"config\": {\n \"bptt_steps\": 100,\n \"user_batch_size\": 64,\n \"embedding_dim\": 192,\n \"hidden_dim\": 192,\n \"user_hidden_1\": 512,\n \"user_hidden_2\": 256,\n \"item_hidden_1\": 512,\n \"item_hidden_2\": 256,\n \"dropout\": 0.05,\n \"epochs\": 1,\n \"learning_rate\": 0.001,\n \"weight_decay\": 1e-05,\n \"gradient_clip\": 2.0,\n \"loss_microbatch_size\": 65536,\n \"uniform_negatives\": 24,\n \"inbatch_negatives\": 8,\n \"inbatch_correction_power\": 0.5,\n \"inbatch_correction_min\": 0.25,\n \"inbatch_correction_max\": 4.0,\n \"negative_filter_scope\": \"full-training-history\",\n \"target_weighting\": \"organic-strong\",\n \"ordinary_target_weight\": 0.25,\n \"organic_strong_target_weight\": 1.0,\n \"organic_like_target_weight\": 3.0,\n \"discovery_horizon_days\": 7,\n \"validation_fraction\": 0.1,\n \"max_train_targets_per_user\": 128,\n \"max_validation_targets_per_user\": 16,\n \"early_stopping_patience\": 1,\n \"early_stopping_min_delta\": 0.0001,\n \"seed\": 29\n },\n \"sequence_tokens\": 20056329,\n \"all_next_item_targets\": 20047194,\n \"selected_training_targets\": 1052953,\n \"selected_validation_targets\": 130103,\n \"discarded_old_targets\": 18864138,\n \"training_history\": [\n {\n \"epoch\": 1.0,\n \"loss\": 0.5398380466771012,\n \"positive_logit\": 0.7427799607756806,\n \"negative_logit\": -0.6222247067485194,\n \"pair_accuracy\": 0.7547733315257186,\n \"targets\": 1052953.0,\n \"target_weight_sum\": 632551.0,\n \"batches\": 266.0,\n \"seconds\": 215.029519907,\n \"validation_loss\": 0.49663621772498157,\n \"validation_targets\": 130103.0,\n \"validation_target_weight_sum\": 78769.25,\n \"validation_seconds\": 17.519004035999956\n }\n ],\n \"embedding_diagnostics\": {\n \"query_norm_mean\": 1.6030172109603882,\n \"query_norm_std\": 0.7697497606277466,\n \"item_norm_mean\": 1.7570279836654663,\n \"item_norm_std\": 0.8279485702514648\n },\n \"torch_version\": \"2.8.0+cpu\",\n \"device\": \"cpu\"\n}\n", "exit_code": 0, "duration": { "secs": 384, "nanos": 317006950 }, "formatted_output": "loaded explicit events: rows=720,270 users=8,220\nprepared users=1,000 tokens=2,090,746 states=35,727 elapsed=4.8s\nprepared users=2,000 tokens=4,301,266 states=71,242 elapsed=9.9s\nprepared users=3,000 tokens=6,486,146 states=109,471 elapsed=15.1s\nprepared users=4,000 tokens=8,793,043 states=148,210 elapsed=20.5s\nprepared users=5,000 tokens=10,992,205 states=182,596 elapsed=25.3s\nprepared users=6,000 tokens=13,061,727 states=217,148 elapsed=30.1s\nprepared users=7,000 tokens=15,334,418 states=256,914 elapsed=35.6s\nprepared users=8,000 tokens=17,462,672 states=296,703 elapsed=40.9s\nprepared users=9,000 tokens=19,568,405 states=331,551 elapsed=45.7s\ntarget users without qualifying catalog sequence=7\nprepared stage=validation sequence_users=9,135 tokens=20,056,329 next_item_targets=20,047,194 states=339,133\nsaved public-derived full-sequence cache: /app/solution/validation_sequences.npz\nchronological BCE window: train=1,052,953 validation=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194\ninbatch correction: min=0.250 mean_on_targets=1.001 max=4.000\ntarget weighting=organic-strong min=0.250 mean=0.575 max=1.000\nfull-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250\nepoch=1/1 batches=250 targets=987,417 batch_bce=0.547529 running_bce=0.541614 elapsed=201.9s\nsaved epoch checkpoint: /app/solution/baseline_checkpoints/epoch-01.pt\nepoch=1/1 train_loss=0.53984 validation_loss=0.49664 pair_accuracy=0.755 train_targets=1,052,953 validation_targets=130,103 train_seconds=215.0 validation_seconds=17.5\nrestored best validation-BCE checkpoint: epoch=1 loss=0.496636\nranked users=1,024/8,456\nranked users=2,048/8,456\nranked users=3,072/8,456\nranked users=4,096/8,456\nranked users=5,120/8,456\nranked users=6,144/8,456\nranked users=7,168/8,456\nranked users=8,192/8,456\nranked users=8,456/8,456\n{\n \"baseline\": \"yambda-windowed-sequence-two-tower-base\",\n \"stage\": \"validation\",\n \"output\": \"/app/solution/production_validation.parquet\",\n \"rows\": 845600,\n \"users\": 8456,\n \"runtime_seconds\": 379.97,\n \"public_inputs_only\": true,\n \"architecture\": {\n \"family\": \"pure-windowed-sequence-two-tower\",\n \"sequence_training\": \"every-adjacent-target-within-recent-chronological-window\",\n \"sequence_encoder\": \"one-layer-GRU-dense-timestep-loss\",\n \"bptt_hidden_state\": \"carried-and-detached\",\n \"model_selection\": \"chronological-validation-BCE-early-stopping\",\n \"loss_execution\": \"gradient-accumulated-microbatches\",\n \"user_id_embedding\": false,\n \"user_mlp\": \"582-512-256-192\",\n \"item_mlp\": \"196-512-256-192\",\n \"normalization\": \"none\",\n \"score\": \"raw-dot-product\",\n \"objective\": \"sample-weighted-BCE-at-every-timestep\",\n \"negative_sampling\": \"24-uniform-plus-8-propensity-corrected-inbatch\",\n \"false_negative_filter\": \"full-training-history\",\n \"target_weighting\": \"organic-strong\"\n },\n \"config\": {\n \"bptt_steps\": 100,\n \"user_batch_size\": 64,\n \"embedding_dim\": 192,\n \"hidden_dim\": 192,\n \"user_hidden_1\": 512,\n \"user_hidden_2\": 256,\n \"item_hidden_1\": 512,\n \"item_hidden_2\": 256,\n \"dropout\": 0.05,\n \"epochs\": 1,\n \"learning_rate\": 0.001,\n \"weight_decay\": 1e-05,\n \"gradient_clip\": 2.0,\n \"loss_microbatch_size\": 65536,\n \"uniform_negatives\": 24,\n \"inbatch_negatives\": 8,\n \"inbatch_correction_power\": 0.5,\n \"inbatch_correction_min\": 0.25,\n \"inbatch_correction_max\": 4.0,\n \"negative_filter_scope\": \"full-training-history\",\n \"target_weighting\": \"organic-strong\",\n \"ordinary_target_weight\": 0.25,\n \"organic_strong_target_weight\": 1.0,\n \"organic_like_target_weight\": 3.0,\n \"discovery_horizon_days\": 7,\n \"validation_fraction\": 0.1,\n \"max_train_targets_per_user\": 128,\n \"max_validation_targets_per_user\": 16,\n \"early_stopping_patience\": 1,\n \"early_stopping_min_delta\": 0.0001,\n \"seed\": 29\n },\n \"sequence_tokens\": 20056329,\n \"all_next_item_targets\": 20047194,\n \"selected_training_targets\": 1052953,\n \"selected_validation_targets\": 130103,\n \"discarded_old_targets\": 18864138,\n \"training_history\": [\n {\n \"epoch\": 1.0,\n \"loss\": 0.5398380466771012,\n \"positive_logit\": 0.7427799607756806,\n \"negative_logit\": -0.6222247067485194,\n \"pair_accuracy\": 0.7547733315257186,\n \"targets\": 1052953.0,\n \"target_weight_sum\": 632551.0,\n \"batches\": 266.0,\n \"seconds\": 215.029519907,\n \"validation_loss\": 0.49663621772498157,\n \"validation_targets\": 130103.0,\n \"validation_target_weight_sum\": 78769.25,\n \"validation_seconds\": 17.519004035999956\n }\n ],\n \"embedding_diagnostics\": {\n \"query_norm_mean\": 1.6030172109603882,\n \"query_norm_std\": 0.7697497606277466,\n \"item_norm_mean\": 1.7570279836654663,\n \"item_norm_std\": 0.8279485702514648\n },\n \"torch_version\": \"2.8.0+cpu\",\n \"device\": \"cpu\"\n}\n" } } ## Step 34 · agent · 2026-09-24T22:56:06.665Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T22:56:07.778Z { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/production_validation.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":5000}); text(r.output);" } ### Tool output · 2026-09-24T22:56:11.186Z Script completed Wall time 3.4 seconds Output: { "model": "/app/solution/production_validation.parquet", "scores": { "quality_ndcg@10": 0.02128, "quality_users": 4263 } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "session_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "root_turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "response_id": "resp_08539f19231b9e9f016ab5aa85d20487d2a94e4ba48c9757fa", "usage": { "input_tokens": 57394, "cached_input_tokens": 56064, "cache_write_input_tokens": 0, "output_tokens": 77, "reasoning_output_tokens": 7, "total_tokens": 57471 }, "turn_token_usage": { "input_tokens": 1167779, "cached_input_tokens": 1117056, "cache_write_input_tokens": 0, "output_tokens": 4821, "reasoning_output_tokens": 1820, "total_tokens": 1172600 }, "thread_token_usage": { "input_tokens": 1167779, "cached_input_tokens": 1117056, "cache_write_input_tokens": 0, "output_tokens": 4821, "reasoning_output_tokens": 1820, "total_tokens": 1172600 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-5b98f676-0a17-421d-9aea-7c313f1a5175", "process_id": "10686", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/production_validation.parquet" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/production_validation.parquet" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{\n \"model\": \"/app/solution/production_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.02128,\n \"quality_users\": 4263\n }\n}\n", "stderr": "", "aggregated_output": "{\n \"model\": \"/app/solution/production_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.02128,\n \"quality_users\": 4263\n }\n}\n", "exit_code": 0, "duration": { "secs": 3, "nanos": 231792649 }, "formatted_output": "{\n \"model\": \"/app/solution/production_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.02128,\n \"quality_users\": 4263\n }\n}\n" } } ## Step 35 · agent · 2026-09-24T22:56:21.384Z ### Thinking unavailable — no text recorded ## Step 36 · agent · 2026-09-24T22:56:22.999Z ### Thinking unavailable — no text recorded The reproduced public score is `0.021280` (versus `0.010710` for global popularity), so the personalization signal is real. The key diagnostic is now whether ranking errors are dominated by item-vector norm/popularity bias—a classic symptom of mostly easy BCE negatives—or by insufficient user–item discrimination. I’m caching the learned user/item vectors once and probing score calibration before choosing the hard-negative formulation. ### exec · 2026-09-24T22:56:41.128Z { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/analyze_scores.py\\n+#!/usr/bin/env python3\\n+\\\"\\\"\\\"Local public-validation diagnostics for a saved two-tower checkpoint.\\\"\\\"\\\"\\n+from pathlib import Path\\n+import sys\\n+\\n+import numpy as np\\n+import pandas as pd\\n+import torch\\n+\\n+HERE = Path(__file__).resolve().parent\\n+sys.path.insert(0, str(HERE))\\n+import train_current_model as trainer\\n+import two_tower_model as base\\n+\\n+\\n+def main() -> None:\\n+ torch.set_num_threads(8)\\n+ config = trainer.Config()\\n+ prepared = trainer.load_cache(\\n+ HERE / \\\"validation_sequences.npz\\\", \\\"validation\\\", config\\n+ )\\n+ candidates = trainer.CandidateSet.read(\\n+ Path(\\\"/task/data/validation_eligible_candidates.npz\\\")\\n+ )\\n+ model = base.SequentialTwoTower(\\n+ prepared.item_features, trainer.model_config(config)\\n+ )\\n+ payload = torch.load(\\n+ HERE / \\\"baseline_checkpoints/epoch-01.pt\\\",\\n+ map_location=\\\"cpu\\\",\\n+ weights_only=False,\\n+ )\\n+ model.load_state_dict(payload[\\\"model_state\\\"])\\n+ model.eval()\\n+ with torch.inference_mode():\\n+ queries = trainer.inference_queries(model, prepared, config, torch.device(\\\"cpu\\\"))\\n+ items = torch.cat(\\n+ [\\n+ model.encode_items(torch.arange(s, min(s + 2048, len(prepared.item_ids))))\\n+ for s in range(0, len(prepared.item_ids), 2048)\\n+ ]\\n+ )\\n+ np.savez(\\n+ HERE / \\\"baseline_vectors.npz\\\",\\n+ queries=queries.numpy(),\\n+ items=items.numpy(),\\n+ )\\n+\\n+ item_norm = torch.linalg.vector_norm(items, dim=1).clamp_min(1e-8)\\n+ # Dividing only by item norm changes rank; query normalization does not.\\n+ variants = {\\n+ \\\"raw\\\": items,\\n+ \\\"norm025\\\": items / item_norm[:, None].pow(0.25),\\n+ \\\"norm050\\\": items / item_norm[:, None].pow(0.50),\\n+ \\\"norm075\\\": items / item_norm[:, None].pow(0.75),\\n+ \\\"cosine\\\": items / item_norm[:, None],\\n+ }\\n+ for name, item_vectors in variants.items():\\n+ rows: list[tuple[int, int, int]] = []\\n+ for start in range(0, len(candidates.uids), 64):\\n+ end = min(start + 64, len(candidates.uids))\\n+ scores = queries[start:end] @ item_vectors.T\\n+ eligible = np.unpackbits(\\n+ candidates.packed[start:end],\\n+ axis=1,\\n+ count=len(prepared.item_ids),\\n+ bitorder=\\\"little\\\",\\n+ ).astype(bool, copy=False)\\n+ scores.masked_fill_(~torch.from_numpy(eligible), -torch.inf)\\n+ selected = torch.topk(scores, k=base.TOP_N, dim=1).indices.numpy()\\n+ for local, columns in enumerate(selected):\\n+ uid = int(candidates.uids[start + local])\\n+ rows.extend(\\n+ (uid, int(prepared.item_ids[col]), rank)\\n+ for rank, col in enumerate(columns, 1)\\n+ )\\n+ pd.DataFrame(rows, columns=[\\\"uid\\\", \\\"item_id\\\", \\\"rank\\\"]).to_parquet(\\n+ HERE / f\\\"baseline_{name}_validation.parquet\\\", index=False\\n+ )\\n+\\n+\\n+if __name__ == \\\"__main__\\\":\\n+ main()\\n*** End Patch\"; text(await tools.apply_patch(patch));" } ### Tool output · 2026-09-24T22:56:41.174Z Script completed Wall time 0.0 seconds Output: {} ### Harness context { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-87c2c4cb-7e8d-409c-a304-a0dd00b75f5f", "changes": { "/app/solution/analyze_scores.py": { "type": "add", "content": "#!/usr/bin/env python3\n\"\"\"Local public-validation diagnostics for a saved two-tower checkpoint.\"\"\"\nfrom pathlib import Path\nimport sys\n\nimport numpy as np\nimport pandas as pd\nimport torch\n\nHERE = Path(__file__).resolve().parent\nsys.path.insert(0, str(HERE))\nimport train_current_model as trainer\nimport two_tower_model as base\n\n\ndef main() -> None:\n torch.set_num_threads(8)\n config = trainer.Config()\n prepared = trainer.load_cache(\n HERE / \"validation_sequences.npz\", \"validation\", config\n )\n candidates = trainer.CandidateSet.read(\n Path(\"/task/data/validation_eligible_candidates.npz\")\n )\n model = base.SequentialTwoTower(\n prepared.item_features, trainer.model_config(config)\n )\n payload = torch.load(\n HERE / \"baseline_checkpoints/epoch-01.pt\",\n map_location=\"cpu\",\n weights_only=False,\n )\n model.load_state_dict(payload[\"model_state\"])\n model.eval()\n with torch.inference_mode():\n queries = trainer.inference_queries(model, prepared, config, torch.device(\"cpu\"))\n items = torch.cat(\n [\n model.encode_items(torch.arange(s, min(s + 2048, len(prepared.item_ids))))\n for s in range(0, len(prepared.item_ids), 2048)\n ]\n )\n np.savez(\n HERE / \"baseline_vectors.npz\",\n queries=queries.numpy(),\n items=items.numpy(),\n )\n\n item_norm = torch.linalg.vector_norm(items, dim=1).clamp_min(1e-8)\n # Dividing only by item norm changes rank; query normalization does not.\n variants = {\n \"raw\": items,\n \"norm025\": items / item_norm[:, None].pow(0.25),\n \"norm050\": items / item_norm[:, None].pow(0.50),\n \"norm075\": items / item_norm[:, None].pow(0.75),\n \"cosine\": items / item_norm[:, None],\n }\n for name, item_vectors in variants.items():\n rows: list[tuple[int, int, int]] = []\n for start in range(0, len(candidates.uids), 64):\n end = min(start + 64, len(candidates.uids))\n scores = queries[start:end] @ item_vectors.T\n eligible = np.unpackbits(\n candidates.packed[start:end],\n axis=1,\n count=len(prepared.item_ids),\n bitorder=\"little\",\n ).astype(bool, copy=False)\n scores.masked_fill_(~torch.from_numpy(eligible), -torch.inf)\n selected = torch.topk(scores, k=base.TOP_N, dim=1).indices.numpy()\n for local, columns in enumerate(selected):\n uid = int(candidates.uids[start + local])\n rows.extend(\n (uid, int(prepared.item_ids[col]), rank)\n for rank, col in enumerate(columns, 1)\n )\n pd.DataFrame(rows, columns=[\"uid\", \"item_id\", \"rank\"]).to_parquet(\n HERE / f\"baseline_{name}_validation.parquet\", index=False\n )\n\n\nif __name__ == \"__main__\":\n main()\n" } }, "status": "completed", "stdout": "Success. Updated the following files:\nA /app/solution/analyze_scores.py\n", "stderr": "" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "session_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "root_turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "response_id": "resp_08539f19231b9e9f016ab5aa8b5fd487d2b71afca86f600105", "usage": { "input_tokens": 57540, "cached_input_tokens": 57216, "cache_write_input_tokens": 0, "output_tokens": 1615, "reasoning_output_tokens": 605, "total_tokens": 59155 }, "turn_token_usage": { "input_tokens": 1225319, "cached_input_tokens": 1174272, "cache_write_input_tokens": 0, "output_tokens": 6436, "reasoning_output_tokens": 2425, "total_tokens": 1231755 }, "thread_token_usage": { "input_tokens": 1225319, "cached_input_tokens": 1174272, "cache_write_input_tokens": 0, "output_tokens": 6436, "reasoning_output_tokens": 2425, "total_tokens": 1231755 } } ## Step 37 · agent · 2026-09-24T22:56:46.530Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T22:56:46.531Z { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python /app/solution/analyze_scores.py 2>&1 | tee /app/solution/score_diagnostics.log\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":5000}); text(JSON.stringify(r));" } ### Tool output · 2026-09-24T22:56:47.706Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"66a443","wall_time_seconds":1.001296891,"session_id":6218,"original_token_count":0,"output":""} ### Harness context { "type": "token_usage_record", "thread_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "session_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "root_turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "response_id": "resp_08539f19231b9e9f016ab5aaa984d087d2beae41d8db6af616", "usage": { "input_tokens": 59179, "cached_input_tokens": 57344, "cache_write_input_tokens": 0, "output_tokens": 96, "reasoning_output_tokens": 17, "total_tokens": 59275 }, "turn_token_usage": { "input_tokens": 1284498, "cached_input_tokens": 1231616, "cache_write_input_tokens": 0, "output_tokens": 6532, "reasoning_output_tokens": 2442, "total_tokens": 1291030 }, "thread_token_usage": { "input_tokens": 1284498, "cached_input_tokens": 1231616, "cache_write_input_tokens": 0, "output_tokens": 6532, "reasoning_output_tokens": 2442, "total_tokens": 1291030 } } ## Step 38 · agent · 2026-09-24T22:56:49.030Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T22:56:49.748Z { "code": "const r = await tools.write_stdin({\"session_id\":6218,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":5000}); text(r.output);\n" } ### Tool output · 2026-09-24T22:57:19.759Z Script completed Wall time 30.0 seconds Output: loaded full-sequence cache: tokens=20,056,329 targets=20,047,194 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "session_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "root_turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "response_id": "resp_08539f19231b9e9f016ab5aaafe34087d28981d9a5671e016d", "usage": { "input_tokens": 59332, "cached_input_tokens": 59008, "cache_write_input_tokens": 0, "output_tokens": 68, "reasoning_output_tokens": 21, "total_tokens": 59400 }, "turn_token_usage": { "input_tokens": 1343830, "cached_input_tokens": 1290624, "cache_write_input_tokens": 0, "output_tokens": 6600, "reasoning_output_tokens": 2463, "total_tokens": 1350430 }, "thread_token_usage": { "input_tokens": 1343830, "cached_input_tokens": 1290624, "cache_write_input_tokens": 0, "output_tokens": 6600, "reasoning_output_tokens": 2463, "total_tokens": 1350430 } } ## Step 39 · agent · 2026-09-24T22:57:20.920Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T22:57:21.630Z { "code": "const r = await tools.write_stdin({\"session_id\":6218,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":5000}); text(r.output);\n" } ### Tool output · 2026-09-24T22:57:51.659Z Script completed Wall time 30.0 seconds Output: ### Harness context { "type": "token_usage_record", "thread_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "session_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "root_turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "response_id": "resp_08539f19231b9e9f016ab5aad00c2887d291386a44c9189275", "usage": { "input_tokens": 59444, "cached_input_tokens": 59136, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 59497 }, "turn_token_usage": { "input_tokens": 1403274, "cached_input_tokens": 1349760, "cache_write_input_tokens": 0, "output_tokens": 6653, "reasoning_output_tokens": 2469, "total_tokens": 1409927 }, "thread_token_usage": { "input_tokens": 1403274, "cached_input_tokens": 1349760, "cache_write_input_tokens": 0, "output_tokens": 6653, "reasoning_output_tokens": 2469, "total_tokens": 1409927 } } ## Step 40 · agent · 2026-09-24T22:57:52.888Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T22:57:53.587Z { "code": "const r = await tools.write_stdin({\"session_id\":6218,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":5000}); text(r.output);\n" } ### Tool output · 2026-09-24T22:58:20.444Z Script completed Wall time 26.8 seconds Output: ### Harness context { "type": "token_usage_record", "thread_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "session_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "root_turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "response_id": "resp_08539f19231b9e9f016ab5aaefe63087d2a0c21dcdad3387f1", "usage": { "input_tokens": 59520, "cached_input_tokens": 59264, "cache_write_input_tokens": 0, "output_tokens": 57, "reasoning_output_tokens": 10, "total_tokens": 59577 }, "turn_token_usage": { "input_tokens": 1462794, "cached_input_tokens": 1409024, "cache_write_input_tokens": 0, "output_tokens": 6710, "reasoning_output_tokens": 2479, "total_tokens": 1469504 }, "thread_token_usage": { "input_tokens": 1462794, "cached_input_tokens": 1409024, "cache_write_input_tokens": 0, "output_tokens": 6710, "reasoning_output_tokens": 2479, "total_tokens": 1469504 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-da818fe4-a8ce-4978-9489-7a522fc34ecf", "process_id": "6218", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python /app/solution/analyze_scores.py 2>&1 | tee /app/solution/score_diagnostics.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python /app/solution/analyze_scores.py 2>&1 | tee /app/solution/score_diagnostics.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "loaded full-sequence cache: tokens=20,056,329 targets=20,047,194\n", "stderr": "", "aggregated_output": "loaded full-sequence cache: tokens=20,056,329 targets=20,047,194\n", "exit_code": 0, "duration": { "secs": 93, "nanos": 730659485 }, "formatted_output": "loaded full-sequence cache: tokens=20,056,329 targets=20,047,194\n" } } ## Step 41 · agent · 2026-09-24T22:58:22.239Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T22:58:23.673Z { "code": "const r = await tools.exec_command({\"cmd\":\"for f in /app/solution/baseline_*_validation.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \\\"$f\\\"; done\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":10000}); text(r.output);" } ### Tool output · 2026-09-24T22:58:40.606Z Script completed Wall time 16.9 seconds Output: { "model": "/app/solution/baseline_cosine_validation.parquet", "scores": { "quality_ndcg@10": 0.012964, "quality_users": 4263 } } { "model": "/app/solution/baseline_norm025_validation.parquet", "scores": { "quality_ndcg@10": 0.021565, "quality_users": 4263 } } { "model": "/app/solution/baseline_norm050_validation.parquet", "scores": { "quality_ndcg@10": 0.020877, "quality_users": 4263 } } { "model": "/app/solution/baseline_norm075_validation.parquet", "scores": { "quality_ndcg@10": 0.017797, "quality_users": 4263 } } { "model": "/app/solution/baseline_raw_validation.parquet", "scores": { "quality_ndcg@10": 0.02128, "quality_users": 4263 } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "session_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "root_turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "response_id": "resp_08539f19231b9e9f016ab5ab0cca9487d2b2cc207a09884032", "usage": { "input_tokens": 59600, "cached_input_tokens": 59392, "cache_write_input_tokens": 0, "output_tokens": 105, "reasoning_output_tokens": 22, "total_tokens": 59705 }, "turn_token_usage": { "input_tokens": 1522394, "cached_input_tokens": 1468416, "cache_write_input_tokens": 0, "output_tokens": 6815, "reasoning_output_tokens": 2501, "total_tokens": 1529209 }, "thread_token_usage": { "input_tokens": 1522394, "cached_input_tokens": 1468416, "cache_write_input_tokens": 0, "output_tokens": 6815, "reasoning_output_tokens": 2501, "total_tokens": 1529209 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-7b81d391-10dc-47d1-9fc7-d362fa14b406", "process_id": "28532", "command": [ "/bin/sh", "-lc", "for f in /app/solution/baseline_*_validation.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \"$f\"; done" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "for f in /app/solution/baseline_*_validation.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \"$f\"; done" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{\n \"model\": \"/app/solution/baseline_cosine_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.012964,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/baseline_norm025_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.021565,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/baseline_norm050_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.020877,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/baseline_norm075_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.017797,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/baseline_raw_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.02128,\n \"quality_users\": 4263\n }\n}\n", "stderr": "", "aggregated_output": "{\n \"model\": \"/app/solution/baseline_cosine_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.012964,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/baseline_norm025_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.021565,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/baseline_norm050_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.020877,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/baseline_norm075_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.017797,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/baseline_raw_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.02128,\n \"quality_users\": 4263\n }\n}\n", "exit_code": 0, "duration": { "secs": 16, "nanos": 746520952 }, "formatted_output": "{\n \"model\": \"/app/solution/baseline_cosine_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.012964,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/baseline_norm025_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.021565,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/baseline_norm050_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.020877,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/baseline_norm075_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.017797,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/baseline_raw_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.02128,\n \"quality_users\": 4263\n }\n}\n" } } ## Step 42 · agent · 2026-09-24T22:58:51.721Z ### Thinking unavailable — no text recorded ## Step 43 · agent · 2026-09-24T22:59:00.961Z ### Thinking unavailable — no text recorded ## Step 44 · agent · 2026-09-24T22:59:09.980Z ### Thinking unavailable — no text recorded ## Step 45 · agent · 2026-09-24T22:59:19.821Z ### Thinking unavailable — no text recorded ## Step 46 · agent · 2026-09-24T22:59:25.580Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T22:59:57.289Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/train_current_model.py\\n@@\\n class Config:\\n@@\\n uniform_negatives: int = 24\\n inbatch_negatives: int = 8\\n+ negative_sampling: str = \\\"mixed\\\"\\n+ hard_pool_size: int = 512\\n@@\\n def invalid_full_history_negatives(\\n@@\\n return invalid\\n+\\n+\\n+def invalid_known_negatives(\\n+ negatives: np.ndarray,\\n+ targets: np.ndarray,\\n+ user_rows: np.ndarray,\\n+ known_positive_packed: np.ndarray,\\n+ liked_items: np.ndarray,\\n+) -> np.ndarray:\\n+ \\\"\\\"\\\"Mask known positives without the quadratic duplicate-column check.\\\"\\\"\\\"\\n+ byte_columns = negatives >> 3\\n+ bit_columns = negatives & 7\\n+ known = known_positive_packed[user_rows[:, None], byte_columns]\\n+ invalid = ((known >> bit_columns) & 1).astype(bool)\\n+ invalid |= negatives == targets[:, None]\\n+ invalid |= (negatives[:, :, None] == liked_items[:, None, :]).any(axis=2)\\n+ return invalid\\n+\\n+\\n+@torch.no_grad()\\n+def draw_hard_mixed_negatives(\\n+ model: base.SequentialTwoTower,\\n+ sequence_vectors: torch.Tensor,\\n+ flat_states: np.ndarray,\\n+ flat_activity: np.ndarray,\\n+ targets: torch.Tensor,\\n+ user_rows: np.ndarray,\\n+ liked_items: torch.Tensor,\\n+ known_positive_packed: np.ndarray,\\n+ prepared: SequenceData,\\n+ config: Config,\\n+ generator: torch.Generator,\\n+ device: torch.device,\\n+) -> tuple[torch.Tensor, torch.Tensor]:\\n+ \\\"\\\"\\\"Mine query-confusable negatives from a bounded mixed proposal pool.\\n+\\n+ The shared pool combines frequency-weighted positive targets from the\\n+ current chronological block with catalog-uniform exploration. Mining is\\n+ detached; only the selected examples are re-encoded by the BCE loss, so\\n+ request-time scoring and gradient memory remain those of a two-tower.\\n+ \\\"\\\"\\\"\\n+ targets_cpu = targets.detach().cpu().numpy().astype(np.int64, copy=False)\\n+ liked_cpu = liked_items.detach().cpu().numpy().astype(np.int64, copy=False)\\n+ batch_size = len(targets_cpu)\\n+ n_items = len(prepared.item_ids)\\n+ hard_count = config.inbatch_negatives\\n+\\n+ # Half the proposal pool follows the empirical positive distribution.\\n+ # Sampling unique IDs without replacement retains broad tail coverage while\\n+ # ensuring popular targets are proposed more often than under catalog-uniform.\\n+ unique_targets, target_counts = np.unique(targets_cpu, return_counts=True)\\n+ positive_budget = min(\\n+ max(config.hard_pool_size // 2, hard_count), len(unique_targets)\\n+ )\\n+ if positive_budget:\\n+ chosen = torch.multinomial(\\n+ torch.from_numpy(target_counts.astype(np.float32)),\\n+ positive_budget,\\n+ replacement=False,\\n+ generator=generator,\\n+ ).numpy()\\n+ positive_pool = unique_targets[chosen]\\n+ else:\\n+ positive_pool = np.empty(0, dtype=np.int64)\\n+ uniform_pool = torch.randint(\\n+ n_items,\\n+ (config.hard_pool_size - len(positive_pool),),\\n+ generator=generator,\\n+ device=\\\"cpu\\\",\\n+ ).numpy()\\n+ pool = np.unique(np.concatenate([positive_pool, uniform_pool]))\\n+ while len(pool) < max(hard_count + 1, config.hard_pool_size // 2):\\n+ filler = torch.randint(\\n+ n_items,\\n+ (config.hard_pool_size - len(pool),),\\n+ generator=generator,\\n+ device=\\\"cpu\\\",\\n+ ).numpy()\\n+ pool = np.unique(np.concatenate([pool, filler]))\\n+\\n+ pool_matrix = np.broadcast_to(pool[None, :], (batch_size, len(pool)))\\n+ invalid_pool = invalid_known_negatives(\\n+ pool_matrix,\\n+ targets_cpu,\\n+ user_rows,\\n+ known_positive_packed,\\n+ liked_cpu,\\n+ )\\n+\\n+ liked_vectors, disliked_vectors, _ = pool_explicit_states(\\n+ model, prepared, flat_states, device\\n+ )\\n+ counts = prepared.state_counts[flat_states]\\n+ numeric = np.zeros((batch_size, 6), dtype=np.float32)\\n+ numeric[:, :4] = flat_activity\\n+ numeric[:, 4] = np.log1p(counts[:, 0]) / 6.0\\n+ numeric[:, 5] = np.log1p(counts[:, 1]) / 4.0\\n+ queries = user_queries(\\n+ model,\\n+ sequence_vectors.detach(),\\n+ liked_vectors,\\n+ disliked_vectors,\\n+ torch.from_numpy(numeric).to(device),\\n+ )\\n+ pool_tensor = torch.from_numpy(pool).to(device)\\n+ pool_vectors = model.encode_items(pool_tensor)\\n+ scores = queries @ pool_vectors.T\\n+ scores.masked_fill_(torch.from_numpy(invalid_pool).to(device), -torch.inf)\\n+ if bool(torch.isinf(scores.topk(hard_count, dim=1).values).any()):\\n+ raise RuntimeError(\\\"hard-negative proposal pool has too few valid items\\\")\\n+ hard = pool_tensor[scores.topk(hard_count, dim=1).indices]\\n+\\n+ uniform = torch.randint(\\n+ n_items,\\n+ (batch_size, config.uniform_negatives),\\n+ generator=generator,\\n+ device=\\\"cpu\\\",\\n+ ).numpy()\\n+ negatives = np.concatenate([uniform, hard.cpu().numpy()], axis=1)\\n+ correction = np.ones_like(negatives, dtype=np.float32)\\n+ # Apply the mature full-history filter to all selected examples and repair\\n+ # the rare uniform/hard collision. Repaired entries are uniform negatives.\\n+ for _ in range(32):\\n+ invalid = invalid_full_history_negatives(\\n+ negatives,\\n+ targets_cpu,\\n+ user_rows,\\n+ known_positive_packed,\\n+ liked_cpu,\\n+ )\\n+ if not np.any(invalid):\\n+ return (\\n+ torch.from_numpy(negatives).to(device),\\n+ torch.from_numpy(correction).to(device),\\n+ )\\n+ negatives[invalid] = torch.randint(\\n+ n_items,\\n+ (int(invalid.sum()),),\\n+ generator=generator,\\n+ device=\\\"cpu\\\",\\n+ ).numpy()\\n+ raise RuntimeError(\\\"hard-negative resampling failed\\\")\\n@@\\n def draw_training_negatives(\\n+ model: base.SequentialTwoTower,\\n+ sequence_vectors: torch.Tensor,\\n+ flat_states: np.ndarray,\\n+ flat_activity: np.ndarray,\\n@@\\n ) -> tuple[torch.Tensor, torch.Tensor]:\\n+ if config.negative_sampling == \\\"hard-mixed\\\":\\n+ if known_positive_packed is None:\\n+ raise RuntimeError(\\\"hard mining requires the full-history filter\\\")\\n+ return draw_hard_mixed_negatives(\\n+ model,\\n+ sequence_vectors,\\n+ flat_states,\\n+ flat_activity,\\n+ positive_items,\\n+ user_rows,\\n+ liked_items,\\n+ known_positive_packed,\\n+ prepared,\\n+ config,\\n+ generator,\\n+ device,\\n+ )\\n if config.negative_filter_scope == \\\"full-training-history\\\":\\n*** End Patch\"; text(await tools.apply_patch(patch));" } ### Tool output · 2026-09-24T22:59:57.345Z Script failed Wall time 0.0 seconds Output: Script error: Exit code: 1 Wall time: 0 seconds Output: Failed to write file /app/solution/train_current_model.py ### Harness context { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-1e777e56-7114-4e68-8179-8c8ec40505d6", "changes": { "/app/solution/train_current_model.py": { "type": "update", "unified_diff": "@@ -57,2 +57,4 @@\n inbatch_negatives: int = 8\n+ negative_sampling: str = \"mixed\"\n+ hard_pool_size: int = 512\n inbatch_correction_power: float = 0.5\n@@ -629,2 +631,144 @@\n \n+def invalid_known_negatives(\n+ negatives: np.ndarray,\n+ targets: np.ndarray,\n+ user_rows: np.ndarray,\n+ known_positive_packed: np.ndarray,\n+ liked_items: np.ndarray,\n+) -> np.ndarray:\n+ \"\"\"Mask known positives without the quadratic duplicate-column check.\"\"\"\n+ byte_columns = negatives >> 3\n+ bit_columns = negatives & 7\n+ known = known_positive_packed[user_rows[:, None], byte_columns]\n+ invalid = ((known >> bit_columns) & 1).astype(bool)\n+ invalid |= negatives == targets[:, None]\n+ invalid |= (negatives[:, :, None] == liked_items[:, None, :]).any(axis=2)\n+ return invalid\n+\n+\n+@torch.no_grad()\n+def draw_hard_mixed_negatives(\n+ model: base.SequentialTwoTower,\n+ sequence_vectors: torch.Tensor,\n+ flat_states: np.ndarray,\n+ flat_activity: np.ndarray,\n+ targets: torch.Tensor,\n+ user_rows: np.ndarray,\n+ liked_items: torch.Tensor,\n+ known_positive_packed: np.ndarray,\n+ prepared: SequenceData,\n+ config: Config,\n+ generator: torch.Generator,\n+ device: torch.device,\n+) -> tuple[torch.Tensor, torch.Tensor]:\n+ \"\"\"Mine query-confusable negatives from a bounded mixed proposal pool.\n+\n+ The shared pool combines frequency-weighted positive targets from the\n+ current chronological block with catalog-uniform exploration. Mining is\n+ detached; only the selected examples are re-encoded by the BCE loss, so\n+ request-time scoring and gradient memory remain those of a two-tower.\n+ \"\"\"\n+ targets_cpu = targets.detach().cpu().numpy().astype(np.int64, copy=False)\n+ liked_cpu = liked_items.detach().cpu().numpy().astype(np.int64, copy=False)\n+ batch_size = len(targets_cpu)\n+ n_items = len(prepared.item_ids)\n+ hard_count = config.inbatch_negatives\n+\n+ # Half the proposal pool follows the empirical positive distribution.\n+ # Sampling unique IDs without replacement retains broad tail coverage while\n+ # ensuring popular targets are proposed more often than under catalog-uniform.\n+ unique_targets, target_counts = np.unique(targets_cpu, return_counts=True)\n+ positive_budget = min(\n+ max(config.hard_pool_size // 2, hard_count), len(unique_targets)\n+ )\n+ if positive_budget:\n+ chosen = torch.multinomial(\n+ torch.from_numpy(target_counts.astype(np.float32)),\n+ positive_budget,\n+ replacement=False,\n+ generator=generator,\n+ ).numpy()\n+ positive_pool = unique_targets[chosen]\n+ else:\n+ positive_pool = np.empty(0, dtype=np.int64)\n+ uniform_pool = torch.randint(\n+ n_items,\n+ (config.hard_pool_size - len(positive_pool),),\n+ generator=generator,\n+ device=\"cpu\",\n+ ).numpy()\n+ pool = np.unique(np.concatenate([positive_pool, uniform_pool]))\n+ while len(pool) < max(hard_count + 1, config.hard_pool_size // 2):\n+ filler = torch.randint(\n+ n_items,\n+ (config.hard_pool_size - len(pool),),\n+ generator=generator,\n+ device=\"cpu\",\n+ ).numpy()\n+ pool = np.unique(np.concatenate([pool, filler]))\n+\n+ pool_matrix = np.broadcast_to(pool[None, :], (batch_size, len(pool)))\n+ invalid_pool = invalid_known_negatives(\n+ pool_matrix,\n+ targets_cpu,\n+ user_rows,\n+ known_positive_packed,\n+ liked_cpu,\n+ )\n+\n+ liked_vectors, disliked_vectors, _ = pool_explicit_states(\n+ model, prepared, flat_states, device\n+ )\n+ counts = prepared.state_counts[flat_states]\n+ numeric = np.zeros((batch_size, 6), dtype=np.float32)\n+ numeric[:, :4] = flat_activity\n+ numeric[:, 4] = np.log1p(counts[:, 0]) / 6.0\n+ numeric[:, 5] = np.log1p(counts[:, 1]) / 4.0\n+ queries = user_queries(\n+ model,\n+ sequence_vectors.detach(),\n+ liked_vectors,\n+ disliked_vectors,\n+ torch.from_numpy(numeric).to(device),\n+ )\n+ pool_tensor = torch.from_numpy(pool).to(device)\n+ pool_vectors = model.encode_items(pool_tensor)\n+ scores = queries @ pool_vectors.T\n+ scores.masked_fill_(torch.from_numpy(invalid_pool).to(device), -torch.inf)\n+ if bool(torch.isinf(scores.topk(hard_count, dim=1).values).any()):\n+ raise RuntimeError(\"hard-negative proposal pool has too few valid items\")\n+ hard = pool_tensor[scores.topk(hard_count, dim=1).indices]\n+\n+ uniform = torch.randint(\n+ n_items,\n+ (batch_size, config.uniform_negatives),\n+ generator=generator,\n+ device=\"cpu\",\n+ ).numpy()\n+ negatives = np.concatenate([uniform, hard.cpu().numpy()], axis=1)\n+ correction = np.ones_like(negatives, dtype=np.float32)\n+ # Apply the mature full-history filter to all selected examples and repair\n+ # the rare uniform/hard collision. Repaired entries are uniform negatives.\n+ for _ in range(32):\n+ invalid = invalid_full_history_negatives(\n+ negatives,\n+ targets_cpu,\n+ user_rows,\n+ known_positive_packed,\n+ liked_cpu,\n+ )\n+ if not np.any(invalid):\n+ return (\n+ torch.from_numpy(negatives).to(device),\n+ torch.from_numpy(correction).to(device),\n+ )\n+ negatives[invalid] = torch.randint(\n+ n_items,\n+ (int(invalid.sum()),),\n+ generator=generator,\n+ device=\"cpu\",\n+ ).numpy()\n+ raise RuntimeError(\"hard-negative resampling failed\")\n+\n+\n def draw_full_history_filtered_negatives(\n@@ -724,2 +868,6 @@\n def draw_training_negatives(\n+ model: base.SequentialTwoTower,\n+ sequence_vectors: torch.Tensor,\n+ flat_states: np.ndarray,\n+ flat_activity: np.ndarray,\n positive_items: torch.Tensor,\n@@ -735,2 +883,19 @@\n ) -> tuple[torch.Tensor, torch.Tensor]:\n+ if config.negative_sampling == \"hard-mixed\":\n+ if known_positive_packed is None:\n+ raise RuntimeError(\"hard mining requires the full-history filter\")\n+ return draw_hard_mixed_negatives(\n+ model,\n+ sequence_vectors,\n+ flat_states,\n+ flat_activity,\n+ positive_items,\n+ user_rows,\n+ liked_items,\n+ known_positive_packed,\n+ prepared,\n+ config,\n+ generator,\n+ device,\n+ )\n if config.negative_filter_scope == \"full-training-history\":\n", "move_path": null } }, "status": "failed", "stdout": "", "stderr": "Failed to write file /app/solution/train_current_model.py\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "session_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "root_turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "response_id": "resp_08539f19231b9e9f016ab5ab20d8f887d291fe4987b3cd254d", "usage": { "input_tokens": 59972, "cached_input_tokens": 59392, "cache_write_input_tokens": 0, "output_tokens": 4182, "reasoning_output_tokens": 2373, "total_tokens": 64154 }, "turn_token_usage": { "input_tokens": 1582366, "cached_input_tokens": 1527808, "cache_write_input_tokens": 0, "output_tokens": 10997, "reasoning_output_tokens": 4874, "total_tokens": 1593363 }, "thread_token_usage": { "input_tokens": 1582366, "cached_input_tokens": 1527808, "cache_write_input_tokens": 0, "output_tokens": 10997, "reasoning_output_tokens": 4874, "total_tokens": 1593363 } } ## Step 47 · agent · 2026-09-24T23:00:00.473Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T23:00:01.891Z { "code": "const r = await tools.exec_command({\"cmd\":\"ls -l /app/solution/train_current_model.py /app/solution/analyze_scores.py; stat -c '%a %U %G' /app/solution/train_current_model.py\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":2000}); text(r.output);" } ### Tool output · 2026-09-24T23:00:02.007Z Script completed Wall time 0.0 seconds Output: -rw-r--r-- 1 agent agent 2929 Sep 24 22:56 /app/solution/analyze_scores.py -r--r--r-- 1 agent agent 80058 Sep 24 22:49 /app/solution/train_current_model.py 444 agent agent ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-45cb569b-5114-4f52-834e-64158a59a30f", "process_id": "29524", "command": [ "/bin/sh", "-lc", "ls -l /app/solution/train_current_model.py /app/solution/analyze_scores.py; stat -c '%a %U %G' /app/solution/train_current_model.py" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "ls -l /app/solution/train_current_model.py /app/solution/analyze_scores.py; stat -c '%a %U %G' /app/solution/train_current_model.py" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "-rw-r--r-- 1 agent agent 2929 Sep 24 22:56 /app/solution/analyze_scores.py\n-r--r--r-- 1 agent agent 80058 Sep 24 22:49 /app/solution/train_current_model.py\n444 agent agent\n", "stderr": "", "aggregated_output": "-rw-r--r-- 1 agent agent 2929 Sep 24 22:56 /app/solution/analyze_scores.py\n-r--r--r-- 1 agent agent 80058 Sep 24 22:49 /app/solution/train_current_model.py\n444 agent agent\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4340 }, "formatted_output": "-rw-r--r-- 1 agent agent 2929 Sep 24 22:56 /app/solution/analyze_scores.py\n-r--r--r-- 1 agent agent 80058 Sep 24 22:49 /app/solution/train_current_model.py\n444 agent agent\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "session_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "root_turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "response_id": "resp_08539f19231b9e9f016ab5ab6dad7487d2856033d90a60df4a", "usage": { "input_tokens": 64208, "cached_input_tokens": 59776, "cache_write_input_tokens": 0, "output_tokens": 108, "reasoning_output_tokens": 23, "total_tokens": 64316 }, "turn_token_usage": { "input_tokens": 1646574, "cached_input_tokens": 1587584, "cache_write_input_tokens": 0, "output_tokens": 11105, "reasoning_output_tokens": 4897, "total_tokens": 1657679 }, "thread_token_usage": { "input_tokens": 1646574, "cached_input_tokens": 1587584, "cache_write_input_tokens": 0, "output_tokens": 11105, "reasoning_output_tokens": 4897, "total_tokens": 1657679 } } ## Step 48 · agent · 2026-09-24T23:00:05.859Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T23:00:06.835Z { "code": "const r = await tools.exec_command({\"cmd\":\"chmod u+w /app/solution/train_current_model.py /app/solution/two_tower_model.py\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":2000}); text(r.output);" } ### Tool output · 2026-09-24T23:00:06.889Z Script completed Wall time 0.0 seconds Output: ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-284df2d4-3be9-4418-8cb9-5bf7fbcaf54b", "process_id": "56693", "command": [ "/bin/sh", "-lc", "chmod u+w /app/solution/train_current_model.py /app/solution/two_tower_model.py" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "chmod u+w /app/solution/train_current_model.py /app/solution/two_tower_model.py" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 0, "nanos": 3521 }, "formatted_output": "" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "session_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "root_turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "response_id": "resp_08539f19231b9e9f016ab5ab724cac87d2a3062a7afb523394", "usage": { "input_tokens": 64402, "cached_input_tokens": 64000, "cache_write_input_tokens": 0, "output_tokens": 95, "reasoning_output_tokens": 27, "total_tokens": 64497 }, "turn_token_usage": { "input_tokens": 1710976, "cached_input_tokens": 1651584, "cache_write_input_tokens": 0, "output_tokens": 11200, "reasoning_output_tokens": 4924, "total_tokens": 1722176 }, "thread_token_usage": { "input_tokens": 1710976, "cached_input_tokens": 1651584, "cache_write_input_tokens": 0, "output_tokens": 11200, "reasoning_output_tokens": 4924, "total_tokens": 1722176 } } ## Step 49 · agent · 2026-09-24T23:00:09.600Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T23:00:44.414Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/train_current_model.py\\n@@\\n class Config:\\n@@\\n uniform_negatives: int = 24\\n inbatch_negatives: int = 8\\n+ negative_sampling: str = \\\"mixed\\\"\\n+ hard_pool_size: int = 512\\n@@\\n def invalid_full_history_negatives(\\n@@\\n return invalid\\n+\\n+\\n+def invalid_known_negatives(\\n+ negatives: np.ndarray,\\n+ targets: np.ndarray,\\n+ user_rows: np.ndarray,\\n+ known_positive_packed: np.ndarray,\\n+ liked_items: np.ndarray,\\n+) -> np.ndarray:\\n+ \\\"\\\"\\\"Mask known positives without the quadratic duplicate-column check.\\\"\\\"\\\"\\n+ byte_columns = negatives >> 3\\n+ bit_columns = negatives & 7\\n+ known = known_positive_packed[user_rows[:, None], byte_columns]\\n+ invalid = ((known >> bit_columns) & 1).astype(bool)\\n+ invalid |= negatives == targets[:, None]\\n+ invalid |= (negatives[:, :, None] == liked_items[:, None, :]).any(axis=2)\\n+ return invalid\\n+\\n+\\n+@torch.no_grad()\\n+def draw_hard_mixed_negatives(\\n+ model: base.SequentialTwoTower,\\n+ sequence_vectors: torch.Tensor,\\n+ flat_states: np.ndarray,\\n+ flat_activity: np.ndarray,\\n+ targets: torch.Tensor,\\n+ user_rows: np.ndarray,\\n+ liked_items: torch.Tensor,\\n+ known_positive_packed: np.ndarray,\\n+ prepared: SequenceData,\\n+ config: Config,\\n+ generator: torch.Generator,\\n+ device: torch.device,\\n+) -> tuple[torch.Tensor, torch.Tensor]:\\n+ \\\"\\\"\\\"Mine query-confusable negatives from a bounded mixed proposal pool.\\n+\\n+ The shared pool combines frequency-weighted positive targets from the\\n+ current chronological block with catalog-uniform exploration. Mining is\\n+ detached; only selected examples are re-encoded by the BCE loss.\\n+ \\\"\\\"\\\"\\n+ targets_cpu = targets.detach().cpu().numpy().astype(np.int64, copy=False)\\n+ liked_cpu = liked_items.detach().cpu().numpy().astype(np.int64, copy=False)\\n+ batch_size = len(targets_cpu)\\n+ n_items = len(prepared.item_ids)\\n+ hard_count = config.inbatch_negatives\\n+\\n+ unique_targets, target_counts = np.unique(targets_cpu, return_counts=True)\\n+ positive_budget = min(\\n+ max(config.hard_pool_size // 2, hard_count), len(unique_targets)\\n+ )\\n+ if positive_budget:\\n+ chosen = torch.multinomial(\\n+ torch.from_numpy(target_counts.astype(np.float32)),\\n+ positive_budget,\\n+ replacement=False,\\n+ generator=generator,\\n+ ).numpy()\\n+ positive_pool = unique_targets[chosen]\\n+ else:\\n+ positive_pool = np.empty(0, dtype=np.int64)\\n+ uniform_pool = torch.randint(\\n+ n_items,\\n+ (config.hard_pool_size - len(positive_pool),),\\n+ generator=generator,\\n+ device=\\\"cpu\\\",\\n+ ).numpy()\\n+ pool = np.unique(np.concatenate([positive_pool, uniform_pool]))\\n+ while len(pool) < max(hard_count + 1, config.hard_pool_size // 2):\\n+ filler = torch.randint(\\n+ n_items,\\n+ (config.hard_pool_size - len(pool),),\\n+ generator=generator,\\n+ device=\\\"cpu\\\",\\n+ ).numpy()\\n+ pool = np.unique(np.concatenate([pool, filler]))\\n+\\n+ pool_matrix = np.broadcast_to(pool[None, :], (batch_size, len(pool)))\\n+ invalid_pool = invalid_known_negatives(\\n+ pool_matrix,\\n+ targets_cpu,\\n+ user_rows,\\n+ known_positive_packed,\\n+ liked_cpu,\\n+ )\\n+\\n+ liked_vectors, disliked_vectors, _ = pool_explicit_states(\\n+ model, prepared, flat_states, device\\n+ )\\n+ counts = prepared.state_counts[flat_states]\\n+ numeric = np.zeros((batch_size, 6), dtype=np.float32)\\n+ numeric[:, :4] = flat_activity\\n+ numeric[:, 4] = np.log1p(counts[:, 0]) / 6.0\\n+ numeric[:, 5] = np.log1p(counts[:, 1]) / 4.0\\n+ queries = user_queries(\\n+ model,\\n+ sequence_vectors.detach(),\\n+ liked_vectors,\\n+ disliked_vectors,\\n+ torch.from_numpy(numeric).to(device),\\n+ )\\n+ pool_tensor = torch.from_numpy(pool).to(device)\\n+ pool_vectors = model.encode_items(pool_tensor)\\n+ scores = queries @ pool_vectors.T\\n+ scores.masked_fill_(torch.from_numpy(invalid_pool).to(device), -torch.inf)\\n+ hard_result = scores.topk(hard_count, dim=1)\\n+ if bool(torch.isinf(hard_result.values).any()):\\n+ raise RuntimeError(\\\"hard-negative proposal pool has too few valid items\\\")\\n+ hard = pool_tensor[hard_result.indices]\\n+\\n+ uniform = torch.randint(\\n+ n_items,\\n+ (batch_size, config.uniform_negatives),\\n+ generator=generator,\\n+ device=\\\"cpu\\\",\\n+ ).numpy()\\n+ negatives = np.concatenate([uniform, hard.cpu().numpy()], axis=1)\\n+ correction = np.ones_like(negatives, dtype=np.float32)\\n+ for _ in range(32):\\n+ invalid = invalid_full_history_negatives(\\n+ negatives,\\n+ targets_cpu,\\n+ user_rows,\\n+ known_positive_packed,\\n+ liked_cpu,\\n+ )\\n+ if not np.any(invalid):\\n+ return (\\n+ torch.from_numpy(negatives).to(device),\\n+ torch.from_numpy(correction).to(device),\\n+ )\\n+ negatives[invalid] = torch.randint(\\n+ n_items,\\n+ (int(invalid.sum()),),\\n+ generator=generator,\\n+ device=\\\"cpu\\\",\\n+ ).numpy()\\n+ raise RuntimeError(\\\"hard-negative resampling failed\\\")\\n@@\\n def draw_training_negatives(\\n+ model: base.SequentialTwoTower,\\n+ sequence_vectors: torch.Tensor,\\n+ flat_states: np.ndarray,\\n+ flat_activity: np.ndarray,\\n@@\\n ) -> tuple[torch.Tensor, torch.Tensor]:\\n+ if config.negative_sampling == \\\"hard-mixed\\\":\\n+ if known_positive_packed is None:\\n+ raise RuntimeError(\\\"hard mining requires the full-history filter\\\")\\n+ return draw_hard_mixed_negatives(\\n+ model,\\n+ sequence_vectors,\\n+ flat_states,\\n+ flat_activity,\\n+ positive_items,\\n+ user_rows,\\n+ liked_items,\\n+ known_positive_packed,\\n+ prepared,\\n+ config,\\n+ generator,\\n+ device,\\n+ )\\n if config.negative_filter_scope == \\\"full-training-history\\\":\\n*** End Patch\"; text(await tools.apply_patch(patch));" } ### Tool output · 2026-09-24T23:00:44.505Z Script completed Wall time 0.0 seconds Output: {} ### Harness context { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-b61ded2d-edbc-40e1-8d46-82916bd54b69", "changes": { "/app/solution/train_current_model.py": { "type": "update", "unified_diff": "@@ -57,2 +57,4 @@\n inbatch_negatives: int = 8\n+ negative_sampling: str = \"mixed\"\n+ hard_pool_size: int = 512\n inbatch_correction_power: float = 0.5\n@@ -629,2 +631,139 @@\n \n+def invalid_known_negatives(\n+ negatives: np.ndarray,\n+ targets: np.ndarray,\n+ user_rows: np.ndarray,\n+ known_positive_packed: np.ndarray,\n+ liked_items: np.ndarray,\n+) -> np.ndarray:\n+ \"\"\"Mask known positives without the quadratic duplicate-column check.\"\"\"\n+ byte_columns = negatives >> 3\n+ bit_columns = negatives & 7\n+ known = known_positive_packed[user_rows[:, None], byte_columns]\n+ invalid = ((known >> bit_columns) & 1).astype(bool)\n+ invalid |= negatives == targets[:, None]\n+ invalid |= (negatives[:, :, None] == liked_items[:, None, :]).any(axis=2)\n+ return invalid\n+\n+\n+@torch.no_grad()\n+def draw_hard_mixed_negatives(\n+ model: base.SequentialTwoTower,\n+ sequence_vectors: torch.Tensor,\n+ flat_states: np.ndarray,\n+ flat_activity: np.ndarray,\n+ targets: torch.Tensor,\n+ user_rows: np.ndarray,\n+ liked_items: torch.Tensor,\n+ known_positive_packed: np.ndarray,\n+ prepared: SequenceData,\n+ config: Config,\n+ generator: torch.Generator,\n+ device: torch.device,\n+) -> tuple[torch.Tensor, torch.Tensor]:\n+ \"\"\"Mine query-confusable negatives from a bounded mixed proposal pool.\n+\n+ The shared pool combines frequency-weighted positive targets from the\n+ current chronological block with catalog-uniform exploration. Mining is\n+ detached; only selected examples are re-encoded by the BCE loss.\n+ \"\"\"\n+ targets_cpu = targets.detach().cpu().numpy().astype(np.int64, copy=False)\n+ liked_cpu = liked_items.detach().cpu().numpy().astype(np.int64, copy=False)\n+ batch_size = len(targets_cpu)\n+ n_items = len(prepared.item_ids)\n+ hard_count = config.inbatch_negatives\n+\n+ unique_targets, target_counts = np.unique(targets_cpu, return_counts=True)\n+ positive_budget = min(\n+ max(config.hard_pool_size // 2, hard_count), len(unique_targets)\n+ )\n+ if positive_budget:\n+ chosen = torch.multinomial(\n+ torch.from_numpy(target_counts.astype(np.float32)),\n+ positive_budget,\n+ replacement=False,\n+ generator=generator,\n+ ).numpy()\n+ positive_pool = unique_targets[chosen]\n+ else:\n+ positive_pool = np.empty(0, dtype=np.int64)\n+ uniform_pool = torch.randint(\n+ n_items,\n+ (config.hard_pool_size - len(positive_pool),),\n+ generator=generator,\n+ device=\"cpu\",\n+ ).numpy()\n+ pool = np.unique(np.concatenate([positive_pool, uniform_pool]))\n+ while len(pool) < max(hard_count + 1, config.hard_pool_size // 2):\n+ filler = torch.randint(\n+ n_items,\n+ (config.hard_pool_size - len(pool),),\n+ generator=generator,\n+ device=\"cpu\",\n+ ).numpy()\n+ pool = np.unique(np.concatenate([pool, filler]))\n+\n+ pool_matrix = np.broadcast_to(pool[None, :], (batch_size, len(pool)))\n+ invalid_pool = invalid_known_negatives(\n+ pool_matrix,\n+ targets_cpu,\n+ user_rows,\n+ known_positive_packed,\n+ liked_cpu,\n+ )\n+\n+ liked_vectors, disliked_vectors, _ = pool_explicit_states(\n+ model, prepared, flat_states, device\n+ )\n+ counts = prepared.state_counts[flat_states]\n+ numeric = np.zeros((batch_size, 6), dtype=np.float32)\n+ numeric[:, :4] = flat_activity\n+ numeric[:, 4] = np.log1p(counts[:, 0]) / 6.0\n+ numeric[:, 5] = np.log1p(counts[:, 1]) / 4.0\n+ queries = user_queries(\n+ model,\n+ sequence_vectors.detach(),\n+ liked_vectors,\n+ disliked_vectors,\n+ torch.from_numpy(numeric).to(device),\n+ )\n+ pool_tensor = torch.from_numpy(pool).to(device)\n+ pool_vectors = model.encode_items(pool_tensor)\n+ scores = queries @ pool_vectors.T\n+ scores.masked_fill_(torch.from_numpy(invalid_pool).to(device), -torch.inf)\n+ hard_result = scores.topk(hard_count, dim=1)\n+ if bool(torch.isinf(hard_result.values).any()):\n+ raise RuntimeError(\"hard-negative proposal pool has too few valid items\")\n+ hard = pool_tensor[hard_result.indices]\n+\n+ uniform = torch.randint(\n+ n_items,\n+ (batch_size, config.uniform_negatives),\n+ generator=generator,\n+ device=\"cpu\",\n+ ).numpy()\n+ negatives = np.concatenate([uniform, hard.cpu().numpy()], axis=1)\n+ correction = np.ones_like(negatives, dtype=np.float32)\n+ for _ in range(32):\n+ invalid = invalid_full_history_negatives(\n+ negatives,\n+ targets_cpu,\n+ user_rows,\n+ known_positive_packed,\n+ liked_cpu,\n+ )\n+ if not np.any(invalid):\n+ return (\n+ torch.from_numpy(negatives).to(device),\n+ torch.from_numpy(correction).to(device),\n+ )\n+ negatives[invalid] = torch.randint(\n+ n_items,\n+ (int(invalid.sum()),),\n+ generator=generator,\n+ device=\"cpu\",\n+ ).numpy()\n+ raise RuntimeError(\"hard-negative resampling failed\")\n+\n+\n def draw_full_history_filtered_negatives(\n@@ -724,2 +863,6 @@\n def draw_training_negatives(\n+ model: base.SequentialTwoTower,\n+ sequence_vectors: torch.Tensor,\n+ flat_states: np.ndarray,\n+ flat_activity: np.ndarray,\n positive_items: torch.Tensor,\n@@ -735,2 +878,19 @@\n ) -> tuple[torch.Tensor, torch.Tensor]:\n+ if config.negative_sampling == \"hard-mixed\":\n+ if known_positive_packed is None:\n+ raise RuntimeError(\"hard mining requires the full-history filter\")\n+ return draw_hard_mixed_negatives(\n+ model,\n+ sequence_vectors,\n+ flat_states,\n+ flat_activity,\n+ positive_items,\n+ user_rows,\n+ liked_items,\n+ known_positive_packed,\n+ prepared,\n+ config,\n+ generator,\n+ device,\n+ )\n if config.negative_filter_scope == \"full-training-history\":\n", "move_path": null } }, "status": "completed", "stdout": "Success. Updated the following files:\nM /app/solution/train_current_model.py\n", "stderr": "" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "session_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "root_turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "response_id": "resp_08539f19231b9e9f016ab5ab7755a087d2bfcda5e93607a3ed", "usage": { "input_tokens": 64520, "cached_input_tokens": 64256, "cache_write_input_tokens": 0, "output_tokens": 1716, "reasoning_output_tokens": 7, "total_tokens": 66236 }, "turn_token_usage": { "input_tokens": 1775496, "cached_input_tokens": 1715840, "cache_write_input_tokens": 0, "output_tokens": 12916, "reasoning_output_tokens": 4931, "total_tokens": 1788412 }, "thread_token_usage": { "input_tokens": 1775496, "cached_input_tokens": 1715840, "cache_write_input_tokens": 0, "output_tokens": 12916, "reasoning_output_tokens": 4931, "total_tokens": 1788412 } } ## Step 50 · agent · 2026-09-24T23:00:50.917Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T23:00:52.340Z { "code": "const r = await tools.exec_command({\"cmd\":\"rg -n -C 12 'draw_training_negatives\\\\(' /app/solution/train_current_model.py; sed -n '1920,2075p' /app/solution/train_current_model.py\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":20000}); text(r.output);" } ### Tool output · 2026-09-24T23:00:52.395Z Script completed Wall time 0.0 seconds Output: 851- user_rows, 852- known_positive_packed, 853- liked_cpu, 854- ) 855- if np.any(invalid): 856- raise RuntimeError("full-history negative resampling failed") 857- return ( 858- torch.from_numpy(negatives).to(device), 859- torch.from_numpy(correction).to(device), 860- ) 861- 862- 863:def draw_training_negatives( 864- model: base.SequentialTwoTower, 865- sequence_vectors: torch.Tensor, 866- flat_states: np.ndarray, 867- flat_activity: np.ndarray, 868- positive_items: torch.Tensor, 869- known_history: torch.Tensor, 870- liked_items: torch.Tensor, 871- user_rows: np.ndarray, 872- known_positive_packed: np.ndarray | None, 873- prepared: SequenceData, 874- config: Config, 875- item_correction: torch.Tensor, -- 1441- ).to(device) 1442- known_history = torch.from_numpy( 1443- block[6].reshape(-1, config.bptt_steps)[flat_scored].astype( 1444- np.int64 1445- ) 1446- ).to(device) 1447- liked_items = torch.from_numpy( 1448- prepared.liked_states[flat_states].astype(np.int64) 1449- ).to(device) 1450- flat_user_rows = np.broadcast_to( 1451- users[:, None], block[5].shape 1452- ).reshape(-1)[flat_scored] 1453: negatives, negative_weights = draw_training_negatives( 1454- positive_items, 1455- known_history, 1456- liked_items, 1457- flat_user_rows, 1458- known_positive_packed, 1459- prepared, 1460- config, 1461- item_correction, 1462- generator, 1463- device, 1464- ) 1465- optimizer.zero_grad(set_to_none=True) -- 1597- ).to(device) 1598- known_history = torch.from_numpy( 1599- block[6].reshape(-1, config.bptt_steps)[flat_scored].astype( 1600- np.int64 1601- ) 1602- ).to(device) 1603- liked_items = torch.from_numpy( 1604- prepared.liked_states[flat_states].astype(np.int64) 1605- ).to(device) 1606- flat_user_rows = np.broadcast_to( 1607- users[:, None], block[5].shape 1608- ).reshape(-1)[flat_scored] 1609: negatives, negative_weights = draw_training_negatives( 1610- positive_items, 1611- known_history, 1612- liked_items, 1613- flat_user_rows, 1614- known_positive_packed, 1615- prepared, 1616- config, 1617- item_correction, 1618- generator, 1619- device, 1620- ) 1621- statistics, _ = score_target_microbatches( f"validation_seconds={diagnostics['validation_seconds']:.1f}" ) release_accelerator_cache(device) if epochs_without_improvement >= config.early_stopping_patience: log( f"early stopping after epoch={epoch}; " f"best_epoch={best_epoch} best_validation_loss={best_loss:.6f}" ) break if best_state is None: raise RuntimeError("training did not produce a valid checkpoint") model.load_state_dict(best_state) model.to(device).eval() log( f"restored best validation-BCE checkpoint: epoch={best_epoch} " f"loss={best_loss:.6f}" ) return model, history @torch.inference_mode() def inference_queries( model: base.SequentialTwoTower, prepared: SequenceData, config: Config, device: torch.device, ) -> torch.Tensor: sequence_rows = prepared.inference_user_rows lengths = np.where( sequence_rows >= 0, prepared.lengths[np.maximum(sequence_rows, 0)], 0 ) ordered = np.argsort(lengths, kind="stable") result = torch.zeros( len(sequence_rows), config.embedding_dim, dtype=torch.float32 ) for group_start in range(0, len(ordered), config.user_batch_size): output_rows = ordered[group_start : group_start + config.user_batch_size] rows = sequence_rows[output_rows] group_lengths = lengths[output_rows] hidden = torch.zeros( 1, len(output_rows), config.embedding_dim, device=device ) for start in range(0, int(group_lengths.max(initial=0)), config.bptt_steps): steps = min( config.bptt_steps, int(group_lengths.max(initial=0)) - start, ) items = np.full( (len(output_rows), steps), len(prepared.item_ids), dtype=np.uint16, ) features = np.zeros((len(output_rows), steps, 3), dtype=np.float16) counts = np.zeros(len(output_rows), dtype=np.int32) for local_row, sequence_row in enumerate(rows): if sequence_row < 0: continue count = min(steps, max(int(group_lengths[local_row]) - start, 0)) if not count: continue offset = int(prepared.offsets[sequence_row]) selected = slice(offset + start, offset + start + count) items[local_row, :count] = prepared.sequence_items[selected] features[local_row, :count] = prepared.event_features[selected] counts[local_row] = count outputs, _ = event_sequence( model, torch.from_numpy(items.astype(np.int64)).to(device), torch.from_numpy(features.astype(np.float32)).to(device), hidden, ) next_rows: list[torch.Tensor] = [] for local_row, count in enumerate(counts): next_rows.append( outputs[local_row, count - 1] if count else hidden[0, local_row] ) hidden = torch.stack(next_rows).unsqueeze(0) liked_items = torch.from_numpy( prepared.inference_liked_items[output_rows].astype(np.int64) ).to(device) disliked_items = torch.from_numpy( prepared.inference_disliked_items[output_rows].astype(np.int64) ).to(device) queries = user_queries( model, hidden.squeeze(0), model._mean_context_channel(liked_items), model._mean_context_channel(disliked_items), torch.from_numpy( prepared.inference_user_features[output_rows].astype(np.float32) ).to(device), ) result[torch.from_numpy(output_rows.astype(np.int64))] = queries.cpu() return result @torch.inference_mode() def rank_candidates( model: base.SequentialTwoTower, prepared: SequenceData, eligibility: CandidateSet, config: Config, device: torch.device, ranking_batch_size: int, ) -> tuple[pd.DataFrame, dict[str, float]]: model.eval() queries = inference_queries(model, prepared, config, device) item_parts: list[torch.Tensor] = [] for start in range(0, len(prepared.item_ids), 2_048): rows = torch.arange( start, min(start + 2_048, len(prepared.item_ids)), dtype=torch.long, device=device, ) item_parts.append(model.encode_items(rows).cpu()) items = torch.cat(item_parts) rows: list[tuple[int, int, int]] = [] for start in range(0, len(eligibility.uids), ranking_batch_size): end = min(start + ranking_batch_size, len(eligibility.uids)) scores = queries[start:end] @ items.T eligible = np.unpackbits( eligibility.packed[start:end], axis=1, count=len(prepared.item_ids), bitorder="little", ).astype(bool, copy=False) scores.masked_fill_(~torch.from_numpy(eligible), -torch.inf) selected = torch.topk(scores, k=base.TOP_N, dim=1).indices.numpy() for local_row, columns in enumerate(selected): uid = int(eligibility.uids[start + local_row]) rows.extend( (uid, int(prepared.item_ids[column]), rank) for rank, column in enumerate(columns, start=1) ) if end == len(eligibility.uids) or end % 1_024 == 0: log(f"ranked users={end:,}/{len(eligibility.uids):,}") query_norm = torch.linalg.vector_norm(queries, dim=1).numpy() item_norm = torch.linalg.vector_norm(items, dim=1).numpy() diagnostics = { "query_norm_mean": float(query_norm.mean()), "query_norm_std": float(query_norm.std()), "item_norm_mean": float(item_norm.mean()), "item_norm_std": float(item_norm.std()), } return pd.DataFrame(rows, columns=["uid", "item_id", "rank"]), diagnostics def write_ranking(frame: pd.DataFrame, output: Path) -> None: output.parent.mkdir(parents=True, exist_ok=True) if output.suffix == ".parquet": frame.to_parquet(output, index=False, compression="zstd") ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-be31d74e-cf43-46c9-8b55-cdaf190c414c", "process_id": "53784", "command": [ "/bin/sh", "-lc", "rg -n -C 12 'draw_training_negatives\\(' /app/solution/train_current_model.py; sed -n '1920,2075p' /app/solution/train_current_model.py" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "search", "cmd": "rg -n -C 12 \"draw_training_negatives\\\\(\" /app/solution/train_current_model.py", "query": "draw_training_negatives\\(", "path": "train_current_model.py" }, { "type": "read", "cmd": "sed -n '1920,2075p' /app/solution/train_current_model.py", "name": "train_current_model.py", "path": "/app/solution/train_current_model.py" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "851- user_rows,\n852- known_positive_packed,\n853- liked_cpu,\n854- )\n855- if np.any(invalid):\n856- raise RuntimeError(\"full-history negative resampling failed\")\n857- return (\n858- torch.from_numpy(negatives).to(device),\n859- torch.from_numpy(correction).to(device),\n860- )\n861-\n862-\n863:def draw_training_negatives(\n864- model: base.SequentialTwoTower,\n865- sequence_vectors: torch.Tensor,\n866- flat_states: np.ndarray,\n867- flat_activity: np.ndarray,\n868- positive_items: torch.Tensor,\n869- known_history: torch.Tensor,\n870- liked_items: torch.Tensor,\n871- user_rows: np.ndarray,\n872- known_positive_packed: np.ndarray | None,\n873- prepared: SequenceData,\n874- config: Config,\n875- item_correction: torch.Tensor,\n--\n1441- ).to(device)\n1442- known_history = torch.from_numpy(\n1443- block[6].reshape(-1, config.bptt_steps)[flat_scored].astype(\n1444- np.int64\n1445- )\n1446- ).to(device)\n1447- liked_items = torch.from_numpy(\n1448- prepared.liked_states[flat_states].astype(np.int64)\n1449- ).to(device)\n1450- flat_user_rows = np.broadcast_to(\n1451- users[:, None], block[5].shape\n1452- ).reshape(-1)[flat_scored]\n1453: negatives, negative_weights = draw_training_negatives(\n1454- positive_items,\n1455- known_history,\n1456- liked_items,\n1457- flat_user_rows,\n1458- known_positive_packed,\n1459- prepared,\n1460- config,\n1461- item_correction,\n1462- generator,\n1463- device,\n1464- )\n1465- optimizer.zero_grad(set_to_none=True)\n--\n1597- ).to(device)\n1598- known_history = torch.from_numpy(\n1599- block[6].reshape(-1, config.bptt_steps)[flat_scored].astype(\n1600- np.int64\n1601- )\n1602- ).to(device)\n1603- liked_items = torch.from_numpy(\n1604- prepared.liked_states[flat_states].astype(np.int64)\n1605- ).to(device)\n1606- flat_user_rows = np.broadcast_to(\n1607- users[:, None], block[5].shape\n1608- ).reshape(-1)[flat_scored]\n1609: negatives, negative_weights = draw_training_negatives(\n1610- positive_items,\n1611- known_history,\n1612- liked_items,\n1613- flat_user_rows,\n1614- known_positive_packed,\n1615- prepared,\n1616- config,\n1617- item_correction,\n1618- generator,\n1619- device,\n1620- )\n1621- statistics, _ = score_target_microbatches(\n f\"validation_seconds={diagnostics['validation_seconds']:.1f}\"\n )\n release_accelerator_cache(device)\n if epochs_without_improvement >= config.early_stopping_patience:\n log(\n f\"early stopping after epoch={epoch}; \"\n f\"best_epoch={best_epoch} best_validation_loss={best_loss:.6f}\"\n )\n break\n\n if best_state is None:\n raise RuntimeError(\"training did not produce a valid checkpoint\")\n model.load_state_dict(best_state)\n model.to(device).eval()\n log(\n f\"restored best validation-BCE checkpoint: epoch={best_epoch} \"\n f\"loss={best_loss:.6f}\"\n )\n return model, history\n\n\n@torch.inference_mode()\ndef inference_queries(\n model: base.SequentialTwoTower,\n prepared: SequenceData,\n config: Config,\n device: torch.device,\n) -> torch.Tensor:\n sequence_rows = prepared.inference_user_rows\n lengths = np.where(\n sequence_rows >= 0, prepared.lengths[np.maximum(sequence_rows, 0)], 0\n )\n ordered = np.argsort(lengths, kind=\"stable\")\n result = torch.zeros(\n len(sequence_rows), config.embedding_dim, dtype=torch.float32\n )\n for group_start in range(0, len(ordered), config.user_batch_size):\n output_rows = ordered[group_start : group_start + config.user_batch_size]\n rows = sequence_rows[output_rows]\n group_lengths = lengths[output_rows]\n hidden = torch.zeros(\n 1, len(output_rows), config.embedding_dim, device=device\n )\n for start in range(0, int(group_lengths.max(initial=0)), config.bptt_steps):\n steps = min(\n config.bptt_steps,\n int(group_lengths.max(initial=0)) - start,\n )\n items = np.full(\n (len(output_rows), steps),\n len(prepared.item_ids),\n dtype=np.uint16,\n )\n features = np.zeros((len(output_rows), steps, 3), dtype=np.float16)\n counts = np.zeros(len(output_rows), dtype=np.int32)\n for local_row, sequence_row in enumerate(rows):\n if sequence_row < 0:\n continue\n count = min(steps, max(int(group_lengths[local_row]) - start, 0))\n if not count:\n continue\n offset = int(prepared.offsets[sequence_row])\n selected = slice(offset + start, offset + start + count)\n items[local_row, :count] = prepared.sequence_items[selected]\n features[local_row, :count] = prepared.event_features[selected]\n counts[local_row] = count\n outputs, _ = event_sequence(\n model,\n torch.from_numpy(items.astype(np.int64)).to(device),\n torch.from_numpy(features.astype(np.float32)).to(device),\n hidden,\n )\n next_rows: list[torch.Tensor] = []\n for local_row, count in enumerate(counts):\n next_rows.append(\n outputs[local_row, count - 1]\n if count\n else hidden[0, local_row]\n )\n hidden = torch.stack(next_rows).unsqueeze(0)\n\n liked_items = torch.from_numpy(\n prepared.inference_liked_items[output_rows].astype(np.int64)\n ).to(device)\n disliked_items = torch.from_numpy(\n prepared.inference_disliked_items[output_rows].astype(np.int64)\n ).to(device)\n queries = user_queries(\n model,\n hidden.squeeze(0),\n model._mean_context_channel(liked_items),\n model._mean_context_channel(disliked_items),\n torch.from_numpy(\n prepared.inference_user_features[output_rows].astype(np.float32)\n ).to(device),\n )\n result[torch.from_numpy(output_rows.astype(np.int64))] = queries.cpu()\n return result\n\n\n@torch.inference_mode()\ndef rank_candidates(\n model: base.SequentialTwoTower,\n prepared: SequenceData,\n eligibility: CandidateSet,\n config: Config,\n device: torch.device,\n ranking_batch_size: int,\n) -> tuple[pd.DataFrame, dict[str, float]]:\n model.eval()\n queries = inference_queries(model, prepared, config, device)\n item_parts: list[torch.Tensor] = []\n for start in range(0, len(prepared.item_ids), 2_048):\n rows = torch.arange(\n start,\n min(start + 2_048, len(prepared.item_ids)),\n dtype=torch.long,\n device=device,\n )\n item_parts.append(model.encode_items(rows).cpu())\n items = torch.cat(item_parts)\n rows: list[tuple[int, int, int]] = []\n for start in range(0, len(eligibility.uids), ranking_batch_size):\n end = min(start + ranking_batch_size, len(eligibility.uids))\n scores = queries[start:end] @ items.T\n eligible = np.unpackbits(\n eligibility.packed[start:end],\n axis=1,\n count=len(prepared.item_ids),\n bitorder=\"little\",\n ).astype(bool, copy=False)\n scores.masked_fill_(~torch.from_numpy(eligible), -torch.inf)\n selected = torch.topk(scores, k=base.TOP_N, dim=1).indices.numpy()\n for local_row, columns in enumerate(selected):\n uid = int(eligibility.uids[start + local_row])\n rows.extend(\n (uid, int(prepared.item_ids[column]), rank)\n for rank, column in enumerate(columns, start=1)\n )\n if end == len(eligibility.uids) or end % 1_024 == 0:\n log(f\"ranked users={end:,}/{len(eligibility.uids):,}\")\n query_norm = torch.linalg.vector_norm(queries, dim=1).numpy()\n item_norm = torch.linalg.vector_norm(items, dim=1).numpy()\n diagnostics = {\n \"query_norm_mean\": float(query_norm.mean()),\n \"query_norm_std\": float(query_norm.std()),\n \"item_norm_mean\": float(item_norm.mean()),\n \"item_norm_std\": float(item_norm.std()),\n }\n return pd.DataFrame(rows, columns=[\"uid\", \"item_id\", \"rank\"]), diagnostics\n\n\ndef write_ranking(frame: pd.DataFrame, output: Path) -> None:\n output.parent.mkdir(parents=True, exist_ok=True)\n if output.suffix == \".parquet\":\n frame.to_parquet(output, index=False, compression=\"zstd\")\n", "stderr": "", "aggregated_output": "851- user_rows,\n852- known_positive_packed,\n853- liked_cpu,\n854- )\n855- if np.any(invalid):\n856- raise RuntimeError(\"full-history negative resampling failed\")\n857- return (\n858- torch.from_numpy(negatives).to(device),\n859- torch.from_numpy(correction).to(device),\n860- )\n861-\n862-\n863:def draw_training_negatives(\n864- model: base.SequentialTwoTower,\n865- sequence_vectors: torch.Tensor,\n866- flat_states: np.ndarray,\n867- flat_activity: np.ndarray,\n868- positive_items: torch.Tensor,\n869- known_history: torch.Tensor,\n870- liked_items: torch.Tensor,\n871- user_rows: np.ndarray,\n872- known_positive_packed: np.ndarray | None,\n873- prepared: SequenceData,\n874- config: Config,\n875- item_correction: torch.Tensor,\n--\n1441- ).to(device)\n1442- known_history = torch.from_numpy(\n1443- block[6].reshape(-1, config.bptt_steps)[flat_scored].astype(\n1444- np.int64\n1445- )\n1446- ).to(device)\n1447- liked_items = torch.from_numpy(\n1448- prepared.liked_states[flat_states].astype(np.int64)\n1449- ).to(device)\n1450- flat_user_rows = np.broadcast_to(\n1451- users[:, None], block[5].shape\n1452- ).reshape(-1)[flat_scored]\n1453: negatives, negative_weights = draw_training_negatives(\n1454- positive_items,\n1455- known_history,\n1456- liked_items,\n1457- flat_user_rows,\n1458- known_positive_packed,\n1459- prepared,\n1460- config,\n1461- item_correction,\n1462- generator,\n1463- device,\n1464- )\n1465- optimizer.zero_grad(set_to_none=True)\n--\n1597- ).to(device)\n1598- known_history = torch.from_numpy(\n1599- block[6].reshape(-1, config.bptt_steps)[flat_scored].astype(\n1600- np.int64\n1601- )\n1602- ).to(device)\n1603- liked_items = torch.from_numpy(\n1604- prepared.liked_states[flat_states].astype(np.int64)\n1605- ).to(device)\n1606- flat_user_rows = np.broadcast_to(\n1607- users[:, None], block[5].shape\n1608- ).reshape(-1)[flat_scored]\n1609: negatives, negative_weights = draw_training_negatives(\n1610- positive_items,\n1611- known_history,\n1612- liked_items,\n1613- flat_user_rows,\n1614- known_positive_packed,\n1615- prepared,\n1616- config,\n1617- item_correction,\n1618- generator,\n1619- device,\n1620- )\n1621- statistics, _ = score_target_microbatches(\n f\"validation_seconds={diagnostics['validation_seconds']:.1f}\"\n )\n release_accelerator_cache(device)\n if epochs_without_improvement >= config.early_stopping_patience:\n log(\n f\"early stopping after epoch={epoch}; \"\n f\"best_epoch={best_epoch} best_validation_loss={best_loss:.6f}\"\n )\n break\n\n if best_state is None:\n raise RuntimeError(\"training did not produce a valid checkpoint\")\n model.load_state_dict(best_state)\n model.to(device).eval()\n log(\n f\"restored best validation-BCE checkpoint: epoch={best_epoch} \"\n f\"loss={best_loss:.6f}\"\n )\n return model, history\n\n\n@torch.inference_mode()\ndef inference_queries(\n model: base.SequentialTwoTower,\n prepared: SequenceData,\n config: Config,\n device: torch.device,\n) -> torch.Tensor:\n sequence_rows = prepared.inference_user_rows\n lengths = np.where(\n sequence_rows >= 0, prepared.lengths[np.maximum(sequence_rows, 0)], 0\n )\n ordered = np.argsort(lengths, kind=\"stable\")\n result = torch.zeros(\n len(sequence_rows), config.embedding_dim, dtype=torch.float32\n )\n for group_start in range(0, len(ordered), config.user_batch_size):\n output_rows = ordered[group_start : group_start + config.user_batch_size]\n rows = sequence_rows[output_rows]\n group_lengths = lengths[output_rows]\n hidden = torch.zeros(\n 1, len(output_rows), config.embedding_dim, device=device\n )\n for start in range(0, int(group_lengths.max(initial=0)), config.bptt_steps):\n steps = min(\n config.bptt_steps,\n int(group_lengths.max(initial=0)) - start,\n )\n items = np.full(\n (len(output_rows), steps),\n len(prepared.item_ids),\n dtype=np.uint16,\n )\n features = np.zeros((len(output_rows), steps, 3), dtype=np.float16)\n counts = np.zeros(len(output_rows), dtype=np.int32)\n for local_row, sequence_row in enumerate(rows):\n if sequence_row < 0:\n continue\n count = min(steps, max(int(group_lengths[local_row]) - start, 0))\n if not count:\n continue\n offset = int(prepared.offsets[sequence_row])\n selected = slice(offset + start, offset + start + count)\n items[local_row, :count] = prepared.sequence_items[selected]\n features[local_row, :count] = prepared.event_features[selected]\n counts[local_row] = count\n outputs, _ = event_sequence(\n model,\n torch.from_numpy(items.astype(np.int64)).to(device),\n torch.from_numpy(features.astype(np.float32)).to(device),\n hidden,\n )\n next_rows: list[torch.Tensor] = []\n for local_row, count in enumerate(counts):\n next_rows.append(\n outputs[local_row, count - 1]\n if count\n else hidden[0, local_row]\n )\n hidden = torch.stack(next_rows).unsqueeze(0)\n\n liked_items = torch.from_numpy(\n prepared.inference_liked_items[output_rows].astype(np.int64)\n ).to(device)\n disliked_items = torch.from_numpy(\n prepared.inference_disliked_items[output_rows].astype(np.int64)\n ).to(device)\n queries = user_queries(\n model,\n hidden.squeeze(0),\n model._mean_context_channel(liked_items),\n model._mean_context_channel(disliked_items),\n torch.from_numpy(\n prepared.inference_user_features[output_rows].astype(np.float32)\n ).to(device),\n )\n result[torch.from_numpy(output_rows.astype(np.int64))] = queries.cpu()\n return result\n\n\n@torch.inference_mode()\ndef rank_candidates(\n model: base.SequentialTwoTower,\n prepared: SequenceData,\n eligibility: CandidateSet,\n config: Config,\n device: torch.device,\n ranking_batch_size: int,\n) -> tuple[pd.DataFrame, dict[str, float]]:\n model.eval()\n queries = inference_queries(model, prepared, config, device)\n item_parts: list[torch.Tensor] = []\n for start in range(0, len(prepared.item_ids), 2_048):\n rows = torch.arange(\n start,\n min(start + 2_048, len(prepared.item_ids)),\n dtype=torch.long,\n device=device,\n )\n item_parts.append(model.encode_items(rows).cpu())\n items = torch.cat(item_parts)\n rows: list[tuple[int, int, int]] = []\n for start in range(0, len(eligibility.uids), ranking_batch_size):\n end = min(start + ranking_batch_size, len(eligibility.uids))\n scores = queries[start:end] @ items.T\n eligible = np.unpackbits(\n eligibility.packed[start:end],\n axis=1,\n count=len(prepared.item_ids),\n bitorder=\"little\",\n ).astype(bool, copy=False)\n scores.masked_fill_(~torch.from_numpy(eligible), -torch.inf)\n selected = torch.topk(scores, k=base.TOP_N, dim=1).indices.numpy()\n for local_row, columns in enumerate(selected):\n uid = int(eligibility.uids[start + local_row])\n rows.extend(\n (uid, int(prepared.item_ids[column]), rank)\n for rank, column in enumerate(columns, start=1)\n )\n if end == len(eligibility.uids) or end % 1_024 == 0:\n log(f\"ranked users={end:,}/{len(eligibility.uids):,}\")\n query_norm = torch.linalg.vector_norm(queries, dim=1).numpy()\n item_norm = torch.linalg.vector_norm(items, dim=1).numpy()\n diagnostics = {\n \"query_norm_mean\": float(query_norm.mean()),\n \"query_norm_std\": float(query_norm.std()),\n \"item_norm_mean\": float(item_norm.mean()),\n \"item_norm_std\": float(item_norm.std()),\n }\n return pd.DataFrame(rows, columns=[\"uid\", \"item_id\", \"rank\"]), diagnostics\n\n\ndef write_ranking(frame: pd.DataFrame, output: Path) -> None:\n output.parent.mkdir(parents=True, exist_ok=True)\n if output.suffix == \".parquet\":\n frame.to_parquet(output, index=False, compression=\"zstd\")\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 3820 }, "formatted_output": "851- user_rows,\n852- known_positive_packed,\n853- liked_cpu,\n854- )\n855- if np.any(invalid):\n856- raise RuntimeError(\"full-history negative resampling failed\")\n857- return (\n858- torch.from_numpy(negatives).to(device),\n859- torch.from_numpy(correction).to(device),\n860- )\n861-\n862-\n863:def draw_training_negatives(\n864- model: base.SequentialTwoTower,\n865- sequence_vectors: torch.Tensor,\n866- flat_states: np.ndarray,\n867- flat_activity: np.ndarray,\n868- positive_items: torch.Tensor,\n869- known_history: torch.Tensor,\n870- liked_items: torch.Tensor,\n871- user_rows: np.ndarray,\n872- known_positive_packed: np.ndarray | None,\n873- prepared: SequenceData,\n874- config: Config,\n875- item_correction: torch.Tensor,\n--\n1441- ).to(device)\n1442- known_history = torch.from_numpy(\n1443- block[6].reshape(-1, config.bptt_steps)[flat_scored].astype(\n1444- np.int64\n1445- )\n1446- ).to(device)\n1447- liked_items = torch.from_numpy(\n1448- prepared.liked_states[flat_states].astype(np.int64)\n1449- ).to(device)\n1450- flat_user_rows = np.broadcast_to(\n1451- users[:, None], block[5].shape\n1452- ).reshape(-1)[flat_scored]\n1453: negatives, negative_weights = draw_training_negatives(\n1454- positive_items,\n1455- known_history,\n1456- liked_items,\n1457- flat_user_rows,\n1458- known_positive_packed,\n1459- prepared,\n1460- config,\n1461- item_correction,\n1462- generator,\n1463- device,\n1464- )\n1465- optimizer.zero_grad(set_to_none=True)\n--\n1597- ).to(device)\n1598- known_history = torch.from_numpy(\n1599- block[6].reshape(-1, config.bptt_steps)[flat_scored].astype(\n1600- np.int64\n1601- )\n1602- ).to(device)\n1603- liked_items = torch.from_numpy(\n1604- prepared.liked_states[flat_states].astype(np.int64)\n1605- ).to(device)\n1606- flat_user_rows = np.broadcast_to(\n1607- users[:, None], block[5].shape\n1608- ).reshape(-1)[flat_scored]\n1609: negatives, negative_weights = draw_training_negatives(\n1610- positive_items,\n1611- known_history,\n1612- liked_items,\n1613- flat_user_rows,\n1614- known_positive_packed,\n1615- prepared,\n1616- config,\n1617- item_correction,\n1618- generator,\n1619- device,\n1620- )\n1621- statistics, _ = score_target_microbatches(\n f\"validation_seconds={diagnostics['validation_seconds']:.1f}\"\n )\n release_accelerator_cache(device)\n if epochs_without_improvement >= config.early_stopping_patience:\n log(\n f\"early stopping after epoch={epoch}; \"\n f\"best_epoch={best_epoch} best_validation_loss={best_loss:.6f}\"\n )\n break\n\n if best_state is None:\n raise RuntimeError(\"training did not produce a valid checkpoint\")\n model.load_state_dict(best_state)\n model.to(device).eval()\n log(\n f\"restored best validation-BCE checkpoint: epoch={best_epoch} \"\n f\"loss={best_loss:.6f}\"\n )\n return model, history\n\n\n@torch.inference_mode()\ndef inference_queries(\n model: base.SequentialTwoTower,\n prepared: SequenceData,\n config: Config,\n device: torch.device,\n) -> torch.Tensor:\n sequence_rows = prepared.inference_user_rows\n lengths = np.where(\n sequence_rows >= 0, prepared.lengths[np.maximum(sequence_rows, 0)], 0\n )\n ordered = np.argsort(lengths, kind=\"stable\")\n result = torch.zeros(\n len(sequence_rows), config.embedding_dim, dtype=torch.float32\n )\n for group_start in range(0, len(ordered), config.user_batch_size):\n output_rows = ordered[group_start : group_start + config.user_batch_size]\n rows = sequence_rows[output_rows]\n group_lengths = lengths[output_rows]\n hidden = torch.zeros(\n 1, len(output_rows), config.embedding_dim, device=device\n )\n for start in range(0, int(group_lengths.max(initial=0)), config.bptt_steps):\n steps = min(\n config.bptt_steps,\n int(group_lengths.max(initial=0)) - start,\n )\n items = np.full(\n (len(output_rows), steps),\n len(prepared.item_ids),\n dtype=np.uint16,\n )\n features = np.zeros((len(output_rows), steps, 3), dtype=np.float16)\n counts = np.zeros(len(output_rows), dtype=np.int32)\n for local_row, sequence_row in enumerate(rows):\n if sequence_row < 0:\n continue\n count = min(steps, max(int(group_lengths[local_row]) - start, 0))\n if not count:\n continue\n offset = int(prepared.offsets[sequence_row])\n selected = slice(offset + start, offset + start + count)\n items[local_row, :count] = prepared.sequence_items[selected]\n features[local_row, :count] = prepared.event_features[selected]\n counts[local_row] = count\n outputs, _ = event_sequence(\n model,\n torch.from_numpy(items.astype(np.int64)).to(device),\n torch.from_numpy(features.astype(np.float32)).to(device),\n hidden,\n )\n next_rows: list[torch.Tensor] = []\n for local_row, count in enumerate(counts):\n next_rows.append(\n outputs[local_row, count - 1]\n if count\n else hidden[0, local_row]\n )\n hidden = torch.stack(next_rows).unsqueeze(0)\n\n liked_items = torch.from_numpy(\n prepared.inference_liked_items[output_rows].astype(np.int64)\n ).to(device)\n disliked_items = torch.from_numpy(\n prepared.inference_disliked_items[output_rows].astype(np.int64)\n ).to(device)\n queries = user_queries(\n model,\n hidden.squeeze(0),\n model._mean_context_channel(liked_items),\n model._mean_context_channel(disliked_items),\n torch.from_numpy(\n prepared.inference_user_features[output_rows].astype(np.float32)\n ).to(device),\n )\n result[torch.from_numpy(output_rows.astype(np.int64))] = queries.cpu()\n return result\n\n\n@torch.inference_mode()\ndef rank_candidates(\n model: base.SequentialTwoTower,\n prepared: SequenceData,\n eligibility: CandidateSet,\n config: Config,\n device: torch.device,\n ranking_batch_size: int,\n) -> tuple[pd.DataFrame, dict[str, float]]:\n model.eval()\n queries = inference_queries(model, prepared, config, device)\n item_parts: list[torch.Tensor] = []\n for start in range(0, len(prepared.item_ids), 2_048):\n rows = torch.arange(\n start,\n min(start + 2_048, len(prepared.item_ids)),\n dtype=torch.long,\n device=device,\n )\n item_parts.append(model.encode_items(rows).cpu())\n items = torch.cat(item_parts)\n rows: list[tuple[int, int, int]] = []\n for start in range(0, len(eligibility.uids), ranking_batch_size):\n end = min(start + ranking_batch_size, len(eligibility.uids))\n scores = queries[start:end] @ items.T\n eligible = np.unpackbits(\n eligibility.packed[start:end],\n axis=1,\n count=len(prepared.item_ids),\n bitorder=\"little\",\n ).astype(bool, copy=False)\n scores.masked_fill_(~torch.from_numpy(eligible), -torch.inf)\n selected = torch.topk(scores, k=base.TOP_N, dim=1).indices.numpy()\n for local_row, columns in enumerate(selected):\n uid = int(eligibility.uids[start + local_row])\n rows.extend(\n (uid, int(prepared.item_ids[column]), rank)\n for rank, column in enumerate(columns, start=1)\n )\n if end == len(eligibility.uids) or end % 1_024 == 0:\n log(f\"ranked users={end:,}/{len(eligibility.uids):,}\")\n query_norm = torch.linalg.vector_norm(queries, dim=1).numpy()\n item_norm = torch.linalg.vector_norm(items, dim=1).numpy()\n diagnostics = {\n \"query_norm_mean\": float(query_norm.mean()),\n \"query_norm_std\": float(query_norm.std()),\n \"item_norm_mean\": float(item_norm.mean()),\n \"item_norm_std\": float(item_norm.std()),\n }\n return pd.DataFrame(rows, columns=[\"uid\", \"item_id\", \"rank\"]), diagnostics\n\n\ndef write_ranking(frame: pd.DataFrame, output: Path) -> None:\n output.parent.mkdir(parents=True, exist_ok=True)\n if output.suffix == \".parquet\":\n frame.to_parquet(output, index=False, compression=\"zstd\")\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "session_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "root_turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "response_id": "resp_08539f19231b9e9f016ab5ab9ce18887d2976aa4e42d13c0f3", "usage": { "input_tokens": 66260, "cached_input_tokens": 64384, "cache_write_input_tokens": 0, "output_tokens": 111, "reasoning_output_tokens": 22, "total_tokens": 66371 }, "turn_token_usage": { "input_tokens": 1841756, "cached_input_tokens": 1780224, "cache_write_input_tokens": 0, "output_tokens": 13027, "reasoning_output_tokens": 4953, "total_tokens": 1854783 }, "thread_token_usage": { "input_tokens": 1841756, "cached_input_tokens": 1780224, "cache_write_input_tokens": 0, "output_tokens": 13027, "reasoning_output_tokens": 4953, "total_tokens": 1854783 } } ## Step 51 · agent · 2026-09-24T23:00:58.505Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T23:00:59.549Z { "code": "const r = await tools.exec_command({\"cmd\":\"sed -n '2060,2245p' /app/solution/train_current_model.py\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":20000}); text(r.output);" } ### Tool output · 2026-09-24T23:00:59.654Z Script completed Wall time 0.0 seconds Output: log(f"ranked users={end:,}/{len(eligibility.uids):,}") query_norm = torch.linalg.vector_norm(queries, dim=1).numpy() item_norm = torch.linalg.vector_norm(items, dim=1).numpy() diagnostics = { "query_norm_mean": float(query_norm.mean()), "query_norm_std": float(query_norm.std()), "item_norm_mean": float(item_norm.mean()), "item_norm_std": float(item_norm.std()), } return pd.DataFrame(rows, columns=["uid", "item_id", "rank"]), diagnostics def write_ranking(frame: pd.DataFrame, output: Path) -> None: output.parent.mkdir(parents=True, exist_ok=True) if output.suffix == ".parquet": frame.to_parquet(output, index=False, compression="zstd") elif output.suffix == ".csv": frame.to_csv(output, index=False) else: raise ValueError("--output must end in .parquet or .csv") def main() -> int: parser = argparse.ArgumentParser(description=__doc__) parser.add_argument( "--stage", choices=("validation", "submission"), default="validation" ) parser.add_argument("--output", type=Path, required=True) parser.add_argument("--public-dir", type=Path, required=True) parser.add_argument("--prepared-cache", type=Path) parser.add_argument("--device", choices=("cpu", "mps"), default="cpu") parser.add_argument("--threads", type=int, default=8) parser.add_argument("--bptt-steps", type=int, default=Config.bptt_steps) parser.add_argument( "--user-batch-size", type=int, default=Config.user_batch_size ) parser.add_argument("--epochs", type=int, default=Config.epochs) parser.add_argument("--learning-rate", type=float, default=Config.learning_rate) parser.add_argument( "--loss-microbatch-size", type=int, default=Config.loss_microbatch_size, ) parser.add_argument( "--negative-filter-scope", choices=("full-training-history", "last-100"), default=Config.negative_filter_scope, ) parser.add_argument( "--target-weighting", choices=("discovery-graded", "organic-strong", "uniform"), default=Config.target_weighting, ) parser.add_argument( "--ordinary-target-weight", type=float, default=Config.ordinary_target_weight, ) parser.add_argument( "--organic-strong-target-weight", type=float, default=Config.organic_strong_target_weight, ) parser.add_argument( "--organic-like-target-weight", type=float, default=Config.organic_like_target_weight, ) parser.add_argument( "--discovery-horizon-days", type=int, default=Config.discovery_horizon_days, ) parser.add_argument( "--validation-fraction", type=float, default=Config.validation_fraction, ) parser.add_argument( "--max-train-targets-per-user", type=int, default=Config.max_train_targets_per_user, help="retain this many recent pre-validation targets per user; 0 is uncapped", ) parser.add_argument( "--max-validation-targets-per-user", type=int, default=Config.max_validation_targets_per_user, help="retain this many final validation targets per user; 0 is uncapped", ) parser.add_argument( "--early-stopping-patience", type=int, default=Config.early_stopping_patience, ) parser.add_argument( "--early-stopping-min-delta", type=float, default=Config.early_stopping_min_delta, ) parser.add_argument("--checkpoint-dir", type=Path) parser.add_argument("--resume-checkpoint", type=Path) parser.add_argument("--seed", type=int, default=Config.seed) parser.add_argument("--ranking-batch-size", type=int, default=64) parser.add_argument("--prepare-only", action="store_true") args = parser.parse_args() if args.threads < 1 or args.bptt_steps < 2 or args.user_batch_size < 1: parser.error("threads, BPTT steps, and user batch size must be positive") if ( args.epochs < 1 or args.learning_rate <= 0 or args.loss_microbatch_size < 1 or args.early_stopping_patience < 1 or args.early_stopping_min_delta < 0 or args.ordinary_target_weight <= 0 or args.organic_strong_target_weight <= 0 or args.organic_like_target_weight <= 0 or args.discovery_horizon_days < 1 or args.max_train_targets_per_user < 0 or args.max_validation_targets_per_user < 0 ): parser.error("training counts, learning rate, and patience are invalid") if not 0.0 < args.validation_fraction < 1.0: parser.error("validation fraction must be strictly between zero and one") public = args.public_dir.resolve() if not (public / "data" / "candidate_catalog.parquet").is_file(): parser.error(f"public task data not found under {public}") config = Config( bptt_steps=args.bptt_steps, user_batch_size=args.user_batch_size, epochs=args.epochs, learning_rate=args.learning_rate, loss_microbatch_size=args.loss_microbatch_size, negative_filter_scope=args.negative_filter_scope, target_weighting=args.target_weighting, ordinary_target_weight=args.ordinary_target_weight, organic_strong_target_weight=args.organic_strong_target_weight, organic_like_target_weight=args.organic_like_target_weight, discovery_horizon_days=args.discovery_horizon_days, validation_fraction=args.validation_fraction, max_train_targets_per_user=args.max_train_targets_per_user, max_validation_targets_per_user=args.max_validation_targets_per_user, early_stopping_patience=args.early_stopping_patience, early_stopping_min_delta=args.early_stopping_min_delta, seed=args.seed, ) torch.set_num_threads(args.threads) torch.set_num_interop_threads(1) seed_everything(config.seed) device = torch.device(args.device) if device.type == "mps" and not torch.backends.mps.is_available(): parser.error("MPS was requested but is unavailable") started = time.monotonic() prepared, eligibility = load_or_prepare( public, args.stage, config, args.prepared_cache.resolve() if args.prepared_cache else None, ) target_partition = chronological_target_partition( prepared, config.validation_fraction, config.max_train_targets_per_user, config.max_validation_targets_per_user, ) if args.prepare_only: print( json.dumps( { "stage": args.stage, "sequence_users": len(prepared.user_ids), "sequence_tokens": len(prepared.sequence_items), "next_item_targets": prepared.target_count, "selected_training_targets": int( target_partition.training_counts.sum() ), "selected_validation_targets": int( target_partition.validation_counts.sum() ), "discarded_old_targets": int( target_partition.window_starts.sum() ), "target_users": len(eligibility.uids), "runtime_seconds": round(time.monotonic() - started, 3), ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-4544d0c3-c397-45ff-88e4-7b820c0daae2", "process_id": "80151", "command": [ "/bin/sh", "-lc", "sed -n '2060,2245p' /app/solution/train_current_model.py" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "read", "cmd": "sed -n '2060,2245p' /app/solution/train_current_model.py", "name": "train_current_model.py", "path": "/app/solution/train_current_model.py" } ], "source": "unified_exec_startup", "status": "completed", "stdout": " log(f\"ranked users={end:,}/{len(eligibility.uids):,}\")\n query_norm = torch.linalg.vector_norm(queries, dim=1).numpy()\n item_norm = torch.linalg.vector_norm(items, dim=1).numpy()\n diagnostics = {\n \"query_norm_mean\": float(query_norm.mean()),\n \"query_norm_std\": float(query_norm.std()),\n \"item_norm_mean\": float(item_norm.mean()),\n \"item_norm_std\": float(item_norm.std()),\n }\n return pd.DataFrame(rows, columns=[\"uid\", \"item_id\", \"rank\"]), diagnostics\n\n\ndef write_ranking(frame: pd.DataFrame, output: Path) -> None:\n output.parent.mkdir(parents=True, exist_ok=True)\n if output.suffix == \".parquet\":\n frame.to_parquet(output, index=False, compression=\"zstd\")\n elif output.suffix == \".csv\":\n frame.to_csv(output, index=False)\n else:\n raise ValueError(\"--output must end in .parquet or .csv\")\n\n\ndef main() -> int:\n parser = argparse.ArgumentParser(description=__doc__)\n parser.add_argument(\n \"--stage\", choices=(\"validation\", \"submission\"), default=\"validation\"\n )\n parser.add_argument(\"--output\", type=Path, required=True)\n parser.add_argument(\"--public-dir\", type=Path, required=True)\n parser.add_argument(\"--prepared-cache\", type=Path)\n parser.add_argument(\"--device\", choices=(\"cpu\", \"mps\"), default=\"cpu\")\n parser.add_argument(\"--threads\", type=int, default=8)\n parser.add_argument(\"--bptt-steps\", type=int, default=Config.bptt_steps)\n parser.add_argument(\n \"--user-batch-size\", type=int, default=Config.user_batch_size\n )\n parser.add_argument(\"--epochs\", type=int, default=Config.epochs)\n parser.add_argument(\"--learning-rate\", type=float, default=Config.learning_rate)\n parser.add_argument(\n \"--loss-microbatch-size\",\n type=int,\n default=Config.loss_microbatch_size,\n )\n parser.add_argument(\n \"--negative-filter-scope\",\n choices=(\"full-training-history\", \"last-100\"),\n default=Config.negative_filter_scope,\n )\n parser.add_argument(\n \"--target-weighting\",\n choices=(\"discovery-graded\", \"organic-strong\", \"uniform\"),\n default=Config.target_weighting,\n )\n parser.add_argument(\n \"--ordinary-target-weight\",\n type=float,\n default=Config.ordinary_target_weight,\n )\n parser.add_argument(\n \"--organic-strong-target-weight\",\n type=float,\n default=Config.organic_strong_target_weight,\n )\n parser.add_argument(\n \"--organic-like-target-weight\",\n type=float,\n default=Config.organic_like_target_weight,\n )\n parser.add_argument(\n \"--discovery-horizon-days\",\n type=int,\n default=Config.discovery_horizon_days,\n )\n parser.add_argument(\n \"--validation-fraction\",\n type=float,\n default=Config.validation_fraction,\n )\n parser.add_argument(\n \"--max-train-targets-per-user\",\n type=int,\n default=Config.max_train_targets_per_user,\n help=\"retain this many recent pre-validation targets per user; 0 is uncapped\",\n )\n parser.add_argument(\n \"--max-validation-targets-per-user\",\n type=int,\n default=Config.max_validation_targets_per_user,\n help=\"retain this many final validation targets per user; 0 is uncapped\",\n )\n parser.add_argument(\n \"--early-stopping-patience\",\n type=int,\n default=Config.early_stopping_patience,\n )\n parser.add_argument(\n \"--early-stopping-min-delta\",\n type=float,\n default=Config.early_stopping_min_delta,\n )\n parser.add_argument(\"--checkpoint-dir\", type=Path)\n parser.add_argument(\"--resume-checkpoint\", type=Path)\n parser.add_argument(\"--seed\", type=int, default=Config.seed)\n parser.add_argument(\"--ranking-batch-size\", type=int, default=64)\n parser.add_argument(\"--prepare-only\", action=\"store_true\")\n args = parser.parse_args()\n\n if args.threads < 1 or args.bptt_steps < 2 or args.user_batch_size < 1:\n parser.error(\"threads, BPTT steps, and user batch size must be positive\")\n if (\n args.epochs < 1\n or args.learning_rate <= 0\n or args.loss_microbatch_size < 1\n or args.early_stopping_patience < 1\n or args.early_stopping_min_delta < 0\n or args.ordinary_target_weight <= 0\n or args.organic_strong_target_weight <= 0\n or args.organic_like_target_weight <= 0\n or args.discovery_horizon_days < 1\n or args.max_train_targets_per_user < 0\n or args.max_validation_targets_per_user < 0\n ):\n parser.error(\"training counts, learning rate, and patience are invalid\")\n if not 0.0 < args.validation_fraction < 1.0:\n parser.error(\"validation fraction must be strictly between zero and one\")\n public = args.public_dir.resolve()\n if not (public / \"data\" / \"candidate_catalog.parquet\").is_file():\n parser.error(f\"public task data not found under {public}\")\n config = Config(\n bptt_steps=args.bptt_steps,\n user_batch_size=args.user_batch_size,\n epochs=args.epochs,\n learning_rate=args.learning_rate,\n loss_microbatch_size=args.loss_microbatch_size,\n negative_filter_scope=args.negative_filter_scope,\n target_weighting=args.target_weighting,\n ordinary_target_weight=args.ordinary_target_weight,\n organic_strong_target_weight=args.organic_strong_target_weight,\n organic_like_target_weight=args.organic_like_target_weight,\n discovery_horizon_days=args.discovery_horizon_days,\n validation_fraction=args.validation_fraction,\n max_train_targets_per_user=args.max_train_targets_per_user,\n max_validation_targets_per_user=args.max_validation_targets_per_user,\n early_stopping_patience=args.early_stopping_patience,\n early_stopping_min_delta=args.early_stopping_min_delta,\n seed=args.seed,\n )\n torch.set_num_threads(args.threads)\n torch.set_num_interop_threads(1)\n seed_everything(config.seed)\n device = torch.device(args.device)\n if device.type == \"mps\" and not torch.backends.mps.is_available():\n parser.error(\"MPS was requested but is unavailable\")\n\n started = time.monotonic()\n prepared, eligibility = load_or_prepare(\n public,\n args.stage,\n config,\n args.prepared_cache.resolve() if args.prepared_cache else None,\n )\n target_partition = chronological_target_partition(\n prepared,\n config.validation_fraction,\n config.max_train_targets_per_user,\n config.max_validation_targets_per_user,\n )\n if args.prepare_only:\n print(\n json.dumps(\n {\n \"stage\": args.stage,\n \"sequence_users\": len(prepared.user_ids),\n \"sequence_tokens\": len(prepared.sequence_items),\n \"next_item_targets\": prepared.target_count,\n \"selected_training_targets\": int(\n target_partition.training_counts.sum()\n ),\n \"selected_validation_targets\": int(\n target_partition.validation_counts.sum()\n ),\n \"discarded_old_targets\": int(\n target_partition.window_starts.sum()\n ),\n \"target_users\": len(eligibility.uids),\n \"runtime_seconds\": round(time.monotonic() - started, 3),\n", "stderr": "", "aggregated_output": " log(f\"ranked users={end:,}/{len(eligibility.uids):,}\")\n query_norm = torch.linalg.vector_norm(queries, dim=1).numpy()\n item_norm = torch.linalg.vector_norm(items, dim=1).numpy()\n diagnostics = {\n \"query_norm_mean\": float(query_norm.mean()),\n \"query_norm_std\": float(query_norm.std()),\n \"item_norm_mean\": float(item_norm.mean()),\n \"item_norm_std\": float(item_norm.std()),\n }\n return pd.DataFrame(rows, columns=[\"uid\", \"item_id\", \"rank\"]), diagnostics\n\n\ndef write_ranking(frame: pd.DataFrame, output: Path) -> None:\n output.parent.mkdir(parents=True, exist_ok=True)\n if output.suffix == \".parquet\":\n frame.to_parquet(output, index=False, compression=\"zstd\")\n elif output.suffix == \".csv\":\n frame.to_csv(output, index=False)\n else:\n raise ValueError(\"--output must end in .parquet or .csv\")\n\n\ndef main() -> int:\n parser = argparse.ArgumentParser(description=__doc__)\n parser.add_argument(\n \"--stage\", choices=(\"validation\", \"submission\"), default=\"validation\"\n )\n parser.add_argument(\"--output\", type=Path, required=True)\n parser.add_argument(\"--public-dir\", type=Path, required=True)\n parser.add_argument(\"--prepared-cache\", type=Path)\n parser.add_argument(\"--device\", choices=(\"cpu\", \"mps\"), default=\"cpu\")\n parser.add_argument(\"--threads\", type=int, default=8)\n parser.add_argument(\"--bptt-steps\", type=int, default=Config.bptt_steps)\n parser.add_argument(\n \"--user-batch-size\", type=int, default=Config.user_batch_size\n )\n parser.add_argument(\"--epochs\", type=int, default=Config.epochs)\n parser.add_argument(\"--learning-rate\", type=float, default=Config.learning_rate)\n parser.add_argument(\n \"--loss-microbatch-size\",\n type=int,\n default=Config.loss_microbatch_size,\n )\n parser.add_argument(\n \"--negative-filter-scope\",\n choices=(\"full-training-history\", \"last-100\"),\n default=Config.negative_filter_scope,\n )\n parser.add_argument(\n \"--target-weighting\",\n choices=(\"discovery-graded\", \"organic-strong\", \"uniform\"),\n default=Config.target_weighting,\n )\n parser.add_argument(\n \"--ordinary-target-weight\",\n type=float,\n default=Config.ordinary_target_weight,\n )\n parser.add_argument(\n \"--organic-strong-target-weight\",\n type=float,\n default=Config.organic_strong_target_weight,\n )\n parser.add_argument(\n \"--organic-like-target-weight\",\n type=float,\n default=Config.organic_like_target_weight,\n )\n parser.add_argument(\n \"--discovery-horizon-days\",\n type=int,\n default=Config.discovery_horizon_days,\n )\n parser.add_argument(\n \"--validation-fraction\",\n type=float,\n default=Config.validation_fraction,\n )\n parser.add_argument(\n \"--max-train-targets-per-user\",\n type=int,\n default=Config.max_train_targets_per_user,\n help=\"retain this many recent pre-validation targets per user; 0 is uncapped\",\n )\n parser.add_argument(\n \"--max-validation-targets-per-user\",\n type=int,\n default=Config.max_validation_targets_per_user,\n help=\"retain this many final validation targets per user; 0 is uncapped\",\n )\n parser.add_argument(\n \"--early-stopping-patience\",\n type=int,\n default=Config.early_stopping_patience,\n )\n parser.add_argument(\n \"--early-stopping-min-delta\",\n type=float,\n default=Config.early_stopping_min_delta,\n )\n parser.add_argument(\"--checkpoint-dir\", type=Path)\n parser.add_argument(\"--resume-checkpoint\", type=Path)\n parser.add_argument(\"--seed\", type=int, default=Config.seed)\n parser.add_argument(\"--ranking-batch-size\", type=int, default=64)\n parser.add_argument(\"--prepare-only\", action=\"store_true\")\n args = parser.parse_args()\n\n if args.threads < 1 or args.bptt_steps < 2 or args.user_batch_size < 1:\n parser.error(\"threads, BPTT steps, and user batch size must be positive\")\n if (\n args.epochs < 1\n or args.learning_rate <= 0\n or args.loss_microbatch_size < 1\n or args.early_stopping_patience < 1\n or args.early_stopping_min_delta < 0\n or args.ordinary_target_weight <= 0\n or args.organic_strong_target_weight <= 0\n or args.organic_like_target_weight <= 0\n or args.discovery_horizon_days < 1\n or args.max_train_targets_per_user < 0\n or args.max_validation_targets_per_user < 0\n ):\n parser.error(\"training counts, learning rate, and patience are invalid\")\n if not 0.0 < args.validation_fraction < 1.0:\n parser.error(\"validation fraction must be strictly between zero and one\")\n public = args.public_dir.resolve()\n if not (public / \"data\" / \"candidate_catalog.parquet\").is_file():\n parser.error(f\"public task data not found under {public}\")\n config = Config(\n bptt_steps=args.bptt_steps,\n user_batch_size=args.user_batch_size,\n epochs=args.epochs,\n learning_rate=args.learning_rate,\n loss_microbatch_size=args.loss_microbatch_size,\n negative_filter_scope=args.negative_filter_scope,\n target_weighting=args.target_weighting,\n ordinary_target_weight=args.ordinary_target_weight,\n organic_strong_target_weight=args.organic_strong_target_weight,\n organic_like_target_weight=args.organic_like_target_weight,\n discovery_horizon_days=args.discovery_horizon_days,\n validation_fraction=args.validation_fraction,\n max_train_targets_per_user=args.max_train_targets_per_user,\n max_validation_targets_per_user=args.max_validation_targets_per_user,\n early_stopping_patience=args.early_stopping_patience,\n early_stopping_min_delta=args.early_stopping_min_delta,\n seed=args.seed,\n )\n torch.set_num_threads(args.threads)\n torch.set_num_interop_threads(1)\n seed_everything(config.seed)\n device = torch.device(args.device)\n if device.type == \"mps\" and not torch.backends.mps.is_available():\n parser.error(\"MPS was requested but is unavailable\")\n\n started = time.monotonic()\n prepared, eligibility = load_or_prepare(\n public,\n args.stage,\n config,\n args.prepared_cache.resolve() if args.prepared_cache else None,\n )\n target_partition = chronological_target_partition(\n prepared,\n config.validation_fraction,\n config.max_train_targets_per_user,\n config.max_validation_targets_per_user,\n )\n if args.prepare_only:\n print(\n json.dumps(\n {\n \"stage\": args.stage,\n \"sequence_users\": len(prepared.user_ids),\n \"sequence_tokens\": len(prepared.sequence_items),\n \"next_item_targets\": prepared.target_count,\n \"selected_training_targets\": int(\n target_partition.training_counts.sum()\n ),\n \"selected_validation_targets\": int(\n target_partition.validation_counts.sum()\n ),\n \"discarded_old_targets\": int(\n target_partition.window_starts.sum()\n ),\n \"target_users\": len(eligibility.uids),\n \"runtime_seconds\": round(time.monotonic() - started, 3),\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4020 }, "formatted_output": " log(f\"ranked users={end:,}/{len(eligibility.uids):,}\")\n query_norm = torch.linalg.vector_norm(queries, dim=1).numpy()\n item_norm = torch.linalg.vector_norm(items, dim=1).numpy()\n diagnostics = {\n \"query_norm_mean\": float(query_norm.mean()),\n \"query_norm_std\": float(query_norm.std()),\n \"item_norm_mean\": float(item_norm.mean()),\n \"item_norm_std\": float(item_norm.std()),\n }\n return pd.DataFrame(rows, columns=[\"uid\", \"item_id\", \"rank\"]), diagnostics\n\n\ndef write_ranking(frame: pd.DataFrame, output: Path) -> None:\n output.parent.mkdir(parents=True, exist_ok=True)\n if output.suffix == \".parquet\":\n frame.to_parquet(output, index=False, compression=\"zstd\")\n elif output.suffix == \".csv\":\n frame.to_csv(output, index=False)\n else:\n raise ValueError(\"--output must end in .parquet or .csv\")\n\n\ndef main() -> int:\n parser = argparse.ArgumentParser(description=__doc__)\n parser.add_argument(\n \"--stage\", choices=(\"validation\", \"submission\"), default=\"validation\"\n )\n parser.add_argument(\"--output\", type=Path, required=True)\n parser.add_argument(\"--public-dir\", type=Path, required=True)\n parser.add_argument(\"--prepared-cache\", type=Path)\n parser.add_argument(\"--device\", choices=(\"cpu\", \"mps\"), default=\"cpu\")\n parser.add_argument(\"--threads\", type=int, default=8)\n parser.add_argument(\"--bptt-steps\", type=int, default=Config.bptt_steps)\n parser.add_argument(\n \"--user-batch-size\", type=int, default=Config.user_batch_size\n )\n parser.add_argument(\"--epochs\", type=int, default=Config.epochs)\n parser.add_argument(\"--learning-rate\", type=float, default=Config.learning_rate)\n parser.add_argument(\n \"--loss-microbatch-size\",\n type=int,\n default=Config.loss_microbatch_size,\n )\n parser.add_argument(\n \"--negative-filter-scope\",\n choices=(\"full-training-history\", \"last-100\"),\n default=Config.negative_filter_scope,\n )\n parser.add_argument(\n \"--target-weighting\",\n choices=(\"discovery-graded\", \"organic-strong\", \"uniform\"),\n default=Config.target_weighting,\n )\n parser.add_argument(\n \"--ordinary-target-weight\",\n type=float,\n default=Config.ordinary_target_weight,\n )\n parser.add_argument(\n \"--organic-strong-target-weight\",\n type=float,\n default=Config.organic_strong_target_weight,\n )\n parser.add_argument(\n \"--organic-like-target-weight\",\n type=float,\n default=Config.organic_like_target_weight,\n )\n parser.add_argument(\n \"--discovery-horizon-days\",\n type=int,\n default=Config.discovery_horizon_days,\n )\n parser.add_argument(\n \"--validation-fraction\",\n type=float,\n default=Config.validation_fraction,\n )\n parser.add_argument(\n \"--max-train-targets-per-user\",\n type=int,\n default=Config.max_train_targets_per_user,\n help=\"retain this many recent pre-validation targets per user; 0 is uncapped\",\n )\n parser.add_argument(\n \"--max-validation-targets-per-user\",\n type=int,\n default=Config.max_validation_targets_per_user,\n help=\"retain this many final validation targets per user; 0 is uncapped\",\n )\n parser.add_argument(\n \"--early-stopping-patience\",\n type=int,\n default=Config.early_stopping_patience,\n )\n parser.add_argument(\n \"--early-stopping-min-delta\",\n type=float,\n default=Config.early_stopping_min_delta,\n )\n parser.add_argument(\"--checkpoint-dir\", type=Path)\n parser.add_argument(\"--resume-checkpoint\", type=Path)\n parser.add_argument(\"--seed\", type=int, default=Config.seed)\n parser.add_argument(\"--ranking-batch-size\", type=int, default=64)\n parser.add_argument(\"--prepare-only\", action=\"store_true\")\n args = parser.parse_args()\n\n if args.threads < 1 or args.bptt_steps < 2 or args.user_batch_size < 1:\n parser.error(\"threads, BPTT steps, and user batch size must be positive\")\n if (\n args.epochs < 1\n or args.learning_rate <= 0\n or args.loss_microbatch_size < 1\n or args.early_stopping_patience < 1\n or args.early_stopping_min_delta < 0\n or args.ordinary_target_weight <= 0\n or args.organic_strong_target_weight <= 0\n or args.organic_like_target_weight <= 0\n or args.discovery_horizon_days < 1\n or args.max_train_targets_per_user < 0\n or args.max_validation_targets_per_user < 0\n ):\n parser.error(\"training counts, learning rate, and patience are invalid\")\n if not 0.0 < args.validation_fraction < 1.0:\n parser.error(\"validation fraction must be strictly between zero and one\")\n public = args.public_dir.resolve()\n if not (public / \"data\" / \"candidate_catalog.parquet\").is_file():\n parser.error(f\"public task data not found under {public}\")\n config = Config(\n bptt_steps=args.bptt_steps,\n user_batch_size=args.user_batch_size,\n epochs=args.epochs,\n learning_rate=args.learning_rate,\n loss_microbatch_size=args.loss_microbatch_size,\n negative_filter_scope=args.negative_filter_scope,\n target_weighting=args.target_weighting,\n ordinary_target_weight=args.ordinary_target_weight,\n organic_strong_target_weight=args.organic_strong_target_weight,\n organic_like_target_weight=args.organic_like_target_weight,\n discovery_horizon_days=args.discovery_horizon_days,\n validation_fraction=args.validation_fraction,\n max_train_targets_per_user=args.max_train_targets_per_user,\n max_validation_targets_per_user=args.max_validation_targets_per_user,\n early_stopping_patience=args.early_stopping_patience,\n early_stopping_min_delta=args.early_stopping_min_delta,\n seed=args.seed,\n )\n torch.set_num_threads(args.threads)\n torch.set_num_interop_threads(1)\n seed_everything(config.seed)\n device = torch.device(args.device)\n if device.type == \"mps\" and not torch.backends.mps.is_available():\n parser.error(\"MPS was requested but is unavailable\")\n\n started = time.monotonic()\n prepared, eligibility = load_or_prepare(\n public,\n args.stage,\n config,\n args.prepared_cache.resolve() if args.prepared_cache else None,\n )\n target_partition = chronological_target_partition(\n prepared,\n config.validation_fraction,\n config.max_train_targets_per_user,\n config.max_validation_targets_per_user,\n )\n if args.prepare_only:\n print(\n json.dumps(\n {\n \"stage\": args.stage,\n \"sequence_users\": len(prepared.user_ids),\n \"sequence_tokens\": len(prepared.sequence_items),\n \"next_item_targets\": prepared.target_count,\n \"selected_training_targets\": int(\n target_partition.training_counts.sum()\n ),\n \"selected_validation_targets\": int(\n target_partition.validation_counts.sum()\n ),\n \"discarded_old_targets\": int(\n target_partition.window_starts.sum()\n ),\n \"target_users\": len(eligibility.uids),\n \"runtime_seconds\": round(time.monotonic() - started, 3),\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "session_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "root_turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "response_id": "resp_08539f19231b9e9f016ab5aba4bd0087d298f4a7d0ca4c7919", "usage": { "input_tokens": 68457, "cached_input_tokens": 66048, "cache_write_input_tokens": 0, "output_tokens": 140, "reasoning_output_tokens": 74, "total_tokens": 68597 }, "turn_token_usage": { "input_tokens": 1910213, "cached_input_tokens": 1846272, "cache_write_input_tokens": 0, "output_tokens": 13167, "reasoning_output_tokens": 5027, "total_tokens": 1923380 }, "thread_token_usage": { "input_tokens": 1910213, "cached_input_tokens": 1846272, "cache_write_input_tokens": 0, "output_tokens": 13167, "reasoning_output_tokens": 5027, "total_tokens": 1923380 } } ## Step 52 · agent · 2026-09-24T23:01:08.139Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T23:01:09.273Z { "code": "const r = await tools.exec_command({\"cmd\":\"sed -n '2245,2325p' /app/solution/train_current_model.py\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":10000}); text(r.output);" } ### Tool output · 2026-09-24T23:01:09.378Z Script completed Wall time 0.0 seconds Output: "runtime_seconds": round(time.monotonic() - started, 3), "config": asdict(config), }, indent=2, ) ) return 0 model, training_history = train_model( public, args.stage, prepared, config, device, args.checkpoint_dir.resolve() if args.checkpoint_dir else None, args.resume_checkpoint.resolve() if args.resume_checkpoint else None, ) ranking, embedding_diagnostics = rank_candidates( model, prepared, eligibility, config, device, args.ranking_batch_size, ) write_ranking(ranking, args.output.resolve()) print( json.dumps( { "baseline": "yambda-windowed-sequence-two-tower-base", "stage": args.stage, "output": str(args.output.resolve()), "rows": len(ranking), "users": int(ranking.uid.nunique()), "runtime_seconds": round(time.monotonic() - started, 3), "public_inputs_only": True, "architecture": { "family": "pure-windowed-sequence-two-tower", "sequence_training": ( "every-adjacent-target-within-recent-chronological-window" ), "sequence_encoder": "one-layer-GRU-dense-timestep-loss", "bptt_hidden_state": "carried-and-detached", "model_selection": "chronological-validation-BCE-early-stopping", "loss_execution": "gradient-accumulated-microbatches", "user_id_embedding": False, "user_mlp": "582-512-256-192", "item_mlp": "196-512-256-192", "normalization": "none", "score": "raw-dot-product", "objective": "sample-weighted-BCE-at-every-timestep", "negative_sampling": ( "24-uniform-plus-8-propensity-corrected-inbatch" ), "false_negative_filter": config.negative_filter_scope, "target_weighting": config.target_weighting, }, "config": asdict(config), "sequence_tokens": len(prepared.sequence_items), "all_next_item_targets": prepared.target_count, "selected_training_targets": int( target_partition.training_counts.sum() ), "selected_validation_targets": int( target_partition.validation_counts.sum() ), "discarded_old_targets": int(target_partition.window_starts.sum()), "training_history": training_history, "embedding_diagnostics": embedding_diagnostics, "torch_version": torch.__version__, "device": str(device), }, indent=2, ) ) return 0 if __name__ == "__main__": raise SystemExit(main()) ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-daa502b4-ec60-497d-86bb-441f2f108ee2", "process_id": "64661", "command": [ "/bin/sh", "-lc", "sed -n '2245,2325p' /app/solution/train_current_model.py" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "read", "cmd": "sed -n '2245,2325p' /app/solution/train_current_model.py", "name": "train_current_model.py", "path": "/app/solution/train_current_model.py" } ], "source": "unified_exec_startup", "status": "completed", "stdout": " \"runtime_seconds\": round(time.monotonic() - started, 3),\n \"config\": asdict(config),\n },\n indent=2,\n )\n )\n return 0\n\n model, training_history = train_model(\n public,\n args.stage,\n prepared,\n config,\n device,\n args.checkpoint_dir.resolve() if args.checkpoint_dir else None,\n args.resume_checkpoint.resolve() if args.resume_checkpoint else None,\n )\n ranking, embedding_diagnostics = rank_candidates(\n model,\n prepared,\n eligibility,\n config,\n device,\n args.ranking_batch_size,\n )\n write_ranking(ranking, args.output.resolve())\n print(\n json.dumps(\n {\n \"baseline\": \"yambda-windowed-sequence-two-tower-base\",\n \"stage\": args.stage,\n \"output\": str(args.output.resolve()),\n \"rows\": len(ranking),\n \"users\": int(ranking.uid.nunique()),\n \"runtime_seconds\": round(time.monotonic() - started, 3),\n \"public_inputs_only\": True,\n \"architecture\": {\n \"family\": \"pure-windowed-sequence-two-tower\",\n \"sequence_training\": (\n \"every-adjacent-target-within-recent-chronological-window\"\n ),\n \"sequence_encoder\": \"one-layer-GRU-dense-timestep-loss\",\n \"bptt_hidden_state\": \"carried-and-detached\",\n \"model_selection\": \"chronological-validation-BCE-early-stopping\",\n \"loss_execution\": \"gradient-accumulated-microbatches\",\n \"user_id_embedding\": False,\n \"user_mlp\": \"582-512-256-192\",\n \"item_mlp\": \"196-512-256-192\",\n \"normalization\": \"none\",\n \"score\": \"raw-dot-product\",\n \"objective\": \"sample-weighted-BCE-at-every-timestep\",\n \"negative_sampling\": (\n \"24-uniform-plus-8-propensity-corrected-inbatch\"\n ),\n \"false_negative_filter\": config.negative_filter_scope,\n \"target_weighting\": config.target_weighting,\n },\n \"config\": asdict(config),\n \"sequence_tokens\": len(prepared.sequence_items),\n \"all_next_item_targets\": prepared.target_count,\n \"selected_training_targets\": int(\n target_partition.training_counts.sum()\n ),\n \"selected_validation_targets\": int(\n target_partition.validation_counts.sum()\n ),\n \"discarded_old_targets\": int(target_partition.window_starts.sum()),\n \"training_history\": training_history,\n \"embedding_diagnostics\": embedding_diagnostics,\n \"torch_version\": torch.__version__,\n \"device\": str(device),\n },\n indent=2,\n )\n )\n return 0\n\n\nif __name__ == \"__main__\":\n raise SystemExit(main())\n", "stderr": "", "aggregated_output": " \"runtime_seconds\": round(time.monotonic() - started, 3),\n \"config\": asdict(config),\n },\n indent=2,\n )\n )\n return 0\n\n model, training_history = train_model(\n public,\n args.stage,\n prepared,\n config,\n device,\n args.checkpoint_dir.resolve() if args.checkpoint_dir else None,\n args.resume_checkpoint.resolve() if args.resume_checkpoint else None,\n )\n ranking, embedding_diagnostics = rank_candidates(\n model,\n prepared,\n eligibility,\n config,\n device,\n args.ranking_batch_size,\n )\n write_ranking(ranking, args.output.resolve())\n print(\n json.dumps(\n {\n \"baseline\": \"yambda-windowed-sequence-two-tower-base\",\n \"stage\": args.stage,\n \"output\": str(args.output.resolve()),\n \"rows\": len(ranking),\n \"users\": int(ranking.uid.nunique()),\n \"runtime_seconds\": round(time.monotonic() - started, 3),\n \"public_inputs_only\": True,\n \"architecture\": {\n \"family\": \"pure-windowed-sequence-two-tower\",\n \"sequence_training\": (\n \"every-adjacent-target-within-recent-chronological-window\"\n ),\n \"sequence_encoder\": \"one-layer-GRU-dense-timestep-loss\",\n \"bptt_hidden_state\": \"carried-and-detached\",\n \"model_selection\": \"chronological-validation-BCE-early-stopping\",\n \"loss_execution\": \"gradient-accumulated-microbatches\",\n \"user_id_embedding\": False,\n \"user_mlp\": \"582-512-256-192\",\n \"item_mlp\": \"196-512-256-192\",\n \"normalization\": \"none\",\n \"score\": \"raw-dot-product\",\n \"objective\": \"sample-weighted-BCE-at-every-timestep\",\n \"negative_sampling\": (\n \"24-uniform-plus-8-propensity-corrected-inbatch\"\n ),\n \"false_negative_filter\": config.negative_filter_scope,\n \"target_weighting\": config.target_weighting,\n },\n \"config\": asdict(config),\n \"sequence_tokens\": len(prepared.sequence_items),\n \"all_next_item_targets\": prepared.target_count,\n \"selected_training_targets\": int(\n target_partition.training_counts.sum()\n ),\n \"selected_validation_targets\": int(\n target_partition.validation_counts.sum()\n ),\n \"discarded_old_targets\": int(target_partition.window_starts.sum()),\n \"training_history\": training_history,\n \"embedding_diagnostics\": embedding_diagnostics,\n \"torch_version\": torch.__version__,\n \"device\": str(device),\n },\n indent=2,\n )\n )\n return 0\n\n\nif __name__ == \"__main__\":\n raise SystemExit(main())\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 6930 }, "formatted_output": " \"runtime_seconds\": round(time.monotonic() - started, 3),\n \"config\": asdict(config),\n },\n indent=2,\n )\n )\n return 0\n\n model, training_history = train_model(\n public,\n args.stage,\n prepared,\n config,\n device,\n args.checkpoint_dir.resolve() if args.checkpoint_dir else None,\n args.resume_checkpoint.resolve() if args.resume_checkpoint else None,\n )\n ranking, embedding_diagnostics = rank_candidates(\n model,\n prepared,\n eligibility,\n config,\n device,\n args.ranking_batch_size,\n )\n write_ranking(ranking, args.output.resolve())\n print(\n json.dumps(\n {\n \"baseline\": \"yambda-windowed-sequence-two-tower-base\",\n \"stage\": args.stage,\n \"output\": str(args.output.resolve()),\n \"rows\": len(ranking),\n \"users\": int(ranking.uid.nunique()),\n \"runtime_seconds\": round(time.monotonic() - started, 3),\n \"public_inputs_only\": True,\n \"architecture\": {\n \"family\": \"pure-windowed-sequence-two-tower\",\n \"sequence_training\": (\n \"every-adjacent-target-within-recent-chronological-window\"\n ),\n \"sequence_encoder\": \"one-layer-GRU-dense-timestep-loss\",\n \"bptt_hidden_state\": \"carried-and-detached\",\n \"model_selection\": \"chronological-validation-BCE-early-stopping\",\n \"loss_execution\": \"gradient-accumulated-microbatches\",\n \"user_id_embedding\": False,\n \"user_mlp\": \"582-512-256-192\",\n \"item_mlp\": \"196-512-256-192\",\n \"normalization\": \"none\",\n \"score\": \"raw-dot-product\",\n \"objective\": \"sample-weighted-BCE-at-every-timestep\",\n \"negative_sampling\": (\n \"24-uniform-plus-8-propensity-corrected-inbatch\"\n ),\n \"false_negative_filter\": config.negative_filter_scope,\n \"target_weighting\": config.target_weighting,\n },\n \"config\": asdict(config),\n \"sequence_tokens\": len(prepared.sequence_items),\n \"all_next_item_targets\": prepared.target_count,\n \"selected_training_targets\": int(\n target_partition.training_counts.sum()\n ),\n \"selected_validation_targets\": int(\n target_partition.validation_counts.sum()\n ),\n \"discarded_old_targets\": int(target_partition.window_starts.sum()),\n \"training_history\": training_history,\n \"embedding_diagnostics\": embedding_diagnostics,\n \"torch_version\": torch.__version__,\n \"device\": str(device),\n },\n indent=2,\n )\n )\n return 0\n\n\nif __name__ == \"__main__\":\n raise SystemExit(main())\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "session_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "root_turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "response_id": "resp_08539f19231b9e9f016ab5abac054c87d28ba9708c24574a65", "usage": { "input_tokens": 70222, "cached_input_tokens": 68224, "cache_write_input_tokens": 0, "output_tokens": 73, "reasoning_output_tokens": 7, "total_tokens": 70295 }, "turn_token_usage": { "input_tokens": 1980435, "cached_input_tokens": 1914496, "cache_write_input_tokens": 0, "output_tokens": 13240, "reasoning_output_tokens": 5034, "total_tokens": 1993675 }, "thread_token_usage": { "input_tokens": 1980435, "cached_input_tokens": 1914496, "cache_write_input_tokens": 0, "output_tokens": 13240, "reasoning_output_tokens": 5034, "total_tokens": 1993675 } } ## Step 53 · agent · 2026-09-24T23:01:16.922Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T23:01:23.510Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/train_current_model.py\\n@@\\n flat_user_rows = np.broadcast_to(\\n users[:, None], block[5].shape\\n ).reshape(-1)[flat_scored]\\n+ flat_activity = block[2].reshape(-1, 4)[flat_scored].astype(np.float32)\\n negatives, negative_weights = draw_training_negatives(\\n+ model,\\n+ sequence_vectors,\\n+ flat_states,\\n+ flat_activity,\\n positive_items,\\n known_history,\\n liked_items,\\n@@\\n config,\\n sequence_vectors,\\n flat_states,\\n- block[2].reshape(-1, 4)[flat_scored].astype(np.float32),\\n+ flat_activity,\\n@@\\n flat_user_rows = np.broadcast_to(\\n users[:, None], block[5].shape\\n ).reshape(-1)[flat_scored]\\n+ flat_activity = block[2].reshape(-1, 4)[flat_scored].astype(np.float32)\\n negatives, negative_weights = draw_training_negatives(\\n+ model,\\n+ sequence_vectors,\\n+ flat_states,\\n+ flat_activity,\\n positive_items,\\n known_history,\\n liked_items,\\n@@\\n config,\\n sequence_vectors,\\n flat_states,\\n- block[2].reshape(-1, 4)[flat_scored].astype(np.float32),\\n+ flat_activity,\\n*** End Patch\"; text(await tools.apply_patch(patch));" } ### Tool output · 2026-09-24T23:01:23.614Z Script completed Wall time 0.0 seconds Output: {} ### Harness context { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-24058e65-6320-46de-99b6-decd0bc8d69c", "changes": { "/app/solution/train_current_model.py": { "type": "update", "unified_diff": "@@ -1452,3 +1452,8 @@\n ).reshape(-1)[flat_scored]\n+ flat_activity = block[2].reshape(-1, 4)[flat_scored].astype(np.float32)\n negatives, negative_weights = draw_training_negatives(\n+ model,\n+ sequence_vectors,\n+ flat_states,\n+ flat_activity,\n positive_items,\n@@ -1471,3 +1476,3 @@\n flat_states,\n- block[2].reshape(-1, 4)[flat_scored].astype(np.float32),\n+ flat_activity,\n positive_items,\n@@ -1608,3 +1613,8 @@\n ).reshape(-1)[flat_scored]\n+ flat_activity = block[2].reshape(-1, 4)[flat_scored].astype(np.float32)\n negatives, negative_weights = draw_training_negatives(\n+ model,\n+ sequence_vectors,\n+ flat_states,\n+ flat_activity,\n positive_items,\n@@ -1626,3 +1636,3 @@\n flat_states,\n- block[2].reshape(-1, 4)[flat_scored].astype(np.float32),\n+ flat_activity,\n positive_items,\n", "move_path": null } }, "status": "completed", "stdout": "Success. Updated the following files:\nM /app/solution/train_current_model.py\n", "stderr": "" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "session_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "root_turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "response_id": "resp_08539f19231b9e9f016ab5abb5b3e487d2bbde600cc458d74a", "usage": { "input_tokens": 70914, "cached_input_tokens": 70016, "cache_write_input_tokens": 0, "output_tokens": 366, "reasoning_output_tokens": 6, "total_tokens": 71280 }, "turn_token_usage": { "input_tokens": 2051349, "cached_input_tokens": 1984512, "cache_write_input_tokens": 0, "output_tokens": 13606, "reasoning_output_tokens": 5040, "total_tokens": 2064955 }, "thread_token_usage": { "input_tokens": 2051349, "cached_input_tokens": 1984512, "cache_write_input_tokens": 0, "output_tokens": 13606, "reasoning_output_tokens": 5040, "total_tokens": 2064955 } } ## Step 54 · agent · 2026-09-24T23:01:28.601Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T23:01:30.317Z { "code": "const r = await tools.exec_command({\"cmd\":\"sed -n '855,940p' /app/solution/train_current_model.py; sed -n '1440,1495p' /app/solution/train_current_model.py; sed -n '1600,1655p' /app/solution/train_current_model.py\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":15000}); text(r.output);" } ### Tool output · 2026-09-24T23:01:30.420Z Script completed Wall time 0.0 seconds Output: if np.any(invalid): raise RuntimeError("full-history negative resampling failed") return ( torch.from_numpy(negatives).to(device), torch.from_numpy(correction).to(device), ) def draw_training_negatives( model: base.SequentialTwoTower, sequence_vectors: torch.Tensor, flat_states: np.ndarray, flat_activity: np.ndarray, positive_items: torch.Tensor, known_history: torch.Tensor, liked_items: torch.Tensor, user_rows: np.ndarray, known_positive_packed: np.ndarray | None, prepared: SequenceData, config: Config, item_correction: torch.Tensor, generator: torch.Generator, device: torch.device, ) -> tuple[torch.Tensor, torch.Tensor]: if config.negative_sampling == "hard-mixed": if known_positive_packed is None: raise RuntimeError("hard mining requires the full-history filter") return draw_hard_mixed_negatives( model, sequence_vectors, flat_states, flat_activity, positive_items, user_rows, liked_items, known_positive_packed, prepared, config, generator, device, ) if config.negative_filter_scope == "full-training-history": if known_positive_packed is None: raise RuntimeError("full-history negative filter was not built") return draw_full_history_filtered_negatives( positive_items, user_rows, liked_items, known_positive_packed, len(prepared.item_ids), config, item_correction, generator, device, ) return base.draw_filtered_mixed_negatives( positive_items, known_history, liked_items, len(prepared.item_ids), config.uniform_negatives, config.inbatch_negatives, item_correction, generator, device, ) def event_sequence( model: base.SequentialTwoTower, items: torch.Tensor, features: torch.Tensor, hidden: torch.Tensor, ) -> tuple[torch.Tensor, torch.Tensor]: inputs = torch.cat([model.context_embedding(items), features], dim=-1) projected = model.event_projection(inputs) return model.sequence_encoder(projected, hidden) def pool_explicit_states( model: base.SequentialTwoTower, prepared: SequenceData, state_ids: np.ndarray, device: torch.device, ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: unique_states, inverse = np.unique(state_ids, return_inverse=True) block[4].reshape(-1)[flat_scored].astype(np.int64) ).to(device) known_history = torch.from_numpy( block[6].reshape(-1, config.bptt_steps)[flat_scored].astype( np.int64 ) ).to(device) liked_items = torch.from_numpy( prepared.liked_states[flat_states].astype(np.int64) ).to(device) flat_user_rows = np.broadcast_to( users[:, None], block[5].shape ).reshape(-1)[flat_scored] flat_activity = block[2].reshape(-1, 4)[flat_scored].astype(np.float32) negatives, negative_weights = draw_training_negatives( model, sequence_vectors, flat_states, flat_activity, positive_items, known_history, liked_items, flat_user_rows, known_positive_packed, prepared, config, item_correction, generator, device, ) optimizer.zero_grad(set_to_none=True) statistics, sequence_gradient = score_target_microbatches( model, prepared, config, sequence_vectors, flat_states, flat_activity, positive_items, negatives, negative_weights, block[8].reshape(-1)[flat_scored], device, backward=True, ) if sequence_gradient is None: raise RuntimeError("training block omitted its sequence gradient") sequence_vectors.backward(sequence_gradient) nn.utils.clip_grad_norm_(model.parameters(), config.gradient_clip) optimizer.step() count = len(positive_items) total_loss += statistics["loss_sum"] total_targets += count total_weight += statistics["weight_sum"] total_positive += statistics["positive_sum"] positive_items = torch.from_numpy( block[4].reshape(-1)[flat_scored].astype(np.int64) ).to(device) known_history = torch.from_numpy( block[6].reshape(-1, config.bptt_steps)[flat_scored].astype( np.int64 ) ).to(device) liked_items = torch.from_numpy( prepared.liked_states[flat_states].astype(np.int64) ).to(device) flat_user_rows = np.broadcast_to( users[:, None], block[5].shape ).reshape(-1)[flat_scored] flat_activity = block[2].reshape(-1, 4)[flat_scored].astype(np.float32) negatives, negative_weights = draw_training_negatives( model, sequence_vectors, flat_states, flat_activity, positive_items, known_history, liked_items, flat_user_rows, known_positive_packed, prepared, config, item_correction, generator, device, ) statistics, _ = score_target_microbatches( model, prepared, config, sequence_vectors, flat_states, flat_activity, positive_items, negatives, negative_weights, block[8].reshape(-1)[flat_scored], device, backward=False, ) total_loss += statistics["loss_sum"] total_targets += len(positive_items) total_weight += statistics["weight_sum"] if group_number % 25 == 0: release_accelerator_cache(device) return { "validation_loss": total_loss / max(total_weight, 1.0e-12), "validation_targets": float(total_targets), "validation_target_weight_sum": total_weight, "validation_seconds": time.monotonic() - started, } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-110574de-43dc-43b9-bf36-2a380731cc08", "process_id": "28725", "command": [ "/bin/sh", "-lc", "sed -n '855,940p' /app/solution/train_current_model.py; sed -n '1440,1495p' /app/solution/train_current_model.py; sed -n '1600,1655p' /app/solution/train_current_model.py" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "read", "cmd": "sed -n '855,940p' /app/solution/train_current_model.py", "name": "train_current_model.py", "path": "/app/solution/train_current_model.py" }, { "type": "read", "cmd": "sed -n '1440,1495p' /app/solution/train_current_model.py", "name": "train_current_model.py", "path": "/app/solution/train_current_model.py" }, { "type": "read", "cmd": "sed -n '1600,1655p' /app/solution/train_current_model.py", "name": "train_current_model.py", "path": "/app/solution/train_current_model.py" } ], "source": "unified_exec_startup", "status": "completed", "stdout": " if np.any(invalid):\n raise RuntimeError(\"full-history negative resampling failed\")\n return (\n torch.from_numpy(negatives).to(device),\n torch.from_numpy(correction).to(device),\n )\n\n\ndef draw_training_negatives(\n model: base.SequentialTwoTower,\n sequence_vectors: torch.Tensor,\n flat_states: np.ndarray,\n flat_activity: np.ndarray,\n positive_items: torch.Tensor,\n known_history: torch.Tensor,\n liked_items: torch.Tensor,\n user_rows: np.ndarray,\n known_positive_packed: np.ndarray | None,\n prepared: SequenceData,\n config: Config,\n item_correction: torch.Tensor,\n generator: torch.Generator,\n device: torch.device,\n) -> tuple[torch.Tensor, torch.Tensor]:\n if config.negative_sampling == \"hard-mixed\":\n if known_positive_packed is None:\n raise RuntimeError(\"hard mining requires the full-history filter\")\n return draw_hard_mixed_negatives(\n model,\n sequence_vectors,\n flat_states,\n flat_activity,\n positive_items,\n user_rows,\n liked_items,\n known_positive_packed,\n prepared,\n config,\n generator,\n device,\n )\n if config.negative_filter_scope == \"full-training-history\":\n if known_positive_packed is None:\n raise RuntimeError(\"full-history negative filter was not built\")\n return draw_full_history_filtered_negatives(\n positive_items,\n user_rows,\n liked_items,\n known_positive_packed,\n len(prepared.item_ids),\n config,\n item_correction,\n generator,\n device,\n )\n return base.draw_filtered_mixed_negatives(\n positive_items,\n known_history,\n liked_items,\n len(prepared.item_ids),\n config.uniform_negatives,\n config.inbatch_negatives,\n item_correction,\n generator,\n device,\n )\n\n\ndef event_sequence(\n model: base.SequentialTwoTower,\n items: torch.Tensor,\n features: torch.Tensor,\n hidden: torch.Tensor,\n) -> tuple[torch.Tensor, torch.Tensor]:\n inputs = torch.cat([model.context_embedding(items), features], dim=-1)\n projected = model.event_projection(inputs)\n return model.sequence_encoder(projected, hidden)\n\n\ndef pool_explicit_states(\n model: base.SequentialTwoTower,\n prepared: SequenceData,\n state_ids: np.ndarray,\n device: torch.device,\n) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:\n unique_states, inverse = np.unique(state_ids, return_inverse=True)\n block[4].reshape(-1)[flat_scored].astype(np.int64)\n ).to(device)\n known_history = torch.from_numpy(\n block[6].reshape(-1, config.bptt_steps)[flat_scored].astype(\n np.int64\n )\n ).to(device)\n liked_items = torch.from_numpy(\n prepared.liked_states[flat_states].astype(np.int64)\n ).to(device)\n flat_user_rows = np.broadcast_to(\n users[:, None], block[5].shape\n ).reshape(-1)[flat_scored]\n flat_activity = block[2].reshape(-1, 4)[flat_scored].astype(np.float32)\n negatives, negative_weights = draw_training_negatives(\n model,\n sequence_vectors,\n flat_states,\n flat_activity,\n positive_items,\n known_history,\n liked_items,\n flat_user_rows,\n known_positive_packed,\n prepared,\n config,\n item_correction,\n generator,\n device,\n )\n optimizer.zero_grad(set_to_none=True)\n statistics, sequence_gradient = score_target_microbatches(\n model,\n prepared,\n config,\n sequence_vectors,\n flat_states,\n flat_activity,\n positive_items,\n negatives,\n negative_weights,\n block[8].reshape(-1)[flat_scored],\n device,\n backward=True,\n )\n if sequence_gradient is None:\n raise RuntimeError(\"training block omitted its sequence gradient\")\n sequence_vectors.backward(sequence_gradient)\n nn.utils.clip_grad_norm_(model.parameters(), config.gradient_clip)\n optimizer.step()\n\n count = len(positive_items)\n total_loss += statistics[\"loss_sum\"]\n total_targets += count\n total_weight += statistics[\"weight_sum\"]\n total_positive += statistics[\"positive_sum\"]\n positive_items = torch.from_numpy(\n block[4].reshape(-1)[flat_scored].astype(np.int64)\n ).to(device)\n known_history = torch.from_numpy(\n block[6].reshape(-1, config.bptt_steps)[flat_scored].astype(\n np.int64\n )\n ).to(device)\n liked_items = torch.from_numpy(\n prepared.liked_states[flat_states].astype(np.int64)\n ).to(device)\n flat_user_rows = np.broadcast_to(\n users[:, None], block[5].shape\n ).reshape(-1)[flat_scored]\n flat_activity = block[2].reshape(-1, 4)[flat_scored].astype(np.float32)\n negatives, negative_weights = draw_training_negatives(\n model,\n sequence_vectors,\n flat_states,\n flat_activity,\n positive_items,\n known_history,\n liked_items,\n flat_user_rows,\n known_positive_packed,\n prepared,\n config,\n item_correction,\n generator,\n device,\n )\n statistics, _ = score_target_microbatches(\n model,\n prepared,\n config,\n sequence_vectors,\n flat_states,\n flat_activity,\n positive_items,\n negatives,\n negative_weights,\n block[8].reshape(-1)[flat_scored],\n device,\n backward=False,\n )\n total_loss += statistics[\"loss_sum\"]\n total_targets += len(positive_items)\n total_weight += statistics[\"weight_sum\"]\n if group_number % 25 == 0:\n release_accelerator_cache(device)\n return {\n \"validation_loss\": total_loss / max(total_weight, 1.0e-12),\n \"validation_targets\": float(total_targets),\n \"validation_target_weight_sum\": total_weight,\n \"validation_seconds\": time.monotonic() - started,\n }\n", "stderr": "", "aggregated_output": " if np.any(invalid):\n raise RuntimeError(\"full-history negative resampling failed\")\n return (\n torch.from_numpy(negatives).to(device),\n torch.from_numpy(correction).to(device),\n )\n\n\ndef draw_training_negatives(\n model: base.SequentialTwoTower,\n sequence_vectors: torch.Tensor,\n flat_states: np.ndarray,\n flat_activity: np.ndarray,\n positive_items: torch.Tensor,\n known_history: torch.Tensor,\n liked_items: torch.Tensor,\n user_rows: np.ndarray,\n known_positive_packed: np.ndarray | None,\n prepared: SequenceData,\n config: Config,\n item_correction: torch.Tensor,\n generator: torch.Generator,\n device: torch.device,\n) -> tuple[torch.Tensor, torch.Tensor]:\n if config.negative_sampling == \"hard-mixed\":\n if known_positive_packed is None:\n raise RuntimeError(\"hard mining requires the full-history filter\")\n return draw_hard_mixed_negatives(\n model,\n sequence_vectors,\n flat_states,\n flat_activity,\n positive_items,\n user_rows,\n liked_items,\n known_positive_packed,\n prepared,\n config,\n generator,\n device,\n )\n if config.negative_filter_scope == \"full-training-history\":\n if known_positive_packed is None:\n raise RuntimeError(\"full-history negative filter was not built\")\n return draw_full_history_filtered_negatives(\n positive_items,\n user_rows,\n liked_items,\n known_positive_packed,\n len(prepared.item_ids),\n config,\n item_correction,\n generator,\n device,\n )\n return base.draw_filtered_mixed_negatives(\n positive_items,\n known_history,\n liked_items,\n len(prepared.item_ids),\n config.uniform_negatives,\n config.inbatch_negatives,\n item_correction,\n generator,\n device,\n )\n\n\ndef event_sequence(\n model: base.SequentialTwoTower,\n items: torch.Tensor,\n features: torch.Tensor,\n hidden: torch.Tensor,\n) -> tuple[torch.Tensor, torch.Tensor]:\n inputs = torch.cat([model.context_embedding(items), features], dim=-1)\n projected = model.event_projection(inputs)\n return model.sequence_encoder(projected, hidden)\n\n\ndef pool_explicit_states(\n model: base.SequentialTwoTower,\n prepared: SequenceData,\n state_ids: np.ndarray,\n device: torch.device,\n) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:\n unique_states, inverse = np.unique(state_ids, return_inverse=True)\n block[4].reshape(-1)[flat_scored].astype(np.int64)\n ).to(device)\n known_history = torch.from_numpy(\n block[6].reshape(-1, config.bptt_steps)[flat_scored].astype(\n np.int64\n )\n ).to(device)\n liked_items = torch.from_numpy(\n prepared.liked_states[flat_states].astype(np.int64)\n ).to(device)\n flat_user_rows = np.broadcast_to(\n users[:, None], block[5].shape\n ).reshape(-1)[flat_scored]\n flat_activity = block[2].reshape(-1, 4)[flat_scored].astype(np.float32)\n negatives, negative_weights = draw_training_negatives(\n model,\n sequence_vectors,\n flat_states,\n flat_activity,\n positive_items,\n known_history,\n liked_items,\n flat_user_rows,\n known_positive_packed,\n prepared,\n config,\n item_correction,\n generator,\n device,\n )\n optimizer.zero_grad(set_to_none=True)\n statistics, sequence_gradient = score_target_microbatches(\n model,\n prepared,\n config,\n sequence_vectors,\n flat_states,\n flat_activity,\n positive_items,\n negatives,\n negative_weights,\n block[8].reshape(-1)[flat_scored],\n device,\n backward=True,\n )\n if sequence_gradient is None:\n raise RuntimeError(\"training block omitted its sequence gradient\")\n sequence_vectors.backward(sequence_gradient)\n nn.utils.clip_grad_norm_(model.parameters(), config.gradient_clip)\n optimizer.step()\n\n count = len(positive_items)\n total_loss += statistics[\"loss_sum\"]\n total_targets += count\n total_weight += statistics[\"weight_sum\"]\n total_positive += statistics[\"positive_sum\"]\n positive_items = torch.from_numpy(\n block[4].reshape(-1)[flat_scored].astype(np.int64)\n ).to(device)\n known_history = torch.from_numpy(\n block[6].reshape(-1, config.bptt_steps)[flat_scored].astype(\n np.int64\n )\n ).to(device)\n liked_items = torch.from_numpy(\n prepared.liked_states[flat_states].astype(np.int64)\n ).to(device)\n flat_user_rows = np.broadcast_to(\n users[:, None], block[5].shape\n ).reshape(-1)[flat_scored]\n flat_activity = block[2].reshape(-1, 4)[flat_scored].astype(np.float32)\n negatives, negative_weights = draw_training_negatives(\n model,\n sequence_vectors,\n flat_states,\n flat_activity,\n positive_items,\n known_history,\n liked_items,\n flat_user_rows,\n known_positive_packed,\n prepared,\n config,\n item_correction,\n generator,\n device,\n )\n statistics, _ = score_target_microbatches(\n model,\n prepared,\n config,\n sequence_vectors,\n flat_states,\n flat_activity,\n positive_items,\n negatives,\n negative_weights,\n block[8].reshape(-1)[flat_scored],\n device,\n backward=False,\n )\n total_loss += statistics[\"loss_sum\"]\n total_targets += len(positive_items)\n total_weight += statistics[\"weight_sum\"]\n if group_number % 25 == 0:\n release_accelerator_cache(device)\n return {\n \"validation_loss\": total_loss / max(total_weight, 1.0e-12),\n \"validation_targets\": float(total_targets),\n \"validation_target_weight_sum\": total_weight,\n \"validation_seconds\": time.monotonic() - started,\n }\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4760 }, "formatted_output": " if np.any(invalid):\n raise RuntimeError(\"full-history negative resampling failed\")\n return (\n torch.from_numpy(negatives).to(device),\n torch.from_numpy(correction).to(device),\n )\n\n\ndef draw_training_negatives(\n model: base.SequentialTwoTower,\n sequence_vectors: torch.Tensor,\n flat_states: np.ndarray,\n flat_activity: np.ndarray,\n positive_items: torch.Tensor,\n known_history: torch.Tensor,\n liked_items: torch.Tensor,\n user_rows: np.ndarray,\n known_positive_packed: np.ndarray | None,\n prepared: SequenceData,\n config: Config,\n item_correction: torch.Tensor,\n generator: torch.Generator,\n device: torch.device,\n) -> tuple[torch.Tensor, torch.Tensor]:\n if config.negative_sampling == \"hard-mixed\":\n if known_positive_packed is None:\n raise RuntimeError(\"hard mining requires the full-history filter\")\n return draw_hard_mixed_negatives(\n model,\n sequence_vectors,\n flat_states,\n flat_activity,\n positive_items,\n user_rows,\n liked_items,\n known_positive_packed,\n prepared,\n config,\n generator,\n device,\n )\n if config.negative_filter_scope == \"full-training-history\":\n if known_positive_packed is None:\n raise RuntimeError(\"full-history negative filter was not built\")\n return draw_full_history_filtered_negatives(\n positive_items,\n user_rows,\n liked_items,\n known_positive_packed,\n len(prepared.item_ids),\n config,\n item_correction,\n generator,\n device,\n )\n return base.draw_filtered_mixed_negatives(\n positive_items,\n known_history,\n liked_items,\n len(prepared.item_ids),\n config.uniform_negatives,\n config.inbatch_negatives,\n item_correction,\n generator,\n device,\n )\n\n\ndef event_sequence(\n model: base.SequentialTwoTower,\n items: torch.Tensor,\n features: torch.Tensor,\n hidden: torch.Tensor,\n) -> tuple[torch.Tensor, torch.Tensor]:\n inputs = torch.cat([model.context_embedding(items), features], dim=-1)\n projected = model.event_projection(inputs)\n return model.sequence_encoder(projected, hidden)\n\n\ndef pool_explicit_states(\n model: base.SequentialTwoTower,\n prepared: SequenceData,\n state_ids: np.ndarray,\n device: torch.device,\n) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:\n unique_states, inverse = np.unique(state_ids, return_inverse=True)\n block[4].reshape(-1)[flat_scored].astype(np.int64)\n ).to(device)\n known_history = torch.from_numpy(\n block[6].reshape(-1, config.bptt_steps)[flat_scored].astype(\n np.int64\n )\n ).to(device)\n liked_items = torch.from_numpy(\n prepared.liked_states[flat_states].astype(np.int64)\n ).to(device)\n flat_user_rows = np.broadcast_to(\n users[:, None], block[5].shape\n ).reshape(-1)[flat_scored]\n flat_activity = block[2].reshape(-1, 4)[flat_scored].astype(np.float32)\n negatives, negative_weights = draw_training_negatives(\n model,\n sequence_vectors,\n flat_states,\n flat_activity,\n positive_items,\n known_history,\n liked_items,\n flat_user_rows,\n known_positive_packed,\n prepared,\n config,\n item_correction,\n generator,\n device,\n )\n optimizer.zero_grad(set_to_none=True)\n statistics, sequence_gradient = score_target_microbatches(\n model,\n prepared,\n config,\n sequence_vectors,\n flat_states,\n flat_activity,\n positive_items,\n negatives,\n negative_weights,\n block[8].reshape(-1)[flat_scored],\n device,\n backward=True,\n )\n if sequence_gradient is None:\n raise RuntimeError(\"training block omitted its sequence gradient\")\n sequence_vectors.backward(sequence_gradient)\n nn.utils.clip_grad_norm_(model.parameters(), config.gradient_clip)\n optimizer.step()\n\n count = len(positive_items)\n total_loss += statistics[\"loss_sum\"]\n total_targets += count\n total_weight += statistics[\"weight_sum\"]\n total_positive += statistics[\"positive_sum\"]\n positive_items = torch.from_numpy(\n block[4].reshape(-1)[flat_scored].astype(np.int64)\n ).to(device)\n known_history = torch.from_numpy(\n block[6].reshape(-1, config.bptt_steps)[flat_scored].astype(\n np.int64\n )\n ).to(device)\n liked_items = torch.from_numpy(\n prepared.liked_states[flat_states].astype(np.int64)\n ).to(device)\n flat_user_rows = np.broadcast_to(\n users[:, None], block[5].shape\n ).reshape(-1)[flat_scored]\n flat_activity = block[2].reshape(-1, 4)[flat_scored].astype(np.float32)\n negatives, negative_weights = draw_training_negatives(\n model,\n sequence_vectors,\n flat_states,\n flat_activity,\n positive_items,\n known_history,\n liked_items,\n flat_user_rows,\n known_positive_packed,\n prepared,\n config,\n item_correction,\n generator,\n device,\n )\n statistics, _ = score_target_microbatches(\n model,\n prepared,\n config,\n sequence_vectors,\n flat_states,\n flat_activity,\n positive_items,\n negatives,\n negative_weights,\n block[8].reshape(-1)[flat_scored],\n device,\n backward=False,\n )\n total_loss += statistics[\"loss_sum\"]\n total_targets += len(positive_items)\n total_weight += statistics[\"weight_sum\"]\n if group_number % 25 == 0:\n release_accelerator_cache(device)\n return {\n \"validation_loss\": total_loss / max(total_weight, 1.0e-12),\n \"validation_targets\": float(total_targets),\n \"validation_target_weight_sum\": total_weight,\n \"validation_seconds\": time.monotonic() - started,\n }\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "session_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "root_turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "response_id": "resp_08539f19231b9e9f016ab5abc3f36c87d288bd0b2c39143563", "usage": { "input_tokens": 71304, "cached_input_tokens": 70784, "cache_write_input_tokens": 0, "output_tokens": 114, "reasoning_output_tokens": 10, "total_tokens": 71418 }, "turn_token_usage": { "input_tokens": 2122653, "cached_input_tokens": 2055296, "cache_write_input_tokens": 0, "output_tokens": 13720, "reasoning_output_tokens": 5050, "total_tokens": 2136373 }, "thread_token_usage": { "input_tokens": 2122653, "cached_input_tokens": 2055296, "cache_write_input_tokens": 0, "output_tokens": 13720, "reasoning_output_tokens": 5050, "total_tokens": 2136373 } } ## Step 55 · agent · 2026-09-24T23:01:36.953Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T23:01:44.620Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/train_current_model.py\\n@@\\n parser.add_argument(\\n \\\"--negative-filter-scope\\\",\\n choices=(\\\"full-training-history\\\", \\\"last-100\\\"),\\n default=Config.negative_filter_scope,\\n )\\n+ parser.add_argument(\\n+ \\\"--negative-sampling\\\",\\n+ choices=(\\\"mixed\\\", \\\"hard-mixed\\\"),\\n+ default=Config.negative_sampling,\\n+ )\\n+ parser.add_argument(\\n+ \\\"--uniform-negatives\\\", type=int, default=Config.uniform_negatives\\n+ )\\n+ parser.add_argument(\\n+ \\\"--inbatch-negatives\\\", type=int, default=Config.inbatch_negatives\\n+ )\\n+ parser.add_argument(\\\"--hard-pool-size\\\", type=int, default=Config.hard_pool_size)\\n@@\\n or args.max_train_targets_per_user < 0\\n or args.max_validation_targets_per_user < 0\\n+ or args.uniform_negatives < 1\\n+ or args.inbatch_negatives < 1\\n+ or args.hard_pool_size <= args.inbatch_negatives\\n@@\\n loss_microbatch_size=args.loss_microbatch_size,\\n+ uniform_negatives=args.uniform_negatives,\\n+ inbatch_negatives=args.inbatch_negatives,\\n+ negative_sampling=args.negative_sampling,\\n+ hard_pool_size=args.hard_pool_size,\\n negative_filter_scope=args.negative_filter_scope,\\n@@\\n \\\"negative_sampling\\\": (\\n- \\\"24-uniform-plus-8-propensity-corrected-inbatch\\\"\\n+ f\\\"{config.uniform_negatives}-uniform-plus-\\\"\\n+ f\\\"{config.inbatch_negatives}-\\\"\\n+ f\\\"{config.negative_sampling}\\\"\\n ),\\n*** End Patch\"; text(await tools.apply_patch(patch));" } ### Tool output · 2026-09-24T23:01:44.699Z Script completed Wall time 0.0 seconds Output: {} ### Harness context { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-cc6437ea-93cf-47e2-8458-5372e68725d1", "changes": { "/app/solution/train_current_model.py": { "type": "update", "unified_diff": "@@ -2118,2 +2118,14 @@\n parser.add_argument(\n+ \"--negative-sampling\",\n+ choices=(\"mixed\", \"hard-mixed\"),\n+ default=Config.negative_sampling,\n+ )\n+ parser.add_argument(\n+ \"--uniform-negatives\", type=int, default=Config.uniform_negatives\n+ )\n+ parser.add_argument(\n+ \"--inbatch-negatives\", type=int, default=Config.inbatch_negatives\n+ )\n+ parser.add_argument(\"--hard-pool-size\", type=int, default=Config.hard_pool_size)\n+ parser.add_argument(\n \"--target-weighting\",\n@@ -2190,2 +2202,5 @@\n or args.max_validation_targets_per_user < 0\n+ or args.uniform_negatives < 1\n+ or args.inbatch_negatives < 1\n+ or args.hard_pool_size <= args.inbatch_negatives\n ):\n@@ -2203,2 +2218,6 @@\n loss_microbatch_size=args.loss_microbatch_size,\n+ uniform_negatives=args.uniform_negatives,\n+ inbatch_negatives=args.inbatch_negatives,\n+ negative_sampling=args.negative_sampling,\n+ hard_pool_size=args.hard_pool_size,\n negative_filter_scope=args.negative_filter_scope,\n@@ -2306,3 +2325,5 @@\n \"negative_sampling\": (\n- \"24-uniform-plus-8-propensity-corrected-inbatch\"\n+ f\"{config.uniform_negatives}-uniform-plus-\"\n+ f\"{config.inbatch_negatives}-\"\n+ f\"{config.negative_sampling}\"\n ),\n", "move_path": null } }, "status": "completed", "stdout": "Success. Updated the following files:\nM /app/solution/train_current_model.py\n", "stderr": "" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "session_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "root_turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "response_id": "resp_08539f19231b9e9f016ab5abcabc8087d2b9e22e81a0f93199", "usage": { "input_tokens": 72731, "cached_input_tokens": 71168, "cache_write_input_tokens": 0, "output_tokens": 520, "reasoning_output_tokens": 86, "total_tokens": 73251 }, "turn_token_usage": { "input_tokens": 2195384, "cached_input_tokens": 2126464, "cache_write_input_tokens": 0, "output_tokens": 14240, "reasoning_output_tokens": 5136, "total_tokens": 2209624 }, "thread_token_usage": { "input_tokens": 2195384, "cached_input_tokens": 2126464, "cache_write_input_tokens": 0, "output_tokens": 14240, "reasoning_output_tokens": 5136, "total_tokens": 2209624 } } ## Step 56 · agent · 2026-09-24T23:01:50.372Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T23:01:56.029Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/train_current_model.py\\n@@\\n stored_window_values = (\\n@@\\n current_window_values = (\\n@@\\n config.max_validation_targets_per_user,\\n )\\n+ stored_negative_values = (\\n+ stored_config.get(\\\"negative_sampling\\\", \\\"mixed\\\"),\\n+ stored_config.get(\\\"uniform_negatives\\\", 24),\\n+ stored_config.get(\\\"inbatch_negatives\\\", 8),\\n+ stored_config.get(\\\"hard_pool_size\\\", 512),\\n+ )\\n+ current_negative_values = (\\n+ config.negative_sampling,\\n+ config.uniform_negatives,\\n+ config.inbatch_negatives,\\n+ config.hard_pool_size,\\n+ )\\n objective_changed = (\\n stored_filter_scope != config.negative_filter_scope\\n or stored_target_weighting != config.target_weighting\\n or stored_weight_values != current_weight_values\\n or stored_window_values != current_window_values\\n+ or stored_negative_values != current_negative_values\\n )\\n@@\\n f\\\"target window {stored_window_values}->{current_window_values}; \\\"\\n+ f\\\"negative sampler {stored_negative_values}->\\\"\\n+ f\\\"{current_negative_values}; \\\"\\n \\\"recomputing the resume checkpoint's validation BCE\\\"\\n*** End Patch\"; text(await tools.apply_patch(patch));" } ### Tool output · 2026-09-24T23:01:56.104Z Script completed Wall time 0.0 seconds Output: {} ### Harness context { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-c186526a-0048-405b-9ef7-303567fa8327", "changes": { "/app/solution/train_current_model.py": { "type": "update", "unified_diff": "@@ -1797,2 +1797,14 @@\n )\n+ stored_negative_values = (\n+ stored_config.get(\"negative_sampling\", \"mixed\"),\n+ stored_config.get(\"uniform_negatives\", 24),\n+ stored_config.get(\"inbatch_negatives\", 8),\n+ stored_config.get(\"hard_pool_size\", 512),\n+ )\n+ current_negative_values = (\n+ config.negative_sampling,\n+ config.uniform_negatives,\n+ config.inbatch_negatives,\n+ config.hard_pool_size,\n+ )\n objective_changed = (\n@@ -1802,2 +1814,3 @@\n or stored_window_values != current_window_values\n+ or stored_negative_values != current_negative_values\n )\n@@ -1833,2 +1846,4 @@\n f\"target window {stored_window_values}->{current_window_values}; \"\n+ f\"negative sampler {stored_negative_values}->\"\n+ f\"{current_negative_values}; \"\n \"recomputing the resume checkpoint's validation BCE\"\n", "move_path": null } }, "status": "completed", "stdout": "Success. Updated the following files:\nM /app/solution/train_current_model.py\n", "stderr": "" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "session_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "root_turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "response_id": "resp_08539f19231b9e9f016ab5abd911b087d2ada503bed2bfc9ea", "usage": { "input_tokens": 73275, "cached_input_tokens": 72576, "cache_write_input_tokens": 0, "output_tokens": 337, "reasoning_output_tokens": 14, "total_tokens": 73612 }, "turn_token_usage": { "input_tokens": 2268659, "cached_input_tokens": 2199040, "cache_write_input_tokens": 0, "output_tokens": 14577, "reasoning_output_tokens": 5150, "total_tokens": 2283236 }, "thread_token_usage": { "input_tokens": 2268659, "cached_input_tokens": 2199040, "cache_write_input_tokens": 0, "output_tokens": 14577, "reasoning_output_tokens": 5150, "total_tokens": 2283236 } } ## Step 57 · agent · 2026-09-24T23:02:02.313Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T23:02:05.102Z { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python -m py_compile /app/solution/train_current_model.py /app/solution/two_tower_model.py /app/solution/analyze_scores.py\\n/opt/task-venv/bin/python /app/solution/train_current_model.py --help | sed -n '1,100p'\\ngit diff --no-index --stat /task/current_model/train_current_model.py /app/solution/train_current_model.py || true\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":10000}); text(r.output);" } ### Tool output · 2026-09-24T23:02:09.277Z Script completed Wall time 4.2 seconds Output: usage: train_current_model.py [-h] [--stage {validation,submission}] --output OUTPUT --public-dir PUBLIC_DIR [--prepared-cache PREPARED_CACHE] [--device {cpu,mps}] [--threads THREADS] [--bptt-steps BPTT_STEPS] [--user-batch-size USER_BATCH_SIZE] [--epochs EPOCHS] [--learning-rate LEARNING_RATE] [--loss-microbatch-size LOSS_MICROBATCH_SIZE] [--negative-filter-scope {full-training-history,last-100}] [--negative-sampling {mixed,hard-mixed}] [--uniform-negatives UNIFORM_NEGATIVES] [--inbatch-negatives INBATCH_NEGATIVES] [--hard-pool-size HARD_POOL_SIZE] [--target-weighting {discovery-graded,organic-strong,uniform}] [--ordinary-target-weight ORDINARY_TARGET_WEIGHT] [--organic-strong-target-weight ORGANIC_STRONG_TARGET_WEIGHT] [--organic-like-target-weight ORGANIC_LIKE_TARGET_WEIGHT] [--discovery-horizon-days DISCOVERY_HORIZON_DAYS] [--validation-fraction VALIDATION_FRACTION] [--max-train-targets-per-user MAX_TRAIN_TARGETS_PER_USER] [--max-validation-targets-per-user MAX_VALIDATION_TARGETS_PER_USER] [--early-stopping-patience EARLY_STOPPING_PATIENCE] [--early-stopping-min-delta EARLY_STOPPING_MIN_DELTA] [--checkpoint-dir CHECKPOINT_DIR] [--resume-checkpoint RESUME_CHECKPOINT] [--seed SEED] [--ranking-batch-size RANKING_BATCH_SIZE] [--prepare-only] Train the Yambda windowed two-tower baseline. Every catalog listen with at least 50 percent completion is a sequence token. Within each selected chronological window, every adjacent target contributes a loss. The fast default retains each user's most recent 128 training targets and 16 held-out validation targets; passing zero for either cap restores the uncapped behavior for that side of the split. A 100-step truncated-BPTT block computes all of its timestep losses and carries the detached GRU hidden state into the next block. The model is a pure two-tower retriever: no user-ID embedding, no output normalization, raw dot-product retrieval, sample-weighted binary cross entropy, and 24 uniform plus eight propensity-corrected unique in-batch negatives. Every selected adjacent target retains a nonzero weight. Only files under the supplied public directory are read. options: -h, --help show this help message and exit --stage {validation,submission} --output OUTPUT --public-dir PUBLIC_DIR --prepared-cache PREPARED_CACHE --device {cpu,mps} --threads THREADS --bptt-steps BPTT_STEPS --user-batch-size USER_BATCH_SIZE --epochs EPOCHS --learning-rate LEARNING_RATE --loss-microbatch-size LOSS_MICROBATCH_SIZE --negative-filter-scope {full-training-history,last-100} --negative-sampling {mixed,hard-mixed} --uniform-negatives UNIFORM_NEGATIVES --inbatch-negatives INBATCH_NEGATIVES --hard-pool-size HARD_POOL_SIZE --target-weighting {discovery-graded,organic-strong,uniform} --ordinary-target-weight ORDINARY_TARGET_WEIGHT --organic-strong-target-weight ORGANIC_STRONG_TARGET_WEIGHT --organic-like-target-weight ORGANIC_LIKE_TARGET_WEIGHT --discovery-horizon-days DISCOVERY_HORIZON_DAYS --validation-fraction VALIDATION_FRACTION --max-train-targets-per-user MAX_TRAIN_TARGETS_PER_USER retain this many recent pre-validation targets per user; 0 is uncapped --max-validation-targets-per-user MAX_VALIDATION_TARGETS_PER_USER retain this many final validation targets per user; 0 is uncapped --early-stopping-patience EARLY_STOPPING_PATIENCE --early-stopping-min-delta EARLY_STOPPING_MIN_DELTA --checkpoint-dir CHECKPOINT_DIR --resume-checkpoint RESUME_CHECKPOINT --seed SEED --ranking-batch-size RANKING_BATCH_SIZE --prepare-only .../solution}/train_current_model.py | 212 ++++++++++++++++++++- 1 file changed, 209 insertions(+), 3 deletions(-) ### Harness context { "type": "token_usage_record", "thread_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "session_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "root_turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "response_id": "resp_08539f19231b9e9f016ab5abe48ca087d2a81e211c5fdd3013", "usage": { "input_tokens": 73636, "cached_input_tokens": 73088, "cache_write_input_tokens": 0, "output_tokens": 149, "reasoning_output_tokens": 9, "total_tokens": 73785 }, "turn_token_usage": { "input_tokens": 2342295, "cached_input_tokens": 2272128, "cache_write_input_tokens": 0, "output_tokens": 14726, "reasoning_output_tokens": 5159, "total_tokens": 2357021 }, "thread_token_usage": { "input_tokens": 2342295, "cached_input_tokens": 2272128, "cache_write_input_tokens": 0, "output_tokens": 14726, "reasoning_output_tokens": 5159, "total_tokens": 2357021 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-d68b22f2-0d36-48b1-88e7-ff454afc52e5", "process_id": "47441", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python -m py_compile /app/solution/train_current_model.py /app/solution/two_tower_model.py /app/solution/analyze_scores.py\n/opt/task-venv/bin/python /app/solution/train_current_model.py --help | sed -n '1,100p'\ngit diff --no-index --stat /task/current_model/train_current_model.py /app/solution/train_current_model.py || true" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python -m py_compile /app/solution/train_current_model.py /app/solution/two_tower_model.py /app/solution/analyze_scores.py\n/opt/task-venv/bin/python /app/solution/train_current_model.py --help | sed -n '1,100p'\ngit diff --no-index --stat /task/current_model/train_current_model.py /app/solution/train_current_model.py || true" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "usage: train_current_model.py [-h] [--stage {validation,submission}] --output\n OUTPUT --public-dir PUBLIC_DIR\n [--prepared-cache PREPARED_CACHE]\n [--device {cpu,mps}] [--threads THREADS]\n [--bptt-steps BPTT_STEPS]\n [--user-batch-size USER_BATCH_SIZE]\n [--epochs EPOCHS]\n [--learning-rate LEARNING_RATE]\n [--loss-microbatch-size LOSS_MICROBATCH_SIZE]\n [--negative-filter-scope {full-training-history,last-100}]\n [--negative-sampling {mixed,hard-mixed}]\n [--uniform-negatives UNIFORM_NEGATIVES]\n [--inbatch-negatives INBATCH_NEGATIVES]\n [--hard-pool-size HARD_POOL_SIZE]\n [--target-weighting {discovery-graded,organic-strong,uniform}]\n [--ordinary-target-weight ORDINARY_TARGET_WEIGHT]\n [--organic-strong-target-weight ORGANIC_STRONG_TARGET_WEIGHT]\n [--organic-like-target-weight ORGANIC_LIKE_TARGET_WEIGHT]\n [--discovery-horizon-days DISCOVERY_HORIZON_DAYS]\n [--validation-fraction VALIDATION_FRACTION]\n [--max-train-targets-per-user MAX_TRAIN_TARGETS_PER_USER]\n [--max-validation-targets-per-user MAX_VALIDATION_TARGETS_PER_USER]\n [--early-stopping-patience EARLY_STOPPING_PATIENCE]\n [--early-stopping-min-delta EARLY_STOPPING_MIN_DELTA]\n [--checkpoint-dir CHECKPOINT_DIR]\n [--resume-checkpoint RESUME_CHECKPOINT]\n [--seed SEED]\n [--ranking-batch-size RANKING_BATCH_SIZE]\n [--prepare-only]\n\nTrain the Yambda windowed two-tower baseline. Every catalog listen with at\nleast 50 percent completion is a sequence token. Within each selected\nchronological window, every adjacent target contributes a loss. The fast\ndefault retains each user's most recent 128 training targets and 16 held-out\nvalidation targets; passing zero for either cap restores the uncapped behavior\nfor that side of the split. A 100-step truncated-BPTT block computes all of\nits timestep losses and carries the detached GRU hidden state into the next\nblock. The model is a pure two-tower retriever: no user-ID embedding, no\noutput normalization, raw dot-product retrieval, sample-weighted binary cross\nentropy, and 24 uniform plus eight propensity-corrected unique in-batch\nnegatives. Every selected adjacent target retains a nonzero weight. Only files\nunder the supplied public directory are read.\n\noptions:\n -h, --help show this help message and exit\n --stage {validation,submission}\n --output OUTPUT\n --public-dir PUBLIC_DIR\n --prepared-cache PREPARED_CACHE\n --device {cpu,mps}\n --threads THREADS\n --bptt-steps BPTT_STEPS\n --user-batch-size USER_BATCH_SIZE\n --epochs EPOCHS\n --learning-rate LEARNING_RATE\n --loss-microbatch-size LOSS_MICROBATCH_SIZE\n --negative-filter-scope {full-training-history,last-100}\n --negative-sampling {mixed,hard-mixed}\n --uniform-negatives UNIFORM_NEGATIVES\n --inbatch-negatives INBATCH_NEGATIVES\n --hard-pool-size HARD_POOL_SIZE\n --target-weighting {discovery-graded,organic-strong,uniform}\n --ordinary-target-weight ORDINARY_TARGET_WEIGHT\n --organic-strong-target-weight ORGANIC_STRONG_TARGET_WEIGHT\n --organic-like-target-weight ORGANIC_LIKE_TARGET_WEIGHT\n --discovery-horizon-days DISCOVERY_HORIZON_DAYS\n --validation-fraction VALIDATION_FRACTION\n --max-train-targets-per-user MAX_TRAIN_TARGETS_PER_USER\n retain this many recent pre-validation targets per\n user; 0 is uncapped\n --max-validation-targets-per-user MAX_VALIDATION_TARGETS_PER_USER\n retain this many final validation targets per user; 0\n is uncapped\n --early-stopping-patience EARLY_STOPPING_PATIENCE\n --early-stopping-min-delta EARLY_STOPPING_MIN_DELTA\n --checkpoint-dir CHECKPOINT_DIR\n --resume-checkpoint RESUME_CHECKPOINT\n --seed SEED\n --ranking-batch-size RANKING_BATCH_SIZE\n --prepare-only\n .../solution}/train_current_model.py | 212 ++++++++++++++++++++-\n 1 file changed, 209 insertions(+), 3 deletions(-)\n", "stderr": "", "aggregated_output": "usage: train_current_model.py [-h] [--stage {validation,submission}] --output\n OUTPUT --public-dir PUBLIC_DIR\n [--prepared-cache PREPARED_CACHE]\n [--device {cpu,mps}] [--threads THREADS]\n [--bptt-steps BPTT_STEPS]\n [--user-batch-size USER_BATCH_SIZE]\n [--epochs EPOCHS]\n [--learning-rate LEARNING_RATE]\n [--loss-microbatch-size LOSS_MICROBATCH_SIZE]\n [--negative-filter-scope {full-training-history,last-100}]\n [--negative-sampling {mixed,hard-mixed}]\n [--uniform-negatives UNIFORM_NEGATIVES]\n [--inbatch-negatives INBATCH_NEGATIVES]\n [--hard-pool-size HARD_POOL_SIZE]\n [--target-weighting {discovery-graded,organic-strong,uniform}]\n [--ordinary-target-weight ORDINARY_TARGET_WEIGHT]\n [--organic-strong-target-weight ORGANIC_STRONG_TARGET_WEIGHT]\n [--organic-like-target-weight ORGANIC_LIKE_TARGET_WEIGHT]\n [--discovery-horizon-days DISCOVERY_HORIZON_DAYS]\n [--validation-fraction VALIDATION_FRACTION]\n [--max-train-targets-per-user MAX_TRAIN_TARGETS_PER_USER]\n [--max-validation-targets-per-user MAX_VALIDATION_TARGETS_PER_USER]\n [--early-stopping-patience EARLY_STOPPING_PATIENCE]\n [--early-stopping-min-delta EARLY_STOPPING_MIN_DELTA]\n [--checkpoint-dir CHECKPOINT_DIR]\n [--resume-checkpoint RESUME_CHECKPOINT]\n [--seed SEED]\n [--ranking-batch-size RANKING_BATCH_SIZE]\n [--prepare-only]\n\nTrain the Yambda windowed two-tower baseline. Every catalog listen with at\nleast 50 percent completion is a sequence token. Within each selected\nchronological window, every adjacent target contributes a loss. The fast\ndefault retains each user's most recent 128 training targets and 16 held-out\nvalidation targets; passing zero for either cap restores the uncapped behavior\nfor that side of the split. A 100-step truncated-BPTT block computes all of\nits timestep losses and carries the detached GRU hidden state into the next\nblock. The model is a pure two-tower retriever: no user-ID embedding, no\noutput normalization, raw dot-product retrieval, sample-weighted binary cross\nentropy, and 24 uniform plus eight propensity-corrected unique in-batch\nnegatives. Every selected adjacent target retains a nonzero weight. Only files\nunder the supplied public directory are read.\n\noptions:\n -h, --help show this help message and exit\n --stage {validation,submission}\n --output OUTPUT\n --public-dir PUBLIC_DIR\n --prepared-cache PREPARED_CACHE\n --device {cpu,mps}\n --threads THREADS\n --bptt-steps BPTT_STEPS\n --user-batch-size USER_BATCH_SIZE\n --epochs EPOCHS\n --learning-rate LEARNING_RATE\n --loss-microbatch-size LOSS_MICROBATCH_SIZE\n --negative-filter-scope {full-training-history,last-100}\n --negative-sampling {mixed,hard-mixed}\n --uniform-negatives UNIFORM_NEGATIVES\n --inbatch-negatives INBATCH_NEGATIVES\n --hard-pool-size HARD_POOL_SIZE\n --target-weighting {discovery-graded,organic-strong,uniform}\n --ordinary-target-weight ORDINARY_TARGET_WEIGHT\n --organic-strong-target-weight ORGANIC_STRONG_TARGET_WEIGHT\n --organic-like-target-weight ORGANIC_LIKE_TARGET_WEIGHT\n --discovery-horizon-days DISCOVERY_HORIZON_DAYS\n --validation-fraction VALIDATION_FRACTION\n --max-train-targets-per-user MAX_TRAIN_TARGETS_PER_USER\n retain this many recent pre-validation targets per\n user; 0 is uncapped\n --max-validation-targets-per-user MAX_VALIDATION_TARGETS_PER_USER\n retain this many final validation targets per user; 0\n is uncapped\n --early-stopping-patience EARLY_STOPPING_PATIENCE\n --early-stopping-min-delta EARLY_STOPPING_MIN_DELTA\n --checkpoint-dir CHECKPOINT_DIR\n --resume-checkpoint RESUME_CHECKPOINT\n --seed SEED\n --ranking-batch-size RANKING_BATCH_SIZE\n --prepare-only\n .../solution}/train_current_model.py | 212 ++++++++++++++++++++-\n 1 file changed, 209 insertions(+), 3 deletions(-)\n", "exit_code": 0, "duration": { "secs": 4, "nanos": 3902069 }, "formatted_output": "usage: train_current_model.py [-h] [--stage {validation,submission}] --output\n OUTPUT --public-dir PUBLIC_DIR\n [--prepared-cache PREPARED_CACHE]\n [--device {cpu,mps}] [--threads THREADS]\n [--bptt-steps BPTT_STEPS]\n [--user-batch-size USER_BATCH_SIZE]\n [--epochs EPOCHS]\n [--learning-rate LEARNING_RATE]\n [--loss-microbatch-size LOSS_MICROBATCH_SIZE]\n [--negative-filter-scope {full-training-history,last-100}]\n [--negative-sampling {mixed,hard-mixed}]\n [--uniform-negatives UNIFORM_NEGATIVES]\n [--inbatch-negatives INBATCH_NEGATIVES]\n [--hard-pool-size HARD_POOL_SIZE]\n [--target-weighting {discovery-graded,organic-strong,uniform}]\n [--ordinary-target-weight ORDINARY_TARGET_WEIGHT]\n [--organic-strong-target-weight ORGANIC_STRONG_TARGET_WEIGHT]\n [--organic-like-target-weight ORGANIC_LIKE_TARGET_WEIGHT]\n [--discovery-horizon-days DISCOVERY_HORIZON_DAYS]\n [--validation-fraction VALIDATION_FRACTION]\n [--max-train-targets-per-user MAX_TRAIN_TARGETS_PER_USER]\n [--max-validation-targets-per-user MAX_VALIDATION_TARGETS_PER_USER]\n [--early-stopping-patience EARLY_STOPPING_PATIENCE]\n [--early-stopping-min-delta EARLY_STOPPING_MIN_DELTA]\n [--checkpoint-dir CHECKPOINT_DIR]\n [--resume-checkpoint RESUME_CHECKPOINT]\n [--seed SEED]\n [--ranking-batch-size RANKING_BATCH_SIZE]\n [--prepare-only]\n\nTrain the Yambda windowed two-tower baseline. Every catalog listen with at\nleast 50 percent completion is a sequence token. Within each selected\nchronological window, every adjacent target contributes a loss. The fast\ndefault retains each user's most recent 128 training targets and 16 held-out\nvalidation targets; passing zero for either cap restores the uncapped behavior\nfor that side of the split. A 100-step truncated-BPTT block computes all of\nits timestep losses and carries the detached GRU hidden state into the next\nblock. The model is a pure two-tower retriever: no user-ID embedding, no\noutput normalization, raw dot-product retrieval, sample-weighted binary cross\nentropy, and 24 uniform plus eight propensity-corrected unique in-batch\nnegatives. Every selected adjacent target retains a nonzero weight. Only files\nunder the supplied public directory are read.\n\noptions:\n -h, --help show this help message and exit\n --stage {validation,submission}\n --output OUTPUT\n --public-dir PUBLIC_DIR\n --prepared-cache PREPARED_CACHE\n --device {cpu,mps}\n --threads THREADS\n --bptt-steps BPTT_STEPS\n --user-batch-size USER_BATCH_SIZE\n --epochs EPOCHS\n --learning-rate LEARNING_RATE\n --loss-microbatch-size LOSS_MICROBATCH_SIZE\n --negative-filter-scope {full-training-history,last-100}\n --negative-sampling {mixed,hard-mixed}\n --uniform-negatives UNIFORM_NEGATIVES\n --inbatch-negatives INBATCH_NEGATIVES\n --hard-pool-size HARD_POOL_SIZE\n --target-weighting {discovery-graded,organic-strong,uniform}\n --ordinary-target-weight ORDINARY_TARGET_WEIGHT\n --organic-strong-target-weight ORGANIC_STRONG_TARGET_WEIGHT\n --organic-like-target-weight ORGANIC_LIKE_TARGET_WEIGHT\n --discovery-horizon-days DISCOVERY_HORIZON_DAYS\n --validation-fraction VALIDATION_FRACTION\n --max-train-targets-per-user MAX_TRAIN_TARGETS_PER_USER\n retain this many recent pre-validation targets per\n user; 0 is uncapped\n --max-validation-targets-per-user MAX_VALIDATION_TARGETS_PER_USER\n retain this many final validation targets per user; 0\n is uncapped\n --early-stopping-patience EARLY_STOPPING_PATIENCE\n --early-stopping-min-delta EARLY_STOPPING_MIN_DELTA\n --checkpoint-dir CHECKPOINT_DIR\n --resume-checkpoint RESUME_CHECKPOINT\n --seed SEED\n --ranking-batch-size RANKING_BATCH_SIZE\n --prepare-only\n .../solution}/train_current_model.py | 212 ++++++++++++++++++++-\n 1 file changed, 209 insertions(+), 3 deletions(-)\n" } } ## Step 58 · agent · 2026-09-24T23:02:18.723Z ### Thinking unavailable — no text recorded Score calibration confirms the diagnosis: a mild item-norm correction improves NDCG to `0.021565`, while full cosine collapses to `0.012964`. That points to over-rewarded easy/catalog directions, but also says popularity magnitude still carries useful signal. I’ve added bounded online mining that keeps eight uniform negatives for coverage and selects 24 query-confusable negatives from a 512-item mixed pool, with the existing full-history false-negative filter preserved. I’m testing it as a conservative low-rate fine-tune from the production checkpoint. ### exec · 2026-09-24T23:02:24.033Z { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python /app/solution/train_current_model.py --stage validation --public-dir /task --prepared-cache /app/solution/validation_sequences.npz --negative-sampling hard-mixed --uniform-negatives 8 --inbatch-negatives 24 --hard-pool-size 512 --epochs 2 --learning-rate 0.0003 --resume-checkpoint /app/solution/baseline_checkpoints/epoch-01.pt --checkpoint-dir /app/solution/hard_ft_checkpoints --output /app/solution/hard_ft_validation.parquet 2>&1 | tee /app/solution/hard_ft_validation.log\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":12000}); text(JSON.stringify(r));" } ### Tool output · 2026-09-24T23:02:25.199Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"6c8c45","wall_time_seconds":1.000926608,"session_id":59085,"original_token_count":0,"output":""} ### Harness context { "type": "token_usage_record", "thread_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "session_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "root_turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "response_id": "resp_08539f19231b9e9f016ab5abf1807487d2adb8bddf834e0fdd", "usage": { "input_tokens": 74794, "cached_input_tokens": 73472, "cache_write_input_tokens": 0, "output_tokens": 562, "reasoning_output_tokens": 263, "total_tokens": 75356 }, "turn_token_usage": { "input_tokens": 2417089, "cached_input_tokens": 2345600, "cache_write_input_tokens": 0, "output_tokens": 15288, "reasoning_output_tokens": 5422, "total_tokens": 2432377 }, "thread_token_usage": { "input_tokens": 2417089, "cached_input_tokens": 2345600, "cache_write_input_tokens": 0, "output_tokens": 15288, "reasoning_output_tokens": 5422, "total_tokens": 2432377 } } ## Step 59 · agent · 2026-09-24T23:02:30.867Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T23:02:31.556Z { "code": "const r = await tools.write_stdin({\"session_id\":59085,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":12000}); text(r.output);\n" } ### Tool output · 2026-09-24T23:03:01.571Z Script completed Wall time 30.0 seconds Output: loaded full-sequence cache: tokens=20,056,329 targets=20,047,194 chronological BCE window: train=1,052,953 validation=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194 inbatch correction: min=0.250 mean_on_targets=1.001 max=4.000 target weighting=organic-strong min=0.250 mean=0.575 max=1.000 full-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250 using a fresh AdamW optimizer after loading /app/solution/baseline_checkpoints/epoch-01.pt BCE objective changed: filter full-training-history->full-training-history, target weighting organic-strong->organic-strong, target window (0.1, 128, 16)->(0.1, 128, 16); negative sampler ('mixed', 24, 8, 512)->('hard-mixed', 8, 24, 512); recomputing the resume checkpoint's validation BCE resumed validation-BCE training: epoch=1 loss=1.254848 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "session_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "root_turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "response_id": "resp_08539f19231b9e9f016ab5ac017ef487d2a3631f183a6544a0", "usage": { "input_tokens": 75415, "cached_input_tokens": 74624, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 75468 }, "turn_token_usage": { "input_tokens": 2492504, "cached_input_tokens": 2420224, "cache_write_input_tokens": 0, "output_tokens": 15341, "reasoning_output_tokens": 5428, "total_tokens": 2507845 }, "thread_token_usage": { "input_tokens": 2492504, "cached_input_tokens": 2420224, "cache_write_input_tokens": 0, "output_tokens": 15341, "reasoning_output_tokens": 5428, "total_tokens": 2507845 } } ## Step 60 · agent · 2026-09-24T23:03:08.546Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T23:03:09.246Z { "code": "const r = await tools.write_stdin({\"session_id\":59085,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":12000}); text(r.output);\n" } ### Tool output · 2026-09-24T23:03:39.290Z Script completed Wall time 30.0 seconds Output: ### Harness context { "type": "token_usage_record", "thread_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "session_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "root_turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "response_id": "resp_08539f19231b9e9f016ab5ac26019487d2b1ca9dab9832e3fe", "usage": { "input_tokens": 75751, "cached_input_tokens": 75264, "cache_write_input_tokens": 0, "output_tokens": 105, "reasoning_output_tokens": 58, "total_tokens": 75856 }, "turn_token_usage": { "input_tokens": 2568255, "cached_input_tokens": 2495488, "cache_write_input_tokens": 0, "output_tokens": 15446, "reasoning_output_tokens": 5486, "total_tokens": 2583701 }, "thread_token_usage": { "input_tokens": 2568255, "cached_input_tokens": 2495488, "cache_write_input_tokens": 0, "output_tokens": 15446, "reasoning_output_tokens": 5486, "total_tokens": 2583701 } } ## Step 61 · agent · 2026-09-24T23:03:48.250Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T23:03:48.251Z { "code": "const r = await tools.write_stdin({\"session_id\":59085,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":12000}); text(r.output);\n" } ### Tool output · 2026-09-24T23:04:18.258Z Script completed Wall time 30.0 seconds Output: ### Harness context { "type": "token_usage_record", "thread_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "session_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "root_turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "response_id": "resp_08539f19231b9e9f016ab5ac4b908c87d28f9cc37ce290b0b5", "usage": { "input_tokens": 75879, "cached_input_tokens": 75520, "cache_write_input_tokens": 0, "output_tokens": 57, "reasoning_output_tokens": 10, "total_tokens": 75936 }, "turn_token_usage": { "input_tokens": 2644134, "cached_input_tokens": 2571008, "cache_write_input_tokens": 0, "output_tokens": 15503, "reasoning_output_tokens": 5496, "total_tokens": 2659637 }, "thread_token_usage": { "input_tokens": 2644134, "cached_input_tokens": 2571008, "cache_write_input_tokens": 0, "output_tokens": 15503, "reasoning_output_tokens": 5496, "total_tokens": 2659637 } } ## Step 62 · agent · 2026-09-24T23:04:22.456Z ### Thinking unavailable — no text recorded The miner is behaving as intended: on the saved production model, its held-out negatives raise BCE from roughly `0.50` to `1.25`, meaning it is finding genuinely confusable candidates rather than relabeling the same easy catalog draws. Training is in progress at a reduced learning rate to avoid erasing the useful popularity signal. ## Step 63 · agent · 2026-09-24T23:04:25.185Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T23:04:26.108Z { "code": "const r = await tools.write_stdin({\"session_id\":59085,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":12000}); text(r.output);\n" } ### Tool output · 2026-09-24T23:04:56.118Z Script completed Wall time 30.0 seconds Output: ### Harness context { "type": "token_usage_record", "thread_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "session_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "root_turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "response_id": "resp_08539f19231b9e9f016ab5ac727f5c87d2b5f152c6e0260551", "usage": { "input_tokens": 75959, "cached_input_tokens": 75648, "cache_write_input_tokens": 0, "output_tokens": 159, "reasoning_output_tokens": 36, "total_tokens": 76118 }, "turn_token_usage": { "input_tokens": 2720093, "cached_input_tokens": 2646656, "cache_write_input_tokens": 0, "output_tokens": 15662, "reasoning_output_tokens": 5532, "total_tokens": 2735755 }, "thread_token_usage": { "input_tokens": 2720093, "cached_input_tokens": 2646656, "cache_write_input_tokens": 0, "output_tokens": 15662, "reasoning_output_tokens": 5532, "total_tokens": 2735755 } } ## Step 64 · agent · 2026-09-24T23:05:06.565Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T23:05:06.962Z { "code": "const r = await tools.write_stdin({\"session_id\":59085,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":12000}); text(r.output);\n" } ### Tool output · 2026-09-24T23:05:37.003Z Script completed Wall time 30.0 seconds Output: ### Harness context { "type": "token_usage_record", "thread_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "session_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "root_turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "response_id": "resp_08539f19231b9e9f016ab5ac986c1087d2bfe6b3cf2d95d46b", "usage": { "input_tokens": 76141, "cached_input_tokens": 75776, "cache_write_input_tokens": 0, "output_tokens": 387, "reasoning_output_tokens": 340, "total_tokens": 76528 }, "turn_token_usage": { "input_tokens": 2796234, "cached_input_tokens": 2722432, "cache_write_input_tokens": 0, "output_tokens": 16049, "reasoning_output_tokens": 5872, "total_tokens": 2812283 }, "thread_token_usage": { "input_tokens": 2796234, "cached_input_tokens": 2722432, "cache_write_input_tokens": 0, "output_tokens": 16049, "reasoning_output_tokens": 5872, "total_tokens": 2812283 } } ## Step 65 · agent · 2026-09-24T23:05:42.347Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T23:05:42.852Z { "code": "const r = await tools.write_stdin({\"session_id\":59085,\"chars\":\"\\u0003\",\"yield_time_ms\":1000,\"max_output_tokens\":5000}); text(r.output);\n" } ### Tool output · 2026-09-24T23:05:43.583Z Script completed Wall time 0.7 seconds Output: ### Harness context { "type": "token_usage_record", "thread_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "session_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "root_turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "response_id": "resp_08539f19231b9e9f016ab5acc4445c87d29c4c25954ac85c0a", "usage": { "input_tokens": 76551, "cached_input_tokens": 75904, "cache_write_input_tokens": 0, "output_tokens": 83, "reasoning_output_tokens": 31, "total_tokens": 76634 }, "turn_token_usage": { "input_tokens": 2872785, "cached_input_tokens": 2798336, "cache_write_input_tokens": 0, "output_tokens": 16132, "reasoning_output_tokens": 5903, "total_tokens": 2888917 }, "thread_token_usage": { "input_tokens": 2872785, "cached_input_tokens": 2798336, "cache_write_input_tokens": 0, "output_tokens": 16132, "reasoning_output_tokens": 5903, "total_tokens": 2888917 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-b0617e29-202e-4770-a8c9-75fd42a044c3", "process_id": "59085", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python /app/solution/train_current_model.py --stage validation --public-dir /task --prepared-cache /app/solution/validation_sequences.npz --negative-sampling hard-mixed --uniform-negatives 8 --inbatch-negatives 24 --hard-pool-size 512 --epochs 2 --learning-rate 0.0003 --resume-checkpoint /app/solution/baseline_checkpoints/epoch-01.pt --checkpoint-dir /app/solution/hard_ft_checkpoints --output /app/solution/hard_ft_validation.parquet 2>&1 | tee /app/solution/hard_ft_validation.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python /app/solution/train_current_model.py --stage validation --public-dir /task --prepared-cache /app/solution/validation_sequences.npz --negative-sampling hard-mixed --uniform-negatives 8 --inbatch-negatives 24 --hard-pool-size 512 --epochs 2 --learning-rate 0.0003 --resume-checkpoint /app/solution/baseline_checkpoints/epoch-01.pt --checkpoint-dir /app/solution/hard_ft_checkpoints --output /app/solution/hard_ft_validation.parquet 2>&1 | tee /app/solution/hard_ft_validation.log" } ], "source": "unified_exec_startup", "status": "failed", "stdout": "loaded full-sequence cache: tokens=20,056,329 targets=20,047,194\nchronological BCE window: train=1,052,953 validation=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194\ninbatch correction: min=0.250 mean_on_targets=1.001 max=4.000\ntarget weighting=organic-strong min=0.250 mean=0.575 max=1.000\nfull-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250\nusing a fresh AdamW optimizer after loading /app/solution/baseline_checkpoints/epoch-01.pt\nBCE objective changed: filter full-training-history->full-training-history, target weighting organic-strong->organic-strong, target window (0.1, 128, 16)->(0.1, 128, 16); negative sampler ('mixed', 24, 8, 512)->('hard-mixed', 8, 24, 512); recomputing the resume checkpoint's validation BCE\nresumed validation-BCE training: epoch=1 loss=1.254848\n", "stderr": "", "aggregated_output": "loaded full-sequence cache: tokens=20,056,329 targets=20,047,194\nchronological BCE window: train=1,052,953 validation=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194\ninbatch correction: min=0.250 mean_on_targets=1.001 max=4.000\ntarget weighting=organic-strong min=0.250 mean=0.575 max=1.000\nfull-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250\nusing a fresh AdamW optimizer after loading /app/solution/baseline_checkpoints/epoch-01.pt\nBCE objective changed: filter full-training-history->full-training-history, target weighting organic-strong->organic-strong, target window (0.1, 128, 16)->(0.1, 128, 16); negative sampler ('mixed', 24, 8, 512)->('hard-mixed', 8, 24, 512); recomputing the resume checkpoint's validation BCE\nresumed validation-BCE training: epoch=1 loss=1.254848\n", "exit_code": 130, "duration": { "secs": 199, "nanos": 382802490 }, "formatted_output": "loaded full-sequence cache: tokens=20,056,329 targets=20,047,194\nchronological BCE window: train=1,052,953 validation=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194\ninbatch correction: min=0.250 mean_on_targets=1.001 max=4.000\ntarget weighting=organic-strong min=0.250 mean=0.575 max=1.000\nfull-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250\nusing a fresh AdamW optimizer after loading /app/solution/baseline_checkpoints/epoch-01.pt\nBCE objective changed: filter full-training-history->full-training-history, target weighting organic-strong->organic-strong, target window (0.1, 128, 16)->(0.1, 128, 16); negative sampler ('mixed', 24, 8, 512)->('hard-mixed', 8, 24, 512); recomputing the resume checkpoint's validation BCE\nresumed validation-BCE training: epoch=1 loss=1.254848\n" } } ## Step 66 · agent · 2026-09-24T23:05:48.818Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T23:05:52.079Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/train_current_model.py\\n@@\\n def invalid_known_negatives(\\n@@\\n- liked_items: np.ndarray,\\n+ liked_items: np.ndarray,\\n ) -> np.ndarray:\\n- \\\"\\\"\\\"Mask known positives without the quadratic duplicate-column check.\\\"\\\"\\\"\\n+ \\\"\\\"\\\"Mask full-history positives without a wide 3-D like comparison.\\n+\\n+ Active likes are still checked after top-k selection, where there are only\\n+ a few dozen columns rather than the entire mining pool.\\n+ \\\"\\\"\\\"\\n@@\\n known = known_positive_packed[user_rows[:, None], byte_columns]\\n invalid = ((known >> bit_columns) & 1).astype(bool)\\n invalid |= negatives == targets[:, None]\\n- invalid |= (negatives[:, :, None] == liked_items[:, None, :]).any(axis=2)\\n return invalid\\n*** End Patch\"; text(await tools.apply_patch(patch));" } ### Tool output · 2026-09-24T23:05:52.181Z Script completed Wall time 0.0 seconds Output: {} ### Harness context { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-d26e6b05-7b5c-410f-af38-f949e9b4bf5f", "changes": { "/app/solution/train_current_model.py": { "type": "update", "unified_diff": "@@ -638,3 +638,7 @@\n ) -> np.ndarray:\n- \"\"\"Mask known positives without the quadratic duplicate-column check.\"\"\"\n+ \"\"\"Mask full-history positives without a wide 3-D like comparison.\n+\n+ Active likes are still checked after top-k selection, where there are only\n+ a few dozen columns rather than the entire mining pool.\n+ \"\"\"\n byte_columns = negatives >> 3\n@@ -644,3 +648,2 @@\n invalid |= negatives == targets[:, None]\n- invalid |= (negatives[:, :, None] == liked_items[:, None, :]).any(axis=2)\n return invalid\n", "move_path": null } }, "status": "completed", "stdout": "Success. Updated the following files:\nM /app/solution/train_current_model.py\n", "stderr": "" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "session_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "root_turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "response_id": "resp_08539f19231b9e9f016ab5acc7c23c87d2b48c7767749fe99c", "usage": { "input_tokens": 76657, "cached_input_tokens": 76416, "cache_write_input_tokens": 0, "output_tokens": 407, "reasoning_output_tokens": 175, "total_tokens": 77064 }, "turn_token_usage": { "input_tokens": 2949442, "cached_input_tokens": 2874752, "cache_write_input_tokens": 0, "output_tokens": 16539, "reasoning_output_tokens": 6078, "total_tokens": 2965981 }, "thread_token_usage": { "input_tokens": 2949442, "cached_input_tokens": 2874752, "cache_write_input_tokens": 0, "output_tokens": 16539, "reasoning_output_tokens": 6078, "total_tokens": 2965981 } } ## Step 67 · agent · 2026-09-24T23:05:55.061Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T23:05:56.973Z { "code": "const r = await tools.exec_command({\"cmd\":\"for d in /proc/[0-9]*; do c=$(tr '\\\\0' ' ' < \\\"$d/cmdline\\\" 2>/dev/null); case \\\"$c\\\" in *'/app/solution/train_current_model.py'*) echo \\\"$d $c\\\";; esac; done | tail -5\\n/opt/task-venv/bin/python -m py_compile /app/solution/train_current_model.py\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":4000}); text(r.output);" } ### Tool output · 2026-09-24T23:05:57.169Z Script completed Wall time 0.2 seconds Output: /proc/9997 /bin/sh -c for d in /proc/[0-9]*; do c=$(tr '' ' ' < "$d/cmdline" 2>/dev/null); case "$c" in *'/app/solution/train_current_model.py'*) echo "$d $c";; esac; done | tail -5 /opt/task-venv/bin/python -m py_compile /app/solution/train_current_model.py /proc/9998 /bin/sh -c for d in /proc/[0-9]*; do c=$(tr '' ' ' < "$d/cmdline" 2>/dev/null); case "$c" in *'/app/solution/train_current_model.py'*) echo "$d $c";; esac; done | tail -5 /opt/task-venv/bin/python -m py_compile /app/solution/train_current_model.py ### Harness context { "type": "token_usage_record", "thread_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "session_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "root_turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "response_id": "resp_08539f19231b9e9f016ab5acd0800887d29675d124e8ed027e", "usage": { "input_tokens": 77088, "cached_input_tokens": 76416, "cache_write_input_tokens": 0, "output_tokens": 154, "reasoning_output_tokens": 22, "total_tokens": 77242 }, "turn_token_usage": { "input_tokens": 3026530, "cached_input_tokens": 2951168, "cache_write_input_tokens": 0, "output_tokens": 16693, "reasoning_output_tokens": 6100, "total_tokens": 3043223 }, "thread_token_usage": { "input_tokens": 3026530, "cached_input_tokens": 2951168, "cache_write_input_tokens": 0, "output_tokens": 16693, "reasoning_output_tokens": 6100, "total_tokens": 3043223 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-2e94e266-aa23-4819-a9ca-44572189d585", "process_id": "55362", "command": [ "/bin/sh", "-lc", "for d in /proc/[0-9]*; do c=$(tr '\\0' ' ' < \"$d/cmdline\" 2>/dev/null); case \"$c\" in *'/app/solution/train_current_model.py'*) echo \"$d $c\";; esac; done | tail -5\n/opt/task-venv/bin/python -m py_compile /app/solution/train_current_model.py" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "for d in /proc/[0-9]*; do c=$(tr '\\0' ' ' < \"$d/cmdline\" 2>/dev/null); case \"$c\" in *'/app/solution/train_current_model.py'*) echo \"$d $c\";; esac; done | tail -5\n/opt/task-venv/bin/python -m py_compile /app/solution/train_current_model.py" } ], "source": "unified_exec_startup", "status": "completed", "stdout": " \n/proc/9997 /bin/sh -c for d in /proc/[0-9]*; do c=$(tr '\u0000' ' ' < \"$d/cmdline\" 2>/dev/null); case \"$c\" in *'/app/solution/train_current_model.py'*) echo \"$d $c\";; esac; done | tail -5\n/opt/task-venv/bin/python -m py_compile /app/solution/train_current_model.py \n/proc/9998 /bin/sh -c for d in /proc/[0-9]*; do c=$(tr '\u0000' ' ' < \"$d/cmdline\" 2>/dev/null); case \"$c\" in *'/app/solution/train_current_model.py'*) echo \"$d $c\";; esac; done | tail -5\n/opt/task-venv/bin/python -m py_compile /app/solution/train_current_model.py \n", "stderr": "", "aggregated_output": " \n/proc/9997 /bin/sh -c for d in /proc/[0-9]*; do c=$(tr '\u0000' ' ' < \"$d/cmdline\" 2>/dev/null); case \"$c\" in *'/app/solution/train_current_model.py'*) echo \"$d $c\";; esac; done | tail -5\n/opt/task-venv/bin/python -m py_compile /app/solution/train_current_model.py \n/proc/9998 /bin/sh -c for d in /proc/[0-9]*; do c=$(tr '\u0000' ' ' < \"$d/cmdline\" 2>/dev/null); case \"$c\" in *'/app/solution/train_current_model.py'*) echo \"$d $c\";; esac; done | tail -5\n/opt/task-venv/bin/python -m py_compile /app/solution/train_current_model.py \n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4310 }, "formatted_output": " \n/proc/9997 /bin/sh -c for d in /proc/[0-9]*; do c=$(tr '\u0000' ' ' < \"$d/cmdline\" 2>/dev/null); case \"$c\" in *'/app/solution/train_current_model.py'*) echo \"$d $c\";; esac; done | tail -5\n/opt/task-venv/bin/python -m py_compile /app/solution/train_current_model.py \n/proc/9998 /bin/sh -c for d in /proc/[0-9]*; do c=$(tr '\u0000' ' ' < \"$d/cmdline\" 2>/dev/null); case \"$c\" in *'/app/solution/train_current_model.py'*) echo \"$d $c\";; esac; done | tail -5\n/opt/task-venv/bin/python -m py_compile /app/solution/train_current_model.py \n" } } ## Step 68 · agent · 2026-09-24T23:05:59.002Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T23:06:02.078Z { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python /app/solution/train_current_model.py --stage validation --public-dir /task --prepared-cache /app/solution/validation_sequences.npz --negative-sampling hard-mixed --uniform-negatives 8 --inbatch-negatives 24 --hard-pool-size 256 --epochs 2 --learning-rate 0.0003 --resume-checkpoint /app/solution/baseline_checkpoints/epoch-01.pt --checkpoint-dir /app/solution/hard_ft256_checkpoints --output /app/solution/hard_ft256_validation.parquet 2>&1 | tee /app/solution/hard_ft256_validation.log\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":12000}); text(JSON.stringify(r));" } ### Tool output · 2026-09-24T23:06:03.244Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"221c60","wall_time_seconds":1.001014213,"session_id":28260,"original_token_count":0,"output":""} ### Harness context { "type": "token_usage_record", "thread_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "session_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "root_turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "response_id": "resp_08539f19231b9e9f016ab5acd5674487d289ae03143ebc452f", "usage": { "input_tokens": 77458, "cached_input_tokens": 76928, "cache_write_input_tokens": 0, "output_tokens": 191, "reasoning_output_tokens": 8, "total_tokens": 77649 }, "turn_token_usage": { "input_tokens": 3103988, "cached_input_tokens": 3028096, "cache_write_input_tokens": 0, "output_tokens": 16884, "reasoning_output_tokens": 6108, "total_tokens": 3120872 }, "thread_token_usage": { "input_tokens": 3103988, "cached_input_tokens": 3028096, "cache_write_input_tokens": 0, "output_tokens": 16884, "reasoning_output_tokens": 6108, "total_tokens": 3120872 } } ## Step 69 · agent · 2026-09-24T23:06:05.332Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T23:06:06.020Z { "code": "const r = await tools.write_stdin({\"session_id\":28260,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":12000}); text(r.output);\n" } ### Tool output · 2026-09-24T23:06:36.037Z Script completed Wall time 30.0 seconds Output: loaded full-sequence cache: tokens=20,056,329 targets=20,047,194 chronological BCE window: train=1,052,953 validation=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194 inbatch correction: min=0.250 mean_on_targets=1.001 max=4.000 target weighting=organic-strong min=0.250 mean=0.575 max=1.000 full-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250 using a fresh AdamW optimizer after loading /app/solution/baseline_checkpoints/epoch-01.pt BCE objective changed: filter full-training-history->full-training-history, target weighting organic-strong->organic-strong, target window (0.1, 128, 16)->(0.1, 128, 16); negative sampler ('mixed', 24, 8, 512)->('hard-mixed', 8, 24, 256); recomputing the resume checkpoint's validation BCE resumed validation-BCE training: epoch=1 loss=1.123556 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "session_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "root_turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "response_id": "resp_08539f19231b9e9f016ab5acdb763487d2bd5ec78f5df8700c", "usage": { "input_tokens": 77706, "cached_input_tokens": 77312, "cache_write_input_tokens": 0, "output_tokens": 54, "reasoning_output_tokens": 7, "total_tokens": 77760 }, "turn_token_usage": { "input_tokens": 3181694, "cached_input_tokens": 3105408, "cache_write_input_tokens": 0, "output_tokens": 16938, "reasoning_output_tokens": 6115, "total_tokens": 3198632 }, "thread_token_usage": { "input_tokens": 3181694, "cached_input_tokens": 3105408, "cache_write_input_tokens": 0, "output_tokens": 16938, "reasoning_output_tokens": 6115, "total_tokens": 3198632 } } ## Step 70 · agent · 2026-09-24T23:06:38.129Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T23:06:38.822Z { "code": "const r = await tools.write_stdin({\"session_id\":28260,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":12000}); text(r.output);\n" } ### Tool output · 2026-09-24T23:07:08.829Z Script completed Wall time 30.0 seconds Output: ### Harness context { "type": "token_usage_record", "thread_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "session_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "root_turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "response_id": "resp_08539f19231b9e9f016ab5acfc569c87d28d28eb1a782852a3", "usage": { "input_tokens": 78043, "cached_input_tokens": 77568, "cache_write_input_tokens": 0, "output_tokens": 54, "reasoning_output_tokens": 7, "total_tokens": 78097 }, "turn_token_usage": { "input_tokens": 3259737, "cached_input_tokens": 3182976, "cache_write_input_tokens": 0, "output_tokens": 16992, "reasoning_output_tokens": 6122, "total_tokens": 3276729 }, "thread_token_usage": { "input_tokens": 3259737, "cached_input_tokens": 3182976, "cache_write_input_tokens": 0, "output_tokens": 16992, "reasoning_output_tokens": 6122, "total_tokens": 3276729 } } ## Step 71 · agent · 2026-09-24T23:07:11.029Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T23:07:12.718Z { "code": "const r = await tools.write_stdin({\"session_id\":28260,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":12000}); text(r.output);\n" } ### Tool output · 2026-09-24T23:07:42.733Z Script completed Wall time 30.0 seconds Output: ### Harness context { "type": "token_usage_record", "thread_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "session_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "root_turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "response_id": "resp_08539f19231b9e9f016ab5ad1d180c87d2a9fdeb670573390f", "usage": { "input_tokens": 78120, "cached_input_tokens": 77824, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 78173 }, "turn_token_usage": { "input_tokens": 3337857, "cached_input_tokens": 3260800, "cache_write_input_tokens": 0, "output_tokens": 17045, "reasoning_output_tokens": 6128, "total_tokens": 3354902 }, "thread_token_usage": { "input_tokens": 3337857, "cached_input_tokens": 3260800, "cache_write_input_tokens": 0, "output_tokens": 17045, "reasoning_output_tokens": 6128, "total_tokens": 3354902 } } ## Step 72 · agent · 2026-09-24T23:07:44.197Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T23:07:44.914Z { "code": "const r = await tools.write_stdin({\"session_id\":28260,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":12000}); text(r.output);\n" } ### Tool output · 2026-09-24T23:08:14.926Z Script completed Wall time 30.0 seconds Output: ### Harness context { "type": "token_usage_record", "thread_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "session_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "root_turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "response_id": "resp_08539f19231b9e9f016ab5ad3efa6887d2945a0f092c97ddbc", "usage": { "input_tokens": 78196, "cached_input_tokens": 77952, "cache_write_input_tokens": 0, "output_tokens": 59, "reasoning_output_tokens": 12, "total_tokens": 78255 }, "turn_token_usage": { "input_tokens": 3416053, "cached_input_tokens": 3338752, "cache_write_input_tokens": 0, "output_tokens": 17104, "reasoning_output_tokens": 6140, "total_tokens": 3433157 }, "thread_token_usage": { "input_tokens": 3416053, "cached_input_tokens": 3338752, "cache_write_input_tokens": 0, "output_tokens": 17104, "reasoning_output_tokens": 6140, "total_tokens": 3433157 } } ## Step 73 · agent · 2026-09-24T23:08:18.279Z ### Thinking unavailable — no text recorded The first mining implementation exposed a CPU hotspot in the wide active-like mask, so I stopped that run before it could consume the budget. The optimized version applies the full-history mask across the proposal pool and defers the much smaller explicit-like check until after top-k selection; the pool is 256 items, keeping the miner bounded without changing its semantics. ## Step 74 · agent · 2026-09-24T23:08:20.000Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T23:08:20.431Z { "code": "const r = await tools.write_stdin({\"session_id\":28260,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":12000}); text(r.output);\n" } ### Tool output · 2026-09-24T23:08:50.455Z Script completed Wall time 30.0 seconds Output: ### Harness context { "type": "token_usage_record", "thread_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "session_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "root_turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "response_id": "resp_08539f19231b9e9f016ab5ad5f21e487d2a1d5a2e6106062ca", "usage": { "input_tokens": 78278, "cached_input_tokens": 78080, "cache_write_input_tokens": 0, "output_tokens": 159, "reasoning_output_tokens": 35, "total_tokens": 78437 }, "turn_token_usage": { "input_tokens": 3494331, "cached_input_tokens": 3416832, "cache_write_input_tokens": 0, "output_tokens": 17263, "reasoning_output_tokens": 6175, "total_tokens": 3511594 }, "thread_token_usage": { "input_tokens": 3494331, "cached_input_tokens": 3416832, "cache_write_input_tokens": 0, "output_tokens": 17263, "reasoning_output_tokens": 6175, "total_tokens": 3511594 } } ## Step 75 · agent · 2026-09-24T23:08:52.565Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T23:08:53.240Z { "code": "const r = await tools.write_stdin({\"session_id\":28260,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":12000}); text(r.output);\n" } ### Tool output · 2026-09-24T23:09:23.265Z Script completed Wall time 30.0 seconds Output: epoch=2/2 batches=250 targets=987,417 batch_bce=0.687096 running_bce=0.693237 elapsed=156.3s saved epoch checkpoint: /app/solution/hard_ft256_checkpoints/epoch-02.pt epoch=2/2 train_loss=0.69259 validation_loss=0.68198 pair_accuracy=0.402 train_targets=1,052,953 validation_targets=130,103 train_seconds=166.8 validation_seconds=12.4 restored best validation-BCE checkpoint: epoch=2 loss=0.681977 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "session_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "root_turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "response_id": "resp_08539f19231b9e9f016ab5ad82aa1087d2bd85bbe1f9802342", "usage": { "input_tokens": 78460, "cached_input_tokens": 78080, "cache_write_input_tokens": 0, "output_tokens": 56, "reasoning_output_tokens": 9, "total_tokens": 78516 }, "turn_token_usage": { "input_tokens": 3572791, "cached_input_tokens": 3494912, "cache_write_input_tokens": 0, "output_tokens": 17319, "reasoning_output_tokens": 6184, "total_tokens": 3590110 }, "thread_token_usage": { "input_tokens": 3572791, "cached_input_tokens": 3494912, "cache_write_input_tokens": 0, "output_tokens": 17319, "reasoning_output_tokens": 6184, "total_tokens": 3590110 } } ## Step 76 · agent · 2026-09-24T23:09:25.019Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T23:09:25.682Z { "code": "const r = await tools.write_stdin({\"session_id\":28260,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":12000}); text(r.output);\n" } ### Tool output · 2026-09-24T23:09:55.708Z Script completed Wall time 30.0 seconds Output: ### Harness context { "type": "token_usage_record", "thread_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "session_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "root_turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "response_id": "resp_08539f19231b9e9f016ab5ada38ac087d29202ace2ee85065d", "usage": { "input_tokens": 78665, "cached_input_tokens": 78336, "cache_write_input_tokens": 0, "output_tokens": 70, "reasoning_output_tokens": 23, "total_tokens": 78735 }, "turn_token_usage": { "input_tokens": 3651456, "cached_input_tokens": 3573248, "cache_write_input_tokens": 0, "output_tokens": 17389, "reasoning_output_tokens": 6207, "total_tokens": 3668845 }, "thread_token_usage": { "input_tokens": 3651456, "cached_input_tokens": 3573248, "cache_write_input_tokens": 0, "output_tokens": 17389, "reasoning_output_tokens": 6207, "total_tokens": 3668845 } } ## Step 77 · agent · 2026-09-24T23:09:59.334Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T23:10:00.060Z { "code": "const r = await tools.write_stdin({\"session_id\":28260,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":12000}); text(r.output);\n" } ### Tool output · 2026-09-24T23:10:30.080Z Script completed Wall time 30.0 seconds Output: ### Harness context { "type": "token_usage_record", "thread_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "session_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "root_turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "response_id": "resp_08539f19231b9e9f016ab5adc4184c87d29afc9a8ffbfc75be", "usage": { "input_tokens": 78758, "cached_input_tokens": 78464, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 78811 }, "turn_token_usage": { "input_tokens": 3730214, "cached_input_tokens": 3651712, "cache_write_input_tokens": 0, "output_tokens": 17442, "reasoning_output_tokens": 6213, "total_tokens": 3747656 }, "thread_token_usage": { "input_tokens": 3730214, "cached_input_tokens": 3651712, "cache_write_input_tokens": 0, "output_tokens": 17442, "reasoning_output_tokens": 6213, "total_tokens": 3747656 } } ## Step 78 · agent · 2026-09-24T23:10:31.466Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T23:10:32.184Z { "code": "const r = await tools.write_stdin({\"session_id\":28260,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":15000}); text(r.output);\n" } ### Tool output · 2026-09-24T23:10:43.191Z Script completed Wall time 11.0 seconds Output: ranked users=1,024/8,456 ranked users=2,048/8,456 ranked users=3,072/8,456 ranked users=4,096/8,456 ranked users=5,120/8,456 ranked users=6,144/8,456 ranked users=7,168/8,456 ranked users=8,192/8,456 ranked users=8,456/8,456 { "baseline": "yambda-windowed-sequence-two-tower-base", "stage": "validation", "output": "/app/solution/hard_ft256_validation.parquet", "rows": 845600, "users": 8456, "runtime_seconds": 276.579, "public_inputs_only": true, "architecture": { "family": "pure-windowed-sequence-two-tower", "sequence_training": "every-adjacent-target-within-recent-chronological-window", "sequence_encoder": "one-layer-GRU-dense-timestep-loss", "bptt_hidden_state": "carried-and-detached", "model_selection": "chronological-validation-BCE-early-stopping", "loss_execution": "gradient-accumulated-microbatches", "user_id_embedding": false, "user_mlp": "582-512-256-192", "item_mlp": "196-512-256-192", "normalization": "none", "score": "raw-dot-product", "objective": "sample-weighted-BCE-at-every-timestep", "negative_sampling": "8-uniform-plus-24-hard-mixed", "false_negative_filter": "full-training-history", "target_weighting": "organic-strong" }, "config": { "bptt_steps": 100, "user_batch_size": 64, "embedding_dim": 192, "hidden_dim": 192, "user_hidden_1": 512, "user_hidden_2": 256, "item_hidden_1": 512, "item_hidden_2": 256, "dropout": 0.05, "epochs": 2, "learning_rate": 0.0003, "weight_decay": 1e-05, "gradient_clip": 2.0, "loss_microbatch_size": 65536, "uniform_negatives": 8, "inbatch_negatives": 24, "negative_sampling": "hard-mixed", "hard_pool_size": 256, "inbatch_correction_power": 0.5, "inbatch_correction_min": 0.25, "inbatch_correction_max": 4.0, "negative_filter_scope": "full-training-history", "target_weighting": "organic-strong", "ordinary_target_weight": 0.25, "organic_strong_target_weight": 1.0, "organic_like_target_weight": 3.0, "discovery_horizon_days": 7, "validation_fraction": 0.1, "max_train_targets_per_user": 128, "max_validation_targets_per_user": 16, "early_stopping_patience": 1, "early_stopping_min_delta": 0.0001, "seed": 29 }, "sequence_tokens": 20056329, "all_next_item_targets": 20047194, "selected_training_targets": 1052953, "selected_validation_targets": 130103, "discarded_old_targets": 18864138, "training_history": [ { "epoch": 1.0, "loss": 0.5398380466771012, "positive_logit": 0.7427799607756806, "negative_logit": -0.6222247067485194, "pair_accuracy": 0.7547733315257186, "targets": 1052953.0, "target_weight_sum": 632551.0, "batches": 266.0, "seconds": 215.029519907, "validation_targets": 130103.0, "validation_target_weight_sum": 78769.25, "validation_seconds": 11.996522798999877, "validation_loss": 1.1235557443846422 }, { "epoch": 2.0, "loss": 0.6925850678499164, "positive_logit": 0.034003850708125755, "negative_logit": -0.01909888959764294, "pair_accuracy": 0.4015211267739396, "targets": 1052953.0, "target_weight_sum": 632551.0, "batches": 266.0, "seconds": 166.84663537300003, "validation_loss": 0.6819765626003776, "validation_targets": 130103.0, "validation_target_weight_sum": 78769.25, "validation_seconds": 12.398591874999966 } ], "embedding_diagnostics": { "query_norm_mean": 0.8924046158790588, "query_norm_std": 0.9947032332420349, "item_norm_mean": 1.557402491569519, "item_norm_std": 0.9982138276100159 }, "torch_version": "2.8.0+cpu", "device": "cpu" } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "session_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "root_turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "response_id": "resp_08539f19231b9e9f016ab5ade66c8c87d2b1296d0474980466", "usage": { "input_tokens": 78834, "cached_input_tokens": 78592, "cache_write_input_tokens": 0, "output_tokens": 54, "reasoning_output_tokens": 7, "total_tokens": 78888 }, "turn_token_usage": { "input_tokens": 3809048, "cached_input_tokens": 3730304, "cache_write_input_tokens": 0, "output_tokens": 17496, "reasoning_output_tokens": 6220, "total_tokens": 3826544 }, "thread_token_usage": { "input_tokens": 3809048, "cached_input_tokens": 3730304, "cache_write_input_tokens": 0, "output_tokens": 17496, "reasoning_output_tokens": 6220, "total_tokens": 3826544 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-577e7aad-2a8f-4f8f-9e35-36ab291665a1", "process_id": "28260", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python /app/solution/train_current_model.py --stage validation --public-dir /task --prepared-cache /app/solution/validation_sequences.npz --negative-sampling hard-mixed --uniform-negatives 8 --inbatch-negatives 24 --hard-pool-size 256 --epochs 2 --learning-rate 0.0003 --resume-checkpoint /app/solution/baseline_checkpoints/epoch-01.pt --checkpoint-dir /app/solution/hard_ft256_checkpoints --output /app/solution/hard_ft256_validation.parquet 2>&1 | tee /app/solution/hard_ft256_validation.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python /app/solution/train_current_model.py --stage validation --public-dir /task --prepared-cache /app/solution/validation_sequences.npz --negative-sampling hard-mixed --uniform-negatives 8 --inbatch-negatives 24 --hard-pool-size 256 --epochs 2 --learning-rate 0.0003 --resume-checkpoint /app/solution/baseline_checkpoints/epoch-01.pt --checkpoint-dir /app/solution/hard_ft256_checkpoints --output /app/solution/hard_ft256_validation.parquet 2>&1 | tee /app/solution/hard_ft256_validation.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "loaded full-sequence cache: tokens=20,056,329 targets=20,047,194\nchronological BCE window: train=1,052,953 validation=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194\ninbatch correction: min=0.250 mean_on_targets=1.001 max=4.000\ntarget weighting=organic-strong min=0.250 mean=0.575 max=1.000\nfull-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250\nusing a fresh AdamW optimizer after loading /app/solution/baseline_checkpoints/epoch-01.pt\nBCE objective changed: filter full-training-history->full-training-history, target weighting organic-strong->organic-strong, target window (0.1, 128, 16)->(0.1, 128, 16); negative sampler ('mixed', 24, 8, 512)->('hard-mixed', 8, 24, 256); recomputing the resume checkpoint's validation BCE\nresumed validation-BCE training: epoch=1 loss=1.123556\nepoch=2/2 batches=250 targets=987,417 batch_bce=0.687096 running_bce=0.693237 elapsed=156.3s\nsaved epoch checkpoint: /app/solution/hard_ft256_checkpoints/epoch-02.pt\nepoch=2/2 train_loss=0.69259 validation_loss=0.68198 pair_accuracy=0.402 train_targets=1,052,953 validation_targets=130,103 train_seconds=166.8 validation_seconds=12.4\nrestored best validation-BCE checkpoint: epoch=2 loss=0.681977\nranked users=1,024/8,456\nranked users=2,048/8,456\nranked users=3,072/8,456\nranked users=4,096/8,456\nranked users=5,120/8,456\nranked users=6,144/8,456\nranked users=7,168/8,456\nranked users=8,192/8,456\nranked users=8,456/8,456\n{\n \"baseline\": \"yambda-windowed-sequence-two-tower-base\",\n \"stage\": \"validation\",\n \"output\": \"/app/solution/hard_ft256_validation.parquet\",\n \"rows\": 845600,\n \"users\": 8456,\n \"runtime_seconds\": 276.579,\n \"public_inputs_only\": true,\n \"architecture\": {\n \"family\": \"pure-windowed-sequence-two-tower\",\n \"sequence_training\": \"every-adjacent-target-within-recent-chronological-window\",\n \"sequence_encoder\": \"one-layer-GRU-dense-timestep-loss\",\n \"bptt_hidden_state\": \"carried-and-detached\",\n \"model_selection\": \"chronological-validation-BCE-early-stopping\",\n \"loss_execution\": \"gradient-accumulated-microbatches\",\n \"user_id_embedding\": false,\n \"user_mlp\": \"582-512-256-192\",\n \"item_mlp\": \"196-512-256-192\",\n \"normalization\": \"none\",\n \"score\": \"raw-dot-product\",\n \"objective\": \"sample-weighted-BCE-at-every-timestep\",\n \"negative_sampling\": \"8-uniform-plus-24-hard-mixed\",\n \"false_negative_filter\": \"full-training-history\",\n \"target_weighting\": \"organic-strong\"\n },\n \"config\": {\n \"bptt_steps\": 100,\n \"user_batch_size\": 64,\n \"embedding_dim\": 192,\n \"hidden_dim\": 192,\n \"user_hidden_1\": 512,\n \"user_hidden_2\": 256,\n \"item_hidden_1\": 512,\n \"item_hidden_2\": 256,\n \"dropout\": 0.05,\n \"epochs\": 2,\n \"learning_rate\": 0.0003,\n \"weight_decay\": 1e-05,\n \"gradient_clip\": 2.0,\n \"loss_microbatch_size\": 65536,\n \"uniform_negatives\": 8,\n \"inbatch_negatives\": 24,\n \"negative_sampling\": \"hard-mixed\",\n \"hard_pool_size\": 256,\n \"inbatch_correction_power\": 0.5,\n \"inbatch_correction_min\": 0.25,\n \"inbatch_correction_max\": 4.0,\n \"negative_filter_scope\": \"full-training-history\",\n \"target_weighting\": \"organic-strong\",\n \"ordinary_target_weight\": 0.25,\n \"organic_strong_target_weight\": 1.0,\n \"organic_like_target_weight\": 3.0,\n \"discovery_horizon_days\": 7,\n \"validation_fraction\": 0.1,\n \"max_train_targets_per_user\": 128,\n \"max_validation_targets_per_user\": 16,\n \"early_stopping_patience\": 1,\n \"early_stopping_min_delta\": 0.0001,\n \"seed\": 29\n },\n \"sequence_tokens\": 20056329,\n \"all_next_item_targets\": 20047194,\n \"selected_training_targets\": 1052953,\n \"selected_validation_targets\": 130103,\n \"discarded_old_targets\": 18864138,\n \"training_history\": [\n {\n \"epoch\": 1.0,\n \"loss\": 0.5398380466771012,\n \"positive_logit\": 0.7427799607756806,\n \"negative_logit\": -0.6222247067485194,\n \"pair_accuracy\": 0.7547733315257186,\n \"targets\": 1052953.0,\n \"target_weight_sum\": 632551.0,\n \"batches\": 266.0,\n \"seconds\": 215.029519907,\n \"validation_targets\": 130103.0,\n \"validation_target_weight_sum\": 78769.25,\n \"validation_seconds\": 11.996522798999877,\n \"validation_loss\": 1.1235557443846422\n },\n {\n \"epoch\": 2.0,\n \"loss\": 0.6925850678499164,\n \"positive_logit\": 0.034003850708125755,\n \"negative_logit\": -0.01909888959764294,\n \"pair_accuracy\": 0.4015211267739396,\n \"targets\": 1052953.0,\n \"target_weight_sum\": 632551.0,\n \"batches\": 266.0,\n \"seconds\": 166.84663537300003,\n \"validation_loss\": 0.6819765626003776,\n \"validation_targets\": 130103.0,\n \"validation_target_weight_sum\": 78769.25,\n \"validation_seconds\": 12.398591874999966\n }\n ],\n \"embedding_diagnostics\": {\n \"query_norm_mean\": 0.8924046158790588,\n \"query_norm_std\": 0.9947032332420349,\n \"item_norm_mean\": 1.557402491569519,\n \"item_norm_std\": 0.9982138276100159\n },\n \"torch_version\": \"2.8.0+cpu\",\n \"device\": \"cpu\"\n}\n", "stderr": "", "aggregated_output": "loaded full-sequence cache: tokens=20,056,329 targets=20,047,194\nchronological BCE window: train=1,052,953 validation=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194\ninbatch correction: min=0.250 mean_on_targets=1.001 max=4.000\ntarget weighting=organic-strong min=0.250 mean=0.575 max=1.000\nfull-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250\nusing a fresh AdamW optimizer after loading /app/solution/baseline_checkpoints/epoch-01.pt\nBCE objective changed: filter full-training-history->full-training-history, target weighting organic-strong->organic-strong, target window (0.1, 128, 16)->(0.1, 128, 16); negative sampler ('mixed', 24, 8, 512)->('hard-mixed', 8, 24, 256); recomputing the resume checkpoint's validation BCE\nresumed validation-BCE training: epoch=1 loss=1.123556\nepoch=2/2 batches=250 targets=987,417 batch_bce=0.687096 running_bce=0.693237 elapsed=156.3s\nsaved epoch checkpoint: /app/solution/hard_ft256_checkpoints/epoch-02.pt\nepoch=2/2 train_loss=0.69259 validation_loss=0.68198 pair_accuracy=0.402 train_targets=1,052,953 validation_targets=130,103 train_seconds=166.8 validation_seconds=12.4\nrestored best validation-BCE checkpoint: epoch=2 loss=0.681977\nranked users=1,024/8,456\nranked users=2,048/8,456\nranked users=3,072/8,456\nranked users=4,096/8,456\nranked users=5,120/8,456\nranked users=6,144/8,456\nranked users=7,168/8,456\nranked users=8,192/8,456\nranked users=8,456/8,456\n{\n \"baseline\": \"yambda-windowed-sequence-two-tower-base\",\n \"stage\": \"validation\",\n \"output\": \"/app/solution/hard_ft256_validation.parquet\",\n \"rows\": 845600,\n \"users\": 8456,\n \"runtime_seconds\": 276.579,\n \"public_inputs_only\": true,\n \"architecture\": {\n \"family\": \"pure-windowed-sequence-two-tower\",\n \"sequence_training\": \"every-adjacent-target-within-recent-chronological-window\",\n \"sequence_encoder\": \"one-layer-GRU-dense-timestep-loss\",\n \"bptt_hidden_state\": \"carried-and-detached\",\n \"model_selection\": \"chronological-validation-BCE-early-stopping\",\n \"loss_execution\": \"gradient-accumulated-microbatches\",\n \"user_id_embedding\": false,\n \"user_mlp\": \"582-512-256-192\",\n \"item_mlp\": \"196-512-256-192\",\n \"normalization\": \"none\",\n \"score\": \"raw-dot-product\",\n \"objective\": \"sample-weighted-BCE-at-every-timestep\",\n \"negative_sampling\": \"8-uniform-plus-24-hard-mixed\",\n \"false_negative_filter\": \"full-training-history\",\n \"target_weighting\": \"organic-strong\"\n },\n \"config\": {\n \"bptt_steps\": 100,\n \"user_batch_size\": 64,\n \"embedding_dim\": 192,\n \"hidden_dim\": 192,\n \"user_hidden_1\": 512,\n \"user_hidden_2\": 256,\n \"item_hidden_1\": 512,\n \"item_hidden_2\": 256,\n \"dropout\": 0.05,\n \"epochs\": 2,\n \"learning_rate\": 0.0003,\n \"weight_decay\": 1e-05,\n \"gradient_clip\": 2.0,\n \"loss_microbatch_size\": 65536,\n \"uniform_negatives\": 8,\n \"inbatch_negatives\": 24,\n \"negative_sampling\": \"hard-mixed\",\n \"hard_pool_size\": 256,\n \"inbatch_correction_power\": 0.5,\n \"inbatch_correction_min\": 0.25,\n \"inbatch_correction_max\": 4.0,\n \"negative_filter_scope\": \"full-training-history\",\n \"target_weighting\": \"organic-strong\",\n \"ordinary_target_weight\": 0.25,\n \"organic_strong_target_weight\": 1.0,\n \"organic_like_target_weight\": 3.0,\n \"discovery_horizon_days\": 7,\n \"validation_fraction\": 0.1,\n \"max_train_targets_per_user\": 128,\n \"max_validation_targets_per_user\": 16,\n \"early_stopping_patience\": 1,\n \"early_stopping_min_delta\": 0.0001,\n \"seed\": 29\n },\n \"sequence_tokens\": 20056329,\n \"all_next_item_targets\": 20047194,\n \"selected_training_targets\": 1052953,\n \"selected_validation_targets\": 130103,\n \"discarded_old_targets\": 18864138,\n \"training_history\": [\n {\n \"epoch\": 1.0,\n \"loss\": 0.5398380466771012,\n \"positive_logit\": 0.7427799607756806,\n \"negative_logit\": -0.6222247067485194,\n \"pair_accuracy\": 0.7547733315257186,\n \"targets\": 1052953.0,\n \"target_weight_sum\": 632551.0,\n \"batches\": 266.0,\n \"seconds\": 215.029519907,\n \"validation_targets\": 130103.0,\n \"validation_target_weight_sum\": 78769.25,\n \"validation_seconds\": 11.996522798999877,\n \"validation_loss\": 1.1235557443846422\n },\n {\n \"epoch\": 2.0,\n \"loss\": 0.6925850678499164,\n \"positive_logit\": 0.034003850708125755,\n \"negative_logit\": -0.01909888959764294,\n \"pair_accuracy\": 0.4015211267739396,\n \"targets\": 1052953.0,\n \"target_weight_sum\": 632551.0,\n \"batches\": 266.0,\n \"seconds\": 166.84663537300003,\n \"validation_loss\": 0.6819765626003776,\n \"validation_targets\": 130103.0,\n \"validation_target_weight_sum\": 78769.25,\n \"validation_seconds\": 12.398591874999966\n }\n ],\n \"embedding_diagnostics\": {\n \"query_norm_mean\": 0.8924046158790588,\n \"query_norm_std\": 0.9947032332420349,\n \"item_norm_mean\": 1.557402491569519,\n \"item_norm_std\": 0.9982138276100159\n },\n \"torch_version\": \"2.8.0+cpu\",\n \"device\": \"cpu\"\n}\n", "exit_code": 0, "duration": { "secs": 280, "nanos": 939784901 }, "formatted_output": "loaded full-sequence cache: tokens=20,056,329 targets=20,047,194\nchronological BCE window: train=1,052,953 validation=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194\ninbatch correction: min=0.250 mean_on_targets=1.001 max=4.000\ntarget weighting=organic-strong min=0.250 mean=0.575 max=1.000\nfull-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250\nusing a fresh AdamW optimizer after loading /app/solution/baseline_checkpoints/epoch-01.pt\nBCE objective changed: filter full-training-history->full-training-history, target weighting organic-strong->organic-strong, target window (0.1, 128, 16)->(0.1, 128, 16); negative sampler ('mixed', 24, 8, 512)->('hard-mixed', 8, 24, 256); recomputing the resume checkpoint's validation BCE\nresumed validation-BCE training: epoch=1 loss=1.123556\nepoch=2/2 batches=250 targets=987,417 batch_bce=0.687096 running_bce=0.693237 elapsed=156.3s\nsaved epoch checkpoint: /app/solution/hard_ft256_checkpoints/epoch-02.pt\nepoch=2/2 train_loss=0.69259 validation_loss=0.68198 pair_accuracy=0.402 train_targets=1,052,953 validation_targets=130,103 train_seconds=166.8 validation_seconds=12.4\nrestored best validation-BCE checkpoint: epoch=2 loss=0.681977\nranked users=1,024/8,456\nranked users=2,048/8,456\nranked users=3,072/8,456\nranked users=4,096/8,456\nranked users=5,120/8,456\nranked users=6,144/8,456\nranked users=7,168/8,456\nranked users=8,192/8,456\nranked users=8,456/8,456\n{\n \"baseline\": \"yambda-windowed-sequence-two-tower-base\",\n \"stage\": \"validation\",\n \"output\": \"/app/solution/hard_ft256_validation.parquet\",\n \"rows\": 845600,\n \"users\": 8456,\n \"runtime_seconds\": 276.579,\n \"public_inputs_only\": true,\n \"architecture\": {\n \"family\": \"pure-windowed-sequence-two-tower\",\n \"sequence_training\": \"every-adjacent-target-within-recent-chronological-window\",\n \"sequence_encoder\": \"one-layer-GRU-dense-timestep-loss\",\n \"bptt_hidden_state\": \"carried-and-detached\",\n \"model_selection\": \"chronological-validation-BCE-early-stopping\",\n \"loss_execution\": \"gradient-accumulated-microbatches\",\n \"user_id_embedding\": false,\n \"user_mlp\": \"582-512-256-192\",\n \"item_mlp\": \"196-512-256-192\",\n \"normalization\": \"none\",\n \"score\": \"raw-dot-product\",\n \"objective\": \"sample-weighted-BCE-at-every-timestep\",\n \"negative_sampling\": \"8-uniform-plus-24-hard-mixed\",\n \"false_negative_filter\": \"full-training-history\",\n \"target_weighting\": \"organic-strong\"\n },\n \"config\": {\n \"bptt_steps\": 100,\n \"user_batch_size\": 64,\n \"embedding_dim\": 192,\n \"hidden_dim\": 192,\n \"user_hidden_1\": 512,\n \"user_hidden_2\": 256,\n \"item_hidden_1\": 512,\n \"item_hidden_2\": 256,\n \"dropout\": 0.05,\n \"epochs\": 2,\n \"learning_rate\": 0.0003,\n \"weight_decay\": 1e-05,\n \"gradient_clip\": 2.0,\n \"loss_microbatch_size\": 65536,\n \"uniform_negatives\": 8,\n \"inbatch_negatives\": 24,\n \"negative_sampling\": \"hard-mixed\",\n \"hard_pool_size\": 256,\n \"inbatch_correction_power\": 0.5,\n \"inbatch_correction_min\": 0.25,\n \"inbatch_correction_max\": 4.0,\n \"negative_filter_scope\": \"full-training-history\",\n \"target_weighting\": \"organic-strong\",\n \"ordinary_target_weight\": 0.25,\n \"organic_strong_target_weight\": 1.0,\n \"organic_like_target_weight\": 3.0,\n \"discovery_horizon_days\": 7,\n \"validation_fraction\": 0.1,\n \"max_train_targets_per_user\": 128,\n \"max_validation_targets_per_user\": 16,\n \"early_stopping_patience\": 1,\n \"early_stopping_min_delta\": 0.0001,\n \"seed\": 29\n },\n \"sequence_tokens\": 20056329,\n \"all_next_item_targets\": 20047194,\n \"selected_training_targets\": 1052953,\n \"selected_validation_targets\": 130103,\n \"discarded_old_targets\": 18864138,\n \"training_history\": [\n {\n \"epoch\": 1.0,\n \"loss\": 0.5398380466771012,\n \"positive_logit\": 0.7427799607756806,\n \"negative_logit\": -0.6222247067485194,\n \"pair_accuracy\": 0.7547733315257186,\n \"targets\": 1052953.0,\n \"target_weight_sum\": 632551.0,\n \"batches\": 266.0,\n \"seconds\": 215.029519907,\n \"validation_targets\": 130103.0,\n \"validation_target_weight_sum\": 78769.25,\n \"validation_seconds\": 11.996522798999877,\n \"validation_loss\": 1.1235557443846422\n },\n {\n \"epoch\": 2.0,\n \"loss\": 0.6925850678499164,\n \"positive_logit\": 0.034003850708125755,\n \"negative_logit\": -0.01909888959764294,\n \"pair_accuracy\": 0.4015211267739396,\n \"targets\": 1052953.0,\n \"target_weight_sum\": 632551.0,\n \"batches\": 266.0,\n \"seconds\": 166.84663537300003,\n \"validation_loss\": 0.6819765626003776,\n \"validation_targets\": 130103.0,\n \"validation_target_weight_sum\": 78769.25,\n \"validation_seconds\": 12.398591874999966\n }\n ],\n \"embedding_diagnostics\": {\n \"query_norm_mean\": 0.8924046158790588,\n \"query_norm_std\": 0.9947032332420349,\n \"item_norm_mean\": 1.557402491569519,\n \"item_norm_std\": 0.9982138276100159\n },\n \"torch_version\": \"2.8.0+cpu\",\n \"device\": \"cpu\"\n}\n" } } ## Step 79 · agent · 2026-09-24T23:10:44.600Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T23:10:45.991Z { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/hard_ft256_validation.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":5000}); text(r.output);" } ### Tool output · 2026-09-24T23:10:49.412Z Script completed Wall time 3.4 seconds Output: { "model": "/app/solution/hard_ft256_validation.parquet", "scores": { "quality_ndcg@10": 0.009742, "quality_users": 4263 } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "session_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "root_turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "response_id": "resp_08539f19231b9e9f016ab5adf36cb887d2bae2ee6a94eef1cf", "usage": { "input_tokens": 80159, "cached_input_tokens": 78592, "cache_write_input_tokens": 0, "output_tokens": 78, "reasoning_output_tokens": 6, "total_tokens": 80237 }, "turn_token_usage": { "input_tokens": 3889207, "cached_input_tokens": 3808896, "cache_write_input_tokens": 0, "output_tokens": 17574, "reasoning_output_tokens": 6226, "total_tokens": 3906781 }, "thread_token_usage": { "input_tokens": 3889207, "cached_input_tokens": 3808896, "cache_write_input_tokens": 0, "output_tokens": 17574, "reasoning_output_tokens": 6226, "total_tokens": 3906781 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-6f765ba7-ac67-4d09-a382-ed66a113b050", "process_id": "96927", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/hard_ft256_validation.parquet" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/hard_ft256_validation.parquet" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{\n \"model\": \"/app/solution/hard_ft256_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.009742,\n \"quality_users\": 4263\n }\n}\n", "stderr": "", "aggregated_output": "{\n \"model\": \"/app/solution/hard_ft256_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.009742,\n \"quality_users\": 4263\n }\n}\n", "exit_code": 0, "duration": { "secs": 3, "nanos": 227501948 }, "formatted_output": "{\n \"model\": \"/app/solution/hard_ft256_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.009742,\n \"quality_users\": 4263\n }\n}\n" } } ## Step 80 · agent · 2026-09-24T23:11:05.692Z ### Thinking unavailable — no text recorded ## Step 81 · agent · 2026-09-24T23:11:14.345Z ### Thinking unavailable — no text recorded The aggressive 24-hard/8-random mix is a useful negative result: although it sharply improves its own held-out hard-negative BCE, public NDCG falls to `0.009742` because selected extremes dominate the binary loss and collapse useful score magnitude. I’m retaining hard mining only as a small calibration term—4 mined plus 28 uniform negatives—with a lower learning rate. This tests the intended “semi-hard regularizer” regime rather than replacing the catalog objective. ### exec · 2026-09-24T23:11:19.200Z { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python /app/solution/train_current_model.py --stage validation --public-dir /task --prepared-cache /app/solution/validation_sequences.npz --negative-sampling hard-mixed --uniform-negatives 28 --inbatch-negatives 4 --hard-pool-size 128 --epochs 2 --learning-rate 0.0001 --resume-checkpoint /app/solution/baseline_checkpoints/epoch-01.pt --checkpoint-dir /app/solution/semihard_checkpoints --output /app/solution/semihard_validation.parquet 2>&1 | tee /app/solution/semihard_validation.log\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":12000}); text(JSON.stringify(r));" } ### Tool output · 2026-09-24T23:11:20.365Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"72d294","wall_time_seconds":1.000753544,"session_id":38244,"original_token_count":0,"output":""} ### Harness context { "type": "token_usage_record", "thread_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "session_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "root_turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "response_id": "resp_08539f19231b9e9f016ab5adf9bdb487d282bb91077d2ab39c", "usage": { "input_tokens": 80308, "cached_input_tokens": 80000, "cache_write_input_tokens": 0, "output_tokens": 1136, "reasoning_output_tokens": 850, "total_tokens": 81444 }, "turn_token_usage": { "input_tokens": 3969515, "cached_input_tokens": 3888896, "cache_write_input_tokens": 0, "output_tokens": 18710, "reasoning_output_tokens": 7076, "total_tokens": 3988225 }, "thread_token_usage": { "input_tokens": 3969515, "cached_input_tokens": 3888896, "cache_write_input_tokens": 0, "output_tokens": 18710, "reasoning_output_tokens": 7076, "total_tokens": 3988225 } } ## Step 82 · agent · 2026-09-24T23:11:21.983Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T23:11:22.733Z { "code": "const r = await tools.write_stdin({\"session_id\":38244,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":12000}); text(r.output);\n" } ### Tool output · 2026-09-24T23:11:52.742Z Script completed Wall time 30.0 seconds Output: loaded full-sequence cache: tokens=20,056,329 targets=20,047,194 chronological BCE window: train=1,052,953 validation=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194 inbatch correction: min=0.250 mean_on_targets=1.001 max=4.000 target weighting=organic-strong min=0.250 mean=0.575 max=1.000 full-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250 using a fresh AdamW optimizer after loading /app/solution/baseline_checkpoints/epoch-01.pt BCE objective changed: filter full-training-history->full-training-history, target weighting organic-strong->organic-strong, target window (0.1, 128, 16)->(0.1, 128, 16); negative sampler ('mixed', 24, 8, 512)->('hard-mixed', 28, 4, 128); recomputing the resume checkpoint's validation BCE resumed validation-BCE training: epoch=1 loss=0.618836 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "session_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "root_turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "response_id": "resp_08539f19231b9e9f016ab5ae18b91887d29db5cf7731e4992b", "usage": { "input_tokens": 81501, "cached_input_tokens": 80128, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 81554 }, "turn_token_usage": { "input_tokens": 4051016, "cached_input_tokens": 3969024, "cache_write_input_tokens": 0, "output_tokens": 18763, "reasoning_output_tokens": 7082, "total_tokens": 4069779 }, "thread_token_usage": { "input_tokens": 4051016, "cached_input_tokens": 3969024, "cache_write_input_tokens": 0, "output_tokens": 18763, "reasoning_output_tokens": 7082, "total_tokens": 4069779 } } ## Step 83 · agent · 2026-09-24T23:11:56.212Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T23:11:56.930Z { "code": "const r = await tools.write_stdin({\"session_id\":38244,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":12000}); text(r.output);\n" } ### Tool output · 2026-09-24T23:12:26.938Z Script completed Wall time 30.0 seconds Output: ### Harness context { "type": "token_usage_record", "thread_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "session_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "root_turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "response_id": "resp_08539f19231b9e9f016ab5ae38fe0487d285c40676612ae904", "usage": { "input_tokens": 81837, "cached_input_tokens": 81280, "cache_write_input_tokens": 0, "output_tokens": 64, "reasoning_output_tokens": 17, "total_tokens": 81901 }, "turn_token_usage": { "input_tokens": 4132853, "cached_input_tokens": 4050304, "cache_write_input_tokens": 0, "output_tokens": 18827, "reasoning_output_tokens": 7099, "total_tokens": 4151680 }, "thread_token_usage": { "input_tokens": 4132853, "cached_input_tokens": 4050304, "cache_write_input_tokens": 0, "output_tokens": 18827, "reasoning_output_tokens": 7099, "total_tokens": 4151680 } } ### exec · 2026-09-24T23:12:29.076Z { "code": "const r = await tools.write_stdin({\"session_id\":38244,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":12000}); text(r.output);\n" } ### Tool output · 2026-09-24T23:12:59.126Z Script completed Wall time 30.0 seconds Output: ### Harness context { "type": "token_usage_record", "thread_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "session_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "root_turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "response_id": "resp_08539f19231b9e9f016ab5ae5b204887d2956d466de48d91a9", "usage": { "input_tokens": 81924, "cached_input_tokens": 81664, "cache_write_input_tokens": 0, "output_tokens": 45, "reasoning_output_tokens": 0, "total_tokens": 81969 }, "turn_token_usage": { "input_tokens": 4214777, "cached_input_tokens": 4131968, "cache_write_input_tokens": 0, "output_tokens": 18872, "reasoning_output_tokens": 7099, "total_tokens": 4233649 }, "thread_token_usage": { "input_tokens": 4214777, "cached_input_tokens": 4131968, "cache_write_input_tokens": 0, "output_tokens": 18872, "reasoning_output_tokens": 7099, "total_tokens": 4233649 } } ### exec · 2026-09-24T23:13:01.582Z { "code": "const r = await tools.write_stdin({\"session_id\":38244,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":12000}); text(r.output);\n" } ### Tool output · 2026-09-24T23:13:31.605Z Script completed Wall time 30.0 seconds Output: ### Harness context { "type": "token_usage_record", "thread_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "session_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "root_turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "response_id": "resp_08539f19231b9e9f016ab5ae7b5d0487d2bdbd2e794602c58a", "usage": { "input_tokens": 81992, "cached_input_tokens": 81792, "cache_write_input_tokens": 0, "output_tokens": 45, "reasoning_output_tokens": 0, "total_tokens": 82037 }, "turn_token_usage": { "input_tokens": 4296769, "cached_input_tokens": 4213760, "cache_write_input_tokens": 0, "output_tokens": 18917, "reasoning_output_tokens": 7099, "total_tokens": 4315686 }, "thread_token_usage": { "input_tokens": 4296769, "cached_input_tokens": 4213760, "cache_write_input_tokens": 0, "output_tokens": 18917, "reasoning_output_tokens": 7099, "total_tokens": 4315686 } } ## Step 84 · agent · 2026-09-24T23:13:32.909Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T23:13:33.688Z { "code": "const r = await tools.write_stdin({\"session_id\":38244,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":12000}); text(r.output);\n" } ### Tool output · 2026-09-24T23:14:03.698Z Script completed Wall time 30.0 seconds Output: ### Harness context { "type": "token_usage_record", "thread_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "session_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "root_turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "response_id": "resp_08539f19231b9e9f016ab5ae9bdbdc87d2ae47809e8ec85c8a", "usage": { "input_tokens": 82060, "cached_input_tokens": 81792, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 82113 }, "turn_token_usage": { "input_tokens": 4378829, "cached_input_tokens": 4295552, "cache_write_input_tokens": 0, "output_tokens": 18970, "reasoning_output_tokens": 7105, "total_tokens": 4397799 }, "thread_token_usage": { "input_tokens": 4378829, "cached_input_tokens": 4295552, "cache_write_input_tokens": 0, "output_tokens": 18970, "reasoning_output_tokens": 7105, "total_tokens": 4397799 } } ## Step 85 · agent · 2026-09-24T23:14:06.129Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T23:14:06.828Z { "code": "const r = await tools.write_stdin({\"session_id\":38244,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":12000}); text(r.output);\n" } ### Tool output · 2026-09-24T23:14:36.872Z Script completed Wall time 30.0 seconds Output: ### Harness context { "type": "token_usage_record", "thread_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "session_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "root_turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "response_id": "resp_08539f19231b9e9f016ab5aebbe98487d28a8b562818d8542e", "usage": { "input_tokens": 82136, "cached_input_tokens": 81920, "cache_write_input_tokens": 0, "output_tokens": 58, "reasoning_output_tokens": 11, "total_tokens": 82194 }, "turn_token_usage": { "input_tokens": 4460965, "cached_input_tokens": 4377472, "cache_write_input_tokens": 0, "output_tokens": 19028, "reasoning_output_tokens": 7116, "total_tokens": 4479993 }, "thread_token_usage": { "input_tokens": 4460965, "cached_input_tokens": 4377472, "cache_write_input_tokens": 0, "output_tokens": 19028, "reasoning_output_tokens": 7116, "total_tokens": 4479993 } } ## Step 86 · agent · 2026-09-24T23:14:43.402Z ### Thinking unavailable — no text recorded The moderated sampler’s starting held-out BCE is `0.619`—challenging enough to add information, but far from the pathological `1.124` of the aggressive mix. Runtime remains within the CPU envelope; I’ll use public NDCG, not internal BCE, as the selection criterion. ### exec · 2026-09-24T23:14:45.406Z { "code": "const r = await tools.write_stdin({\"session_id\":38244,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":12000}); text(r.output);\n" } ### Tool output · 2026-09-24T23:15:15.468Z Script completed Wall time 30.0 seconds Output: epoch=2/2 batches=250 targets=987,417 batch_bce=0.538545 running_bce=0.539800 elapsed=192.8s ### Harness context { "type": "token_usage_record", "thread_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "session_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "root_turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "response_id": "resp_08539f19231b9e9f016ab5aedd36b887d286cfd158cd8d884e", "usage": { "input_tokens": 82217, "cached_input_tokens": 81920, "cache_write_input_tokens": 0, "output_tokens": 128, "reasoning_output_tokens": 13, "total_tokens": 82345 }, "turn_token_usage": { "input_tokens": 4543182, "cached_input_tokens": 4459392, "cache_write_input_tokens": 0, "output_tokens": 19156, "reasoning_output_tokens": 7129, "total_tokens": 4562338 }, "thread_token_usage": { "input_tokens": 4543182, "cached_input_tokens": 4459392, "cache_write_input_tokens": 0, "output_tokens": 19156, "reasoning_output_tokens": 7129, "total_tokens": 4562338 } } ## Step 87 · agent · 2026-09-24T23:15:18.740Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T23:15:19.420Z { "code": "const r = await tools.write_stdin({\"session_id\":38244,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":12000}); text(r.output);\n" } ### Tool output · 2026-09-24T23:15:49.450Z Script completed Wall time 30.0 seconds Output: saved epoch checkpoint: /app/solution/semihard_checkpoints/epoch-02.pt epoch=2/2 train_loss=0.53980 validation_loss=0.55054 pair_accuracy=0.752 train_targets=1,052,953 validation_targets=130,103 train_seconds=205.5 validation_seconds=16.7 restored best validation-BCE checkpoint: epoch=2 loss=0.550539 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "session_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "root_turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "response_id": "resp_08539f19231b9e9f016ab5af03cf8487d297f5893109ef5e09", "usage": { "input_tokens": 82404, "cached_input_tokens": 82048, "cache_write_input_tokens": 0, "output_tokens": 59, "reasoning_output_tokens": 12, "total_tokens": 82463 }, "turn_token_usage": { "input_tokens": 4625586, "cached_input_tokens": 4541440, "cache_write_input_tokens": 0, "output_tokens": 19215, "reasoning_output_tokens": 7141, "total_tokens": 4644801 }, "thread_token_usage": { "input_tokens": 4625586, "cached_input_tokens": 4541440, "cache_write_input_tokens": 0, "output_tokens": 19215, "reasoning_output_tokens": 7141, "total_tokens": 4644801 } } ## Step 88 · agent · 2026-09-24T23:15:51.087Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T23:15:51.747Z { "code": "const r = await tools.write_stdin({\"session_id\":38244,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":12000}); text(r.output);\n" } ### Tool output · 2026-09-24T23:16:21.767Z Script completed Wall time 30.0 seconds Output: ### Harness context { "type": "token_usage_record", "thread_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "session_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "root_turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "response_id": "resp_08539f19231b9e9f016ab5af25c64087d288fc13046c34198c", "usage": { "input_tokens": 82576, "cached_input_tokens": 82176, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 82629 }, "turn_token_usage": { "input_tokens": 4708162, "cached_input_tokens": 4623616, "cache_write_input_tokens": 0, "output_tokens": 19268, "reasoning_output_tokens": 7147, "total_tokens": 4727430 }, "thread_token_usage": { "input_tokens": 4708162, "cached_input_tokens": 4623616, "cache_write_input_tokens": 0, "output_tokens": 19268, "reasoning_output_tokens": 7147, "total_tokens": 4727430 } } ### exec · 2026-09-24T23:16:25.524Z { "code": "const r = await tools.write_stdin({\"session_id\":38244,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":12000}); text(r.output);\n" } ### Tool output · 2026-09-24T23:16:49.889Z Script completed Wall time 24.3 seconds Output: ranked users=1,024/8,456 ranked users=2,048/8,456 ranked users=3,072/8,456 ranked users=4,096/8,456 ranked users=5,120/8,456 ranked users=6,144/8,456 ranked users=7,168/8,456 ranked users=8,192/8,456 ranked users=8,456/8,456 { "baseline": "yambda-windowed-sequence-two-tower-base", "stage": "validation", "output": "/app/solution/semihard_validation.parquet", "rows": 845600, "users": 8456, "runtime_seconds": 326.15, "public_inputs_only": true, "architecture": { "family": "pure-windowed-sequence-two-tower", "sequence_training": "every-adjacent-target-within-recent-chronological-window", "sequence_encoder": "one-layer-GRU-dense-timestep-loss", "bptt_hidden_state": "carried-and-detached", "model_selection": "chronological-validation-BCE-early-stopping", "loss_execution": "gradient-accumulated-microbatches", "user_id_embedding": false, "user_mlp": "582-512-256-192", "item_mlp": "196-512-256-192", "normalization": "none", "score": "raw-dot-product", "objective": "sample-weighted-BCE-at-every-timestep", "negative_sampling": "28-uniform-plus-4-hard-mixed", "false_negative_filter": "full-training-history", "target_weighting": "organic-strong" }, "config": { "bptt_steps": 100, "user_batch_size": 64, "embedding_dim": 192, "hidden_dim": 192, "user_hidden_1": 512, "user_hidden_2": 256, "item_hidden_1": 512, "item_hidden_2": 256, "dropout": 0.05, "epochs": 2, "learning_rate": 0.0001, "weight_decay": 1e-05, "gradient_clip": 2.0, "loss_microbatch_size": 65536, "uniform_negatives": 28, "inbatch_negatives": 4, "negative_sampling": "hard-mixed", "hard_pool_size": 128, "inbatch_correction_power": 0.5, "inbatch_correction_min": 0.25, "inbatch_correction_max": 4.0, "negative_filter_scope": "full-training-history", "target_weighting": "organic-strong", "ordinary_target_weight": 0.25, "organic_strong_target_weight": 1.0, "organic_like_target_weight": 3.0, "discovery_horizon_days": 7, "validation_fraction": 0.1, "max_train_targets_per_user": 128, "max_validation_targets_per_user": 16, "early_stopping_patience": 1, "early_stopping_min_delta": 0.0001, "seed": 29 }, "sequence_tokens": 20056329, "all_next_item_targets": 20047194, "selected_training_targets": 1052953, "selected_validation_targets": 130103, "discarded_old_targets": 18864138, "training_history": [ { "epoch": 1.0, "loss": 0.5398380466771012, "positive_logit": 0.7427799607756806, "negative_logit": -0.6222247067485194, "pair_accuracy": 0.7547733315257186, "targets": 1052953.0, "target_weight_sum": 632551.0, "batches": 266.0, "seconds": 215.029519907, "validation_targets": 130103.0, "validation_target_weight_sum": 78769.25, "validation_seconds": 18.142914074999908, "validation_loss": 0.6188362454237306 }, { "epoch": 2.0, "loss": 0.5398017297449722, "positive_logit": 0.568064952148388, "negative_logit": -1.0288186199283518, "pair_accuracy": 0.7524283788070313, "targets": 1052953.0, "target_weight_sum": 632551.0, "batches": 266.0, "seconds": 205.54811433300006, "validation_loss": 0.5505390212617484, "validation_targets": 130103.0, "validation_target_weight_sum": 78769.25, "validation_seconds": 16.65496691899989 } ], "embedding_diagnostics": { "query_norm_mean": 1.7484843730926514, "query_norm_std": 0.8525374531745911, "item_norm_mean": 2.4714162349700928, "item_norm_std": 1.4270710945129395 }, "torch_version": "2.8.0+cpu", "device": "cpu" } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "session_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "root_turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "response_id": "resp_08539f19231b9e9f016ab5af460db887d2b248194c6d0da12c", "usage": { "input_tokens": 82652, "cached_input_tokens": 82432, "cache_write_input_tokens": 0, "output_tokens": 45, "reasoning_output_tokens": 0, "total_tokens": 82697 }, "turn_token_usage": { "input_tokens": 4790814, "cached_input_tokens": 4706048, "cache_write_input_tokens": 0, "output_tokens": 19313, "reasoning_output_tokens": 7147, "total_tokens": 4810127 }, "thread_token_usage": { "input_tokens": 4790814, "cached_input_tokens": 4706048, "cache_write_input_tokens": 0, "output_tokens": 19313, "reasoning_output_tokens": 7147, "total_tokens": 4810127 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-b4faee1b-0581-4b46-9503-65093012dab5", "process_id": "38244", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python /app/solution/train_current_model.py --stage validation --public-dir /task --prepared-cache /app/solution/validation_sequences.npz --negative-sampling hard-mixed --uniform-negatives 28 --inbatch-negatives 4 --hard-pool-size 128 --epochs 2 --learning-rate 0.0001 --resume-checkpoint /app/solution/baseline_checkpoints/epoch-01.pt --checkpoint-dir /app/solution/semihard_checkpoints --output /app/solution/semihard_validation.parquet 2>&1 | tee /app/solution/semihard_validation.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python /app/solution/train_current_model.py --stage validation --public-dir /task --prepared-cache /app/solution/validation_sequences.npz --negative-sampling hard-mixed --uniform-negatives 28 --inbatch-negatives 4 --hard-pool-size 128 --epochs 2 --learning-rate 0.0001 --resume-checkpoint /app/solution/baseline_checkpoints/epoch-01.pt --checkpoint-dir /app/solution/semihard_checkpoints --output /app/solution/semihard_validation.parquet 2>&1 | tee /app/solution/semihard_validation.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "loaded full-sequence cache: tokens=20,056,329 targets=20,047,194\nchronological BCE window: train=1,052,953 validation=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194\ninbatch correction: min=0.250 mean_on_targets=1.001 max=4.000\ntarget weighting=organic-strong min=0.250 mean=0.575 max=1.000\nfull-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250\nusing a fresh AdamW optimizer after loading /app/solution/baseline_checkpoints/epoch-01.pt\nBCE objective changed: filter full-training-history->full-training-history, target weighting organic-strong->organic-strong, target window (0.1, 128, 16)->(0.1, 128, 16); negative sampler ('mixed', 24, 8, 512)->('hard-mixed', 28, 4, 128); recomputing the resume checkpoint's validation BCE\nresumed validation-BCE training: epoch=1 loss=0.618836\nepoch=2/2 batches=250 targets=987,417 batch_bce=0.538545 running_bce=0.539800 elapsed=192.8s\nsaved epoch checkpoint: /app/solution/semihard_checkpoints/epoch-02.pt\nepoch=2/2 train_loss=0.53980 validation_loss=0.55054 pair_accuracy=0.752 train_targets=1,052,953 validation_targets=130,103 train_seconds=205.5 validation_seconds=16.7\nrestored best validation-BCE checkpoint: epoch=2 loss=0.550539\nranked users=1,024/8,456\nranked users=2,048/8,456\nranked users=3,072/8,456\nranked users=4,096/8,456\nranked users=5,120/8,456\nranked users=6,144/8,456\nranked users=7,168/8,456\nranked users=8,192/8,456\nranked users=8,456/8,456\n{\n \"baseline\": \"yambda-windowed-sequence-two-tower-base\",\n \"stage\": \"validation\",\n \"output\": \"/app/solution/semihard_validation.parquet\",\n \"rows\": 845600,\n \"users\": 8456,\n \"runtime_seconds\": 326.15,\n \"public_inputs_only\": true,\n \"architecture\": {\n \"family\": \"pure-windowed-sequence-two-tower\",\n \"sequence_training\": \"every-adjacent-target-within-recent-chronological-window\",\n \"sequence_encoder\": \"one-layer-GRU-dense-timestep-loss\",\n \"bptt_hidden_state\": \"carried-and-detached\",\n \"model_selection\": \"chronological-validation-BCE-early-stopping\",\n \"loss_execution\": \"gradient-accumulated-microbatches\",\n \"user_id_embedding\": false,\n \"user_mlp\": \"582-512-256-192\",\n \"item_mlp\": \"196-512-256-192\",\n \"normalization\": \"none\",\n \"score\": \"raw-dot-product\",\n \"objective\": \"sample-weighted-BCE-at-every-timestep\",\n \"negative_sampling\": \"28-uniform-plus-4-hard-mixed\",\n \"false_negative_filter\": \"full-training-history\",\n \"target_weighting\": \"organic-strong\"\n },\n \"config\": {\n \"bptt_steps\": 100,\n \"user_batch_size\": 64,\n \"embedding_dim\": 192,\n \"hidden_dim\": 192,\n \"user_hidden_1\": 512,\n \"user_hidden_2\": 256,\n \"item_hidden_1\": 512,\n \"item_hidden_2\": 256,\n \"dropout\": 0.05,\n \"epochs\": 2,\n \"learning_rate\": 0.0001,\n \"weight_decay\": 1e-05,\n \"gradient_clip\": 2.0,\n \"loss_microbatch_size\": 65536,\n \"uniform_negatives\": 28,\n \"inbatch_negatives\": 4,\n \"negative_sampling\": \"hard-mixed\",\n \"hard_pool_size\": 128,\n \"inbatch_correction_power\": 0.5,\n \"inbatch_correction_min\": 0.25,\n \"inbatch_correction_max\": 4.0,\n \"negative_filter_scope\": \"full-training-history\",\n \"target_weighting\": \"organic-strong\",\n \"ordinary_target_weight\": 0.25,\n \"organic_strong_target_weight\": 1.0,\n \"organic_like_target_weight\": 3.0,\n \"discovery_horizon_days\": 7,\n \"validation_fraction\": 0.1,\n \"max_train_targets_per_user\": 128,\n \"max_validation_targets_per_user\": 16,\n \"early_stopping_patience\": 1,\n \"early_stopping_min_delta\": 0.0001,\n \"seed\": 29\n },\n \"sequence_tokens\": 20056329,\n \"all_next_item_targets\": 20047194,\n \"selected_training_targets\": 1052953,\n \"selected_validation_targets\": 130103,\n \"discarded_old_targets\": 18864138,\n \"training_history\": [\n {\n \"epoch\": 1.0,\n \"loss\": 0.5398380466771012,\n \"positive_logit\": 0.7427799607756806,\n \"negative_logit\": -0.6222247067485194,\n \"pair_accuracy\": 0.7547733315257186,\n \"targets\": 1052953.0,\n \"target_weight_sum\": 632551.0,\n \"batches\": 266.0,\n \"seconds\": 215.029519907,\n \"validation_targets\": 130103.0,\n \"validation_target_weight_sum\": 78769.25,\n \"validation_seconds\": 18.142914074999908,\n \"validation_loss\": 0.6188362454237306\n },\n {\n \"epoch\": 2.0,\n \"loss\": 0.5398017297449722,\n \"positive_logit\": 0.568064952148388,\n \"negative_logit\": -1.0288186199283518,\n \"pair_accuracy\": 0.7524283788070313,\n \"targets\": 1052953.0,\n \"target_weight_sum\": 632551.0,\n \"batches\": 266.0,\n \"seconds\": 205.54811433300006,\n \"validation_loss\": 0.5505390212617484,\n \"validation_targets\": 130103.0,\n \"validation_target_weight_sum\": 78769.25,\n \"validation_seconds\": 16.65496691899989\n }\n ],\n \"embedding_diagnostics\": {\n \"query_norm_mean\": 1.7484843730926514,\n \"query_norm_std\": 0.8525374531745911,\n \"item_norm_mean\": 2.4714162349700928,\n \"item_norm_std\": 1.4270710945129395\n },\n \"torch_version\": \"2.8.0+cpu\",\n \"device\": \"cpu\"\n}\n", "stderr": "", "aggregated_output": "loaded full-sequence cache: tokens=20,056,329 targets=20,047,194\nchronological BCE window: train=1,052,953 validation=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194\ninbatch correction: min=0.250 mean_on_targets=1.001 max=4.000\ntarget weighting=organic-strong min=0.250 mean=0.575 max=1.000\nfull-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250\nusing a fresh AdamW optimizer after loading /app/solution/baseline_checkpoints/epoch-01.pt\nBCE objective changed: filter full-training-history->full-training-history, target weighting organic-strong->organic-strong, target window (0.1, 128, 16)->(0.1, 128, 16); negative sampler ('mixed', 24, 8, 512)->('hard-mixed', 28, 4, 128); recomputing the resume checkpoint's validation BCE\nresumed validation-BCE training: epoch=1 loss=0.618836\nepoch=2/2 batches=250 targets=987,417 batch_bce=0.538545 running_bce=0.539800 elapsed=192.8s\nsaved epoch checkpoint: /app/solution/semihard_checkpoints/epoch-02.pt\nepoch=2/2 train_loss=0.53980 validation_loss=0.55054 pair_accuracy=0.752 train_targets=1,052,953 validation_targets=130,103 train_seconds=205.5 validation_seconds=16.7\nrestored best validation-BCE checkpoint: epoch=2 loss=0.550539\nranked users=1,024/8,456\nranked users=2,048/8,456\nranked users=3,072/8,456\nranked users=4,096/8,456\nranked users=5,120/8,456\nranked users=6,144/8,456\nranked users=7,168/8,456\nranked users=8,192/8,456\nranked users=8,456/8,456\n{\n \"baseline\": \"yambda-windowed-sequence-two-tower-base\",\n \"stage\": \"validation\",\n \"output\": \"/app/solution/semihard_validation.parquet\",\n \"rows\": 845600,\n \"users\": 8456,\n \"runtime_seconds\": 326.15,\n \"public_inputs_only\": true,\n \"architecture\": {\n \"family\": \"pure-windowed-sequence-two-tower\",\n \"sequence_training\": \"every-adjacent-target-within-recent-chronological-window\",\n \"sequence_encoder\": \"one-layer-GRU-dense-timestep-loss\",\n \"bptt_hidden_state\": \"carried-and-detached\",\n \"model_selection\": \"chronological-validation-BCE-early-stopping\",\n \"loss_execution\": \"gradient-accumulated-microbatches\",\n \"user_id_embedding\": false,\n \"user_mlp\": \"582-512-256-192\",\n \"item_mlp\": \"196-512-256-192\",\n \"normalization\": \"none\",\n \"score\": \"raw-dot-product\",\n \"objective\": \"sample-weighted-BCE-at-every-timestep\",\n \"negative_sampling\": \"28-uniform-plus-4-hard-mixed\",\n \"false_negative_filter\": \"full-training-history\",\n \"target_weighting\": \"organic-strong\"\n },\n \"config\": {\n \"bptt_steps\": 100,\n \"user_batch_size\": 64,\n \"embedding_dim\": 192,\n \"hidden_dim\": 192,\n \"user_hidden_1\": 512,\n \"user_hidden_2\": 256,\n \"item_hidden_1\": 512,\n \"item_hidden_2\": 256,\n \"dropout\": 0.05,\n \"epochs\": 2,\n \"learning_rate\": 0.0001,\n \"weight_decay\": 1e-05,\n \"gradient_clip\": 2.0,\n \"loss_microbatch_size\": 65536,\n \"uniform_negatives\": 28,\n \"inbatch_negatives\": 4,\n \"negative_sampling\": \"hard-mixed\",\n \"hard_pool_size\": 128,\n \"inbatch_correction_power\": 0.5,\n \"inbatch_correction_min\": 0.25,\n \"inbatch_correction_max\": 4.0,\n \"negative_filter_scope\": \"full-training-history\",\n \"target_weighting\": \"organic-strong\",\n \"ordinary_target_weight\": 0.25,\n \"organic_strong_target_weight\": 1.0,\n \"organic_like_target_weight\": 3.0,\n \"discovery_horizon_days\": 7,\n \"validation_fraction\": 0.1,\n \"max_train_targets_per_user\": 128,\n \"max_validation_targets_per_user\": 16,\n \"early_stopping_patience\": 1,\n \"early_stopping_min_delta\": 0.0001,\n \"seed\": 29\n },\n \"sequence_tokens\": 20056329,\n \"all_next_item_targets\": 20047194,\n \"selected_training_targets\": 1052953,\n \"selected_validation_targets\": 130103,\n \"discarded_old_targets\": 18864138,\n \"training_history\": [\n {\n \"epoch\": 1.0,\n \"loss\": 0.5398380466771012,\n \"positive_logit\": 0.7427799607756806,\n \"negative_logit\": -0.6222247067485194,\n \"pair_accuracy\": 0.7547733315257186,\n \"targets\": 1052953.0,\n \"target_weight_sum\": 632551.0,\n \"batches\": 266.0,\n \"seconds\": 215.029519907,\n \"validation_targets\": 130103.0,\n \"validation_target_weight_sum\": 78769.25,\n \"validation_seconds\": 18.142914074999908,\n \"validation_loss\": 0.6188362454237306\n },\n {\n \"epoch\": 2.0,\n \"loss\": 0.5398017297449722,\n \"positive_logit\": 0.568064952148388,\n \"negative_logit\": -1.0288186199283518,\n \"pair_accuracy\": 0.7524283788070313,\n \"targets\": 1052953.0,\n \"target_weight_sum\": 632551.0,\n \"batches\": 266.0,\n \"seconds\": 205.54811433300006,\n \"validation_loss\": 0.5505390212617484,\n \"validation_targets\": 130103.0,\n \"validation_target_weight_sum\": 78769.25,\n \"validation_seconds\": 16.65496691899989\n }\n ],\n \"embedding_diagnostics\": {\n \"query_norm_mean\": 1.7484843730926514,\n \"query_norm_std\": 0.8525374531745911,\n \"item_norm_mean\": 2.4714162349700928,\n \"item_norm_std\": 1.4270710945129395\n },\n \"torch_version\": \"2.8.0+cpu\",\n \"device\": \"cpu\"\n}\n", "exit_code": 0, "duration": { "secs": 330, "nanos": 519816878 }, "formatted_output": "loaded full-sequence cache: tokens=20,056,329 targets=20,047,194\nchronological BCE window: train=1,052,953 validation=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194\ninbatch correction: min=0.250 mean_on_targets=1.001 max=4.000\ntarget weighting=organic-strong min=0.250 mean=0.575 max=1.000\nfull-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250\nusing a fresh AdamW optimizer after loading /app/solution/baseline_checkpoints/epoch-01.pt\nBCE objective changed: filter full-training-history->full-training-history, target weighting organic-strong->organic-strong, target window (0.1, 128, 16)->(0.1, 128, 16); negative sampler ('mixed', 24, 8, 512)->('hard-mixed', 28, 4, 128); recomputing the resume checkpoint's validation BCE\nresumed validation-BCE training: epoch=1 loss=0.618836\nepoch=2/2 batches=250 targets=987,417 batch_bce=0.538545 running_bce=0.539800 elapsed=192.8s\nsaved epoch checkpoint: /app/solution/semihard_checkpoints/epoch-02.pt\nepoch=2/2 train_loss=0.53980 validation_loss=0.55054 pair_accuracy=0.752 train_targets=1,052,953 validation_targets=130,103 train_seconds=205.5 validation_seconds=16.7\nrestored best validation-BCE checkpoint: epoch=2 loss=0.550539\nranked users=1,024/8,456\nranked users=2,048/8,456\nranked users=3,072/8,456\nranked users=4,096/8,456\nranked users=5,120/8,456\nranked users=6,144/8,456\nranked users=7,168/8,456\nranked users=8,192/8,456\nranked users=8,456/8,456\n{\n \"baseline\": \"yambda-windowed-sequence-two-tower-base\",\n \"stage\": \"validation\",\n \"output\": \"/app/solution/semihard_validation.parquet\",\n \"rows\": 845600,\n \"users\": 8456,\n \"runtime_seconds\": 326.15,\n \"public_inputs_only\": true,\n \"architecture\": {\n \"family\": \"pure-windowed-sequence-two-tower\",\n \"sequence_training\": \"every-adjacent-target-within-recent-chronological-window\",\n \"sequence_encoder\": \"one-layer-GRU-dense-timestep-loss\",\n \"bptt_hidden_state\": \"carried-and-detached\",\n \"model_selection\": \"chronological-validation-BCE-early-stopping\",\n \"loss_execution\": \"gradient-accumulated-microbatches\",\n \"user_id_embedding\": false,\n \"user_mlp\": \"582-512-256-192\",\n \"item_mlp\": \"196-512-256-192\",\n \"normalization\": \"none\",\n \"score\": \"raw-dot-product\",\n \"objective\": \"sample-weighted-BCE-at-every-timestep\",\n \"negative_sampling\": \"28-uniform-plus-4-hard-mixed\",\n \"false_negative_filter\": \"full-training-history\",\n \"target_weighting\": \"organic-strong\"\n },\n \"config\": {\n \"bptt_steps\": 100,\n \"user_batch_size\": 64,\n \"embedding_dim\": 192,\n \"hidden_dim\": 192,\n \"user_hidden_1\": 512,\n \"user_hidden_2\": 256,\n \"item_hidden_1\": 512,\n \"item_hidden_2\": 256,\n \"dropout\": 0.05,\n \"epochs\": 2,\n \"learning_rate\": 0.0001,\n \"weight_decay\": 1e-05,\n \"gradient_clip\": 2.0,\n \"loss_microbatch_size\": 65536,\n \"uniform_negatives\": 28,\n \"inbatch_negatives\": 4,\n \"negative_sampling\": \"hard-mixed\",\n \"hard_pool_size\": 128,\n \"inbatch_correction_power\": 0.5,\n \"inbatch_correction_min\": 0.25,\n \"inbatch_correction_max\": 4.0,\n \"negative_filter_scope\": \"full-training-history\",\n \"target_weighting\": \"organic-strong\",\n \"ordinary_target_weight\": 0.25,\n \"organic_strong_target_weight\": 1.0,\n \"organic_like_target_weight\": 3.0,\n \"discovery_horizon_days\": 7,\n \"validation_fraction\": 0.1,\n \"max_train_targets_per_user\": 128,\n \"max_validation_targets_per_user\": 16,\n \"early_stopping_patience\": 1,\n \"early_stopping_min_delta\": 0.0001,\n \"seed\": 29\n },\n \"sequence_tokens\": 20056329,\n \"all_next_item_targets\": 20047194,\n \"selected_training_targets\": 1052953,\n \"selected_validation_targets\": 130103,\n \"discarded_old_targets\": 18864138,\n \"training_history\": [\n {\n \"epoch\": 1.0,\n \"loss\": 0.5398380466771012,\n \"positive_logit\": 0.7427799607756806,\n \"negative_logit\": -0.6222247067485194,\n \"pair_accuracy\": 0.7547733315257186,\n \"targets\": 1052953.0,\n \"target_weight_sum\": 632551.0,\n \"batches\": 266.0,\n \"seconds\": 215.029519907,\n \"validation_targets\": 130103.0,\n \"validation_target_weight_sum\": 78769.25,\n \"validation_seconds\": 18.142914074999908,\n \"validation_loss\": 0.6188362454237306\n },\n {\n \"epoch\": 2.0,\n \"loss\": 0.5398017297449722,\n \"positive_logit\": 0.568064952148388,\n \"negative_logit\": -1.0288186199283518,\n \"pair_accuracy\": 0.7524283788070313,\n \"targets\": 1052953.0,\n \"target_weight_sum\": 632551.0,\n \"batches\": 266.0,\n \"seconds\": 205.54811433300006,\n \"validation_loss\": 0.5505390212617484,\n \"validation_targets\": 130103.0,\n \"validation_target_weight_sum\": 78769.25,\n \"validation_seconds\": 16.65496691899989\n }\n ],\n \"embedding_diagnostics\": {\n \"query_norm_mean\": 1.7484843730926514,\n \"query_norm_std\": 0.8525374531745911,\n \"item_norm_mean\": 2.4714162349700928,\n \"item_norm_std\": 1.4270710945129395\n },\n \"torch_version\": \"2.8.0+cpu\",\n \"device\": \"cpu\"\n}\n" } } ## Step 89 · agent · 2026-09-24T23:16:51.198Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T23:16:54.770Z { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/semihard_validation.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":5000}); text(r.output);" } ### Tool output · 2026-09-24T23:16:58.220Z Script completed Wall time 3.4 seconds Output: { "model": "/app/solution/semihard_validation.parquet", "scores": { "quality_ndcg@10": 0.0084, "quality_users": 4263 } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "session_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "root_turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "response_id": "resp_08539f19231b9e9f016ab5af6218c087d2a341c81b5c93a83d", "usage": { "input_tokens": 83968, "cached_input_tokens": 82432, "cache_write_input_tokens": 0, "output_tokens": 78, "reasoning_output_tokens": 6, "total_tokens": 84046 }, "turn_token_usage": { "input_tokens": 4874782, "cached_input_tokens": 4788480, "cache_write_input_tokens": 0, "output_tokens": 19391, "reasoning_output_tokens": 7153, "total_tokens": 4894173 }, "thread_token_usage": { "input_tokens": 4874782, "cached_input_tokens": 4788480, "cache_write_input_tokens": 0, "output_tokens": 19391, "reasoning_output_tokens": 7153, "total_tokens": 4894173 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-386e9ce6-c289-4d4b-9385-114b2d52617b", "process_id": "22158", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/semihard_validation.parquet" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/semihard_validation.parquet" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{\n \"model\": \"/app/solution/semihard_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.0084,\n \"quality_users\": 4263\n }\n}\n", "stderr": "", "aggregated_output": "{\n \"model\": \"/app/solution/semihard_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.0084,\n \"quality_users\": 4263\n }\n}\n", "exit_code": 0, "duration": { "secs": 3, "nanos": 269386950 }, "formatted_output": "{\n \"model\": \"/app/solution/semihard_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.0084,\n \"quality_users\": 4263\n }\n}\n" } } ## Step 90 · agent · 2026-09-24T23:17:10.291Z ### Thinking unavailable — no text recorded ## Step 91 · agent · 2026-09-24T23:17:18.060Z ### Thinking unavailable — no text recorded The small hard-negative term still hurts (`0.008400`), and its embedding norms grow sharply. That isolates the deeper issue: pointwise BCE asks for absolute negative logits, so even a few mined extremes distort vector magnitudes instead of improving relative order. I’m changing only the loss’s use of those same negatives to a pairwise softplus on `(negative score − positive score)`, which directly matches retrieval ordering and is invariant to shared score offsets. ### exec · 2026-09-24T23:17:25.217Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/train_current_model.py\\n@@\\n class Config:\\n@@\\n hard_pool_size: int = 512\\n+ retrieval_loss: str = \\\"bce\\\"\\n@@\\n def corrected_negative_loss(\\n logits: torch.Tensor,\\n correction: torch.Tensor,\\n config: Config,\\n ) -> torch.Tensor:\\n- raw = F.softplus(logits)\\n+ return corrected_negative_average(F.softplus(logits), correction, config)\\n+\\n+\\n+def corrected_negative_average(\\n+ raw: torch.Tensor,\\n+ correction: torch.Tensor,\\n+ config: Config,\\n+) -> torch.Tensor:\\n+ \\\"\\\"\\\"Average per-negative losses while preserving sampler strata weights.\\\"\\\"\\\"\\n total = config.uniform_negatives + config.inbatch_negatives\\n@@\\n- positive_loss = F.softplus(-positive_logits)\\n- negative_loss = corrected_negative_loss(\\n- negative_logits, negative_weights[begin:end], config\\n- )\\n+ if config.retrieval_loss == \\\"pairwise\\\":\\n+ positive_loss = torch.zeros_like(positive_logits)\\n+ negative_loss = corrected_negative_average(\\n+ F.softplus(negative_logits - positive_logits[:, None]),\\n+ negative_weights[begin:end],\\n+ config,\\n+ )\\n+ loss_scale = 1.0\\n+ else:\\n+ positive_loss = F.softplus(-positive_logits)\\n+ negative_loss = corrected_negative_loss(\\n+ negative_logits, negative_weights[begin:end], config\\n+ )\\n+ loss_scale = 0.5\\n@@\\n- selected_weights * (positive_loss + negative_loss) / 2.0\\n+ selected_weights * (positive_loss + negative_loss) * loss_scale\\n*** End Patch\"; text(await tools.apply_patch(patch));" } ### Tool output · 2026-09-24T23:17:25.329Z Script completed Wall time 0.0 seconds Output: {} ### Harness context { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-79e84fb7-f70e-4cef-bfaf-f29ab0a412d5", "changes": { "/app/solution/train_current_model.py": { "type": "update", "unified_diff": "@@ -59,2 +59,3 @@\n hard_pool_size: int = 512\n+ retrieval_loss: str = \"bce\"\n inbatch_correction_power: float = 0.5\n@@ -1012,3 +1013,11 @@\n ) -> torch.Tensor:\n- raw = F.softplus(logits)\n+ return corrected_negative_average(F.softplus(logits), correction, config)\n+\n+\n+def corrected_negative_average(\n+ raw: torch.Tensor,\n+ correction: torch.Tensor,\n+ config: Config,\n+) -> torch.Tensor:\n+ \"\"\"Average per-negative losses while preserving sampler strata weights.\"\"\"\n total = config.uniform_negatives + config.inbatch_negatives\n@@ -1333,6 +1342,16 @@\n )\n- positive_loss = F.softplus(-positive_logits)\n- negative_loss = corrected_negative_loss(\n- negative_logits, negative_weights[begin:end], config\n- )\n+ if config.retrieval_loss == \"pairwise\":\n+ positive_loss = torch.zeros_like(positive_logits)\n+ negative_loss = corrected_negative_average(\n+ F.softplus(negative_logits - positive_logits[:, None]),\n+ negative_weights[begin:end],\n+ config,\n+ )\n+ loss_scale = 1.0\n+ else:\n+ positive_loss = F.softplus(-positive_logits)\n+ negative_loss = corrected_negative_loss(\n+ negative_logits, negative_weights[begin:end], config\n+ )\n+ loss_scale = 0.5\n selected_weights = torch.from_numpy(\n@@ -1341,3 +1360,3 @@\n loss_sum = (\n- selected_weights * (positive_loss + negative_loss) / 2.0\n+ selected_weights * (positive_loss + negative_loss) * loss_scale\n ).sum()\n", "move_path": null } }, "status": "completed", "stdout": "Success. Updated the following files:\nM /app/solution/train_current_model.py\n", "stderr": "" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "session_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "root_turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "response_id": "resp_08539f19231b9e9f016ab5af6a775c87d29b3c1aab98569944", "usage": { "input_tokens": 84117, "cached_input_tokens": 83840, "cache_write_input_tokens": 0, "output_tokens": 1440, "reasoning_output_tokens": 909, "total_tokens": 85557 }, "turn_token_usage": { "input_tokens": 4958899, "cached_input_tokens": 4872320, "cache_write_input_tokens": 0, "output_tokens": 20831, "reasoning_output_tokens": 8062, "total_tokens": 4979730 }, "thread_token_usage": { "input_tokens": 4958899, "cached_input_tokens": 4872320, "cache_write_input_tokens": 0, "output_tokens": 20831, "reasoning_output_tokens": 8062, "total_tokens": 4979730 } } ## Step 92 · agent · 2026-09-24T23:17:26.814Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T23:17:30.186Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/train_current_model.py\\n@@\\n parser.add_argument(\\\"--hard-pool-size\\\", type=int, default=Config.hard_pool_size)\\n+ parser.add_argument(\\n+ \\\"--retrieval-loss\\\",\\n+ choices=(\\\"bce\\\", \\\"pairwise\\\"),\\n+ default=Config.retrieval_loss,\\n+ )\\n@@\\n hard_pool_size=args.hard_pool_size,\\n+ retrieval_loss=args.retrieval_loss,\\n@@\\n- \\\"objective\\\": \\\"sample-weighted-BCE-at-every-timestep\\\",\\n+ \\\"objective\\\": (\\n+ f\\\"sample-weighted-{config.retrieval_loss}-at-every-timestep\\\"\\n+ ),\\n*** End Patch\"; text(await tools.apply_patch(patch));" } ### Tool output · 2026-09-24T23:17:30.314Z Script completed Wall time 0.0 seconds Output: {} ### Harness context { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-df47b8e7-674c-43cf-819a-4a8f886d7259", "changes": { "/app/solution/train_current_model.py": { "type": "update", "unified_diff": "@@ -2167,2 +2167,7 @@\n parser.add_argument(\n+ \"--retrieval-loss\",\n+ choices=(\"bce\", \"pairwise\"),\n+ default=Config.retrieval_loss,\n+ )\n+ parser.add_argument(\n \"--target-weighting\",\n@@ -2259,2 +2264,3 @@\n hard_pool_size=args.hard_pool_size,\n+ retrieval_loss=args.retrieval_loss,\n negative_filter_scope=args.negative_filter_scope,\n@@ -2360,3 +2366,5 @@\n \"score\": \"raw-dot-product\",\n- \"objective\": \"sample-weighted-BCE-at-every-timestep\",\n+ \"objective\": (\n+ f\"sample-weighted-{config.retrieval_loss}-at-every-timestep\"\n+ ),\n \"negative_sampling\": (\n", "move_path": null } }, "status": "completed", "stdout": "Success. Updated the following files:\nM /app/solution/train_current_model.py\n", "stderr": "" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "session_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "root_turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "response_id": "resp_08539f19231b9e9f016ab5af85a18c87d2a412d7b275eace3a", "usage": { "input_tokens": 85581, "cached_input_tokens": 83968, "cache_write_input_tokens": 0, "output_tokens": 205, "reasoning_output_tokens": 9, "total_tokens": 85786 }, "turn_token_usage": { "input_tokens": 5044480, "cached_input_tokens": 4956288, "cache_write_input_tokens": 0, "output_tokens": 21036, "reasoning_output_tokens": 8071, "total_tokens": 5065516 }, "thread_token_usage": { "input_tokens": 5044480, "cached_input_tokens": 4956288, "cache_write_input_tokens": 0, "output_tokens": 21036, "reasoning_output_tokens": 8071, "total_tokens": 5065516 } } ## Step 93 · agent · 2026-09-24T23:17:32.291Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T23:17:35.126Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/train_current_model.py\\n@@\\n stored_negative_values = (\\n stored_config.get(\\\"negative_sampling\\\", \\\"mixed\\\"),\\n stored_config.get(\\\"uniform_negatives\\\", 24),\\n stored_config.get(\\\"inbatch_negatives\\\", 8),\\n stored_config.get(\\\"hard_pool_size\\\", 512),\\n+ stored_config.get(\\\"retrieval_loss\\\", \\\"bce\\\"),\\n )\\n@@\\n config.inbatch_negatives,\\n config.hard_pool_size,\\n+ config.retrieval_loss,\\n )\\n*** End Patch\"; text(await tools.apply_patch(patch));" } ### Tool output · 2026-09-24T23:17:35.265Z Script completed Wall time 0.0 seconds Output: {} ### Harness context { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-a11aa285-e3bf-43df-9150-e63b7f082637", "changes": { "/app/solution/train_current_model.py": { "type": "update", "unified_diff": "@@ -1824,2 +1824,3 @@\n stored_config.get(\"hard_pool_size\", 512),\n+ stored_config.get(\"retrieval_loss\", \"bce\"),\n )\n@@ -1830,2 +1831,3 @@\n config.hard_pool_size,\n+ config.retrieval_loss,\n )\n", "move_path": null } }, "status": "completed", "stdout": "Success. Updated the following files:\nM /app/solution/train_current_model.py\n", "stderr": "" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "session_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "root_turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "response_id": "resp_08539f19231b9e9f016ab5af8aad5087d2989775b3c65a7252", "usage": { "input_tokens": 85810, "cached_input_tokens": 85376, "cache_write_input_tokens": 0, "output_tokens": 176, "reasoning_output_tokens": 12, "total_tokens": 85986 }, "turn_token_usage": { "input_tokens": 5130290, "cached_input_tokens": 5041664, "cache_write_input_tokens": 0, "output_tokens": 21212, "reasoning_output_tokens": 8083, "total_tokens": 5151502 }, "thread_token_usage": { "input_tokens": 5130290, "cached_input_tokens": 5041664, "cache_write_input_tokens": 0, "output_tokens": 21212, "reasoning_output_tokens": 8083, "total_tokens": 5151502 } } ## Step 94 · agent · 2026-09-24T23:17:38.649Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T23:17:42.215Z { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python -m py_compile /app/solution/train_current_model.py\\n/opt/task-venv/bin/python /app/solution/train_current_model.py --stage validation --public-dir /task --prepared-cache /app/solution/validation_sequences.npz --negative-sampling hard-mixed --uniform-negatives 28 --inbatch-negatives 4 --hard-pool-size 128 --retrieval-loss pairwise --epochs 2 --learning-rate 0.0001 --resume-checkpoint /app/solution/baseline_checkpoints/epoch-01.pt --checkpoint-dir /app/solution/pairwise_checkpoints --output /app/solution/pairwise_validation.parquet 2>&1 | tee /app/solution/pairwise_validation.log\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":12000}); text(JSON.stringify(r));" } ### Tool output · 2026-09-24T23:17:43.388Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"70f8c2","wall_time_seconds":1.000915751,"session_id":15745,"original_token_count":0,"output":""} ### Harness context { "type": "token_usage_record", "thread_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "session_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "root_turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "response_id": "resp_08539f19231b9e9f016ab5af8f8e8887d2be647ec392cc13eb", "usage": { "input_tokens": 86010, "cached_input_tokens": 85632, "cache_write_input_tokens": 0, "output_tokens": 317, "reasoning_output_tokens": 110, "total_tokens": 86327 }, "turn_token_usage": { "input_tokens": 5216300, "cached_input_tokens": 5127296, "cache_write_input_tokens": 0, "output_tokens": 21529, "reasoning_output_tokens": 8193, "total_tokens": 5237829 }, "thread_token_usage": { "input_tokens": 5216300, "cached_input_tokens": 5127296, "cache_write_input_tokens": 0, "output_tokens": 21529, "reasoning_output_tokens": 8193, "total_tokens": 5237829 } } ## Step 95 · agent · 2026-09-24T23:17:44.649Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T23:17:45.362Z { "code": "const r = await tools.write_stdin({\"session_id\":15745,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":12000}); text(r.output);\n" } ### Tool output · 2026-09-24T23:17:51.921Z Script completed Wall time 6.6 seconds Output: loaded full-sequence cache: tokens=20,056,329 targets=20,047,194 chronological BCE window: train=1,052,953 validation=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194 inbatch correction: min=0.250 mean_on_targets=1.001 max=4.000 target weighting=organic-strong min=0.250 mean=0.575 max=1.000 full-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250 using a fresh AdamW optimizer after loading /app/solution/baseline_checkpoints/epoch-01.pt BCE objective changed: filter full-training-history->full-training-history, target weighting organic-strong->organic-strong, target window (0.1, 128, 16)->(0.1, 128, 16); negative sampler ('mixed', 24, 8, 512, 'bce')->('hard-mixed', 28, 4, 128, 'pairwise'); recomputing the resume checkpoint's validation BCE Traceback (most recent call last): File "/app/solution/train_current_model.py", line 2402, in raise SystemExit(main()) ^^^^^^ File "/app/solution/train_current_model.py", line 2327, in main model, training_history = train_model( ^^^^^^^^^^^^ File "/app/solution/train_current_model.py", line 1877, in train_model evaluate_validation_bce( File "/opt/task-venv/lib/python3.12/site-packages/torch/utils/_contextlib.py", line 120, in decorate_context return func(*args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^ File "/app/solution/train_current_model.py", line 1653, in evaluate_validation_bce statistics, _ = score_target_microbatches( ^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/app/solution/train_current_model.py", line 1345, in score_target_microbatches negative_loss = corrected_negative_average( ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/app/solution/train_current_model.py", line 1024, in corrected_negative_average result = torch.zeros(len(logits), device=logits.device) ^^^^^^ NameError: name 'logits' is not defined ### Harness context { "type": "token_usage_record", "thread_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "session_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "root_turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "response_id": "resp_08539f19231b9e9f016ab5af979ec487d2b4d96d5c28fe03d6", "usage": { "input_tokens": 86386, "cached_input_tokens": 85888, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 86439 }, "turn_token_usage": { "input_tokens": 5302686, "cached_input_tokens": 5213184, "cache_write_input_tokens": 0, "output_tokens": 21582, "reasoning_output_tokens": 8199, "total_tokens": 5324268 }, "thread_token_usage": { "input_tokens": 5302686, "cached_input_tokens": 5213184, "cache_write_input_tokens": 0, "output_tokens": 21582, "reasoning_output_tokens": 8199, "total_tokens": 5324268 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-ba92f236-8586-4834-a997-ac3995ba4eb6", "process_id": "15745", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python -m py_compile /app/solution/train_current_model.py\n/opt/task-venv/bin/python /app/solution/train_current_model.py --stage validation --public-dir /task --prepared-cache /app/solution/validation_sequences.npz --negative-sampling hard-mixed --uniform-negatives 28 --inbatch-negatives 4 --hard-pool-size 128 --retrieval-loss pairwise --epochs 2 --learning-rate 0.0001 --resume-checkpoint /app/solution/baseline_checkpoints/epoch-01.pt --checkpoint-dir /app/solution/pairwise_checkpoints --output /app/solution/pairwise_validation.parquet 2>&1 | tee /app/solution/pairwise_validation.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python -m py_compile /app/solution/train_current_model.py\n/opt/task-venv/bin/python /app/solution/train_current_model.py --stage validation --public-dir /task --prepared-cache /app/solution/validation_sequences.npz --negative-sampling hard-mixed --uniform-negatives 28 --inbatch-negatives 4 --hard-pool-size 128 --retrieval-loss pairwise --epochs 2 --learning-rate 0.0001 --resume-checkpoint /app/solution/baseline_checkpoints/epoch-01.pt --checkpoint-dir /app/solution/pairwise_checkpoints --output /app/solution/pairwise_validation.parquet 2>&1 | tee /app/solution/pairwise_validation.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "loaded full-sequence cache: tokens=20,056,329 targets=20,047,194\nchronological BCE window: train=1,052,953 validation=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194\ninbatch correction: min=0.250 mean_on_targets=1.001 max=4.000\ntarget weighting=organic-strong min=0.250 mean=0.575 max=1.000\nfull-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250\nusing a fresh AdamW optimizer after loading /app/solution/baseline_checkpoints/epoch-01.pt\nBCE objective changed: filter full-training-history->full-training-history, target weighting organic-strong->organic-strong, target window (0.1, 128, 16)->(0.1, 128, 16); negative sampler ('mixed', 24, 8, 512, 'bce')->('hard-mixed', 28, 4, 128, 'pairwise'); recomputing the resume checkpoint's validation BCE\nTraceback (most recent call last):\n File \"/app/solution/train_current_model.py\", line 2402, in \n raise SystemExit(main())\n ^^^^^^\n File \"/app/solution/train_current_model.py\", line 2327, in main\n model, training_history = train_model(\n ^^^^^^^^^^^^\n File \"/app/solution/train_current_model.py\", line 1877, in train_model\n evaluate_validation_bce(\n File \"/opt/task-venv/lib/python3.12/site-packages/torch/utils/_contextlib.py\", line 120, in decorate_context\n return func(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^\n File \"/app/solution/train_current_model.py\", line 1653, in evaluate_validation_bce\n statistics, _ = score_target_microbatches(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/app/solution/train_current_model.py\", line 1345, in score_target_microbatches\n negative_loss = corrected_negative_average(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/app/solution/train_current_model.py\", line 1024, in corrected_negative_average\n result = torch.zeros(len(logits), device=logits.device)\n ^^^^^^\nNameError: name 'logits' is not defined\n", "stderr": "", "aggregated_output": "loaded full-sequence cache: tokens=20,056,329 targets=20,047,194\nchronological BCE window: train=1,052,953 validation=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194\ninbatch correction: min=0.250 mean_on_targets=1.001 max=4.000\ntarget weighting=organic-strong min=0.250 mean=0.575 max=1.000\nfull-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250\nusing a fresh AdamW optimizer after loading /app/solution/baseline_checkpoints/epoch-01.pt\nBCE objective changed: filter full-training-history->full-training-history, target weighting organic-strong->organic-strong, target window (0.1, 128, 16)->(0.1, 128, 16); negative sampler ('mixed', 24, 8, 512, 'bce')->('hard-mixed', 28, 4, 128, 'pairwise'); recomputing the resume checkpoint's validation BCE\nTraceback (most recent call last):\n File \"/app/solution/train_current_model.py\", line 2402, in \n raise SystemExit(main())\n ^^^^^^\n File \"/app/solution/train_current_model.py\", line 2327, in main\n model, training_history = train_model(\n ^^^^^^^^^^^^\n File \"/app/solution/train_current_model.py\", line 1877, in train_model\n evaluate_validation_bce(\n File \"/opt/task-venv/lib/python3.12/site-packages/torch/utils/_contextlib.py\", line 120, in decorate_context\n return func(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^\n File \"/app/solution/train_current_model.py\", line 1653, in evaluate_validation_bce\n statistics, _ = score_target_microbatches(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/app/solution/train_current_model.py\", line 1345, in score_target_microbatches\n negative_loss = corrected_negative_average(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/app/solution/train_current_model.py\", line 1024, in corrected_negative_average\n result = torch.zeros(len(logits), device=logits.device)\n ^^^^^^\nNameError: name 'logits' is not defined\n", "exit_code": 0, "duration": { "secs": 9, "nanos": 523445824 }, "formatted_output": "loaded full-sequence cache: tokens=20,056,329 targets=20,047,194\nchronological BCE window: train=1,052,953 validation=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194\ninbatch correction: min=0.250 mean_on_targets=1.001 max=4.000\ntarget weighting=organic-strong min=0.250 mean=0.575 max=1.000\nfull-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250\nusing a fresh AdamW optimizer after loading /app/solution/baseline_checkpoints/epoch-01.pt\nBCE objective changed: filter full-training-history->full-training-history, target weighting organic-strong->organic-strong, target window (0.1, 128, 16)->(0.1, 128, 16); negative sampler ('mixed', 24, 8, 512, 'bce')->('hard-mixed', 28, 4, 128, 'pairwise'); recomputing the resume checkpoint's validation BCE\nTraceback (most recent call last):\n File \"/app/solution/train_current_model.py\", line 2402, in \n raise SystemExit(main())\n ^^^^^^\n File \"/app/solution/train_current_model.py\", line 2327, in main\n model, training_history = train_model(\n ^^^^^^^^^^^^\n File \"/app/solution/train_current_model.py\", line 1877, in train_model\n evaluate_validation_bce(\n File \"/opt/task-venv/lib/python3.12/site-packages/torch/utils/_contextlib.py\", line 120, in decorate_context\n return func(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^\n File \"/app/solution/train_current_model.py\", line 1653, in evaluate_validation_bce\n statistics, _ = score_target_microbatches(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/app/solution/train_current_model.py\", line 1345, in score_target_microbatches\n negative_loss = corrected_negative_average(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/app/solution/train_current_model.py\", line 1024, in corrected_negative_average\n result = torch.zeros(len(logits), device=logits.device)\n ^^^^^^\nNameError: name 'logits' is not defined\n" } } ## Step 96 · agent · 2026-09-24T23:17:53.445Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T23:17:54.732Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/train_current_model.py\\n@@\\n- result = torch.zeros(len(logits), device=logits.device)\\n+ result = torch.zeros(len(raw), device=raw.device)\\n*** End Patch\"; text(await tools.apply_patch(patch));" } ### Tool output · 2026-09-24T23:17:54.882Z Script completed Wall time 0.0 seconds Output: {} ### Harness context { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-70ce0324-781c-4486-809f-a7c25495354c", "changes": { "/app/solution/train_current_model.py": { "type": "update", "unified_diff": "@@ -1023,3 +1023,3 @@\n total = config.uniform_negatives + config.inbatch_negatives\n- result = torch.zeros(len(logits), device=logits.device)\n+ result = torch.zeros(len(raw), device=raw.device)\n if config.uniform_negatives:\n", "move_path": null } }, "status": "completed", "stdout": "Success. Updated the following files:\nM /app/solution/train_current_model.py\n", "stderr": "" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "session_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "root_turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "response_id": "resp_08539f19231b9e9f016ab5afa0355c87d28ea94dbc8b96d165", "usage": { "input_tokens": 86990, "cached_input_tokens": 86144, "cache_write_input_tokens": 0, "output_tokens": 93, "reasoning_output_tokens": 14, "total_tokens": 87083 }, "turn_token_usage": { "input_tokens": 5389676, "cached_input_tokens": 5299328, "cache_write_input_tokens": 0, "output_tokens": 21675, "reasoning_output_tokens": 8213, "total_tokens": 5411351 }, "thread_token_usage": { "input_tokens": 5389676, "cached_input_tokens": 5299328, "cache_write_input_tokens": 0, "output_tokens": 21675, "reasoning_output_tokens": 8213, "total_tokens": 5411351 } } ## Step 97 · agent · 2026-09-24T23:17:56.389Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T23:18:00.020Z { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python -m py_compile /app/solution/train_current_model.py && /opt/task-venv/bin/python /app/solution/train_current_model.py --stage validation --public-dir /task --prepared-cache /app/solution/validation_sequences.npz --negative-sampling hard-mixed --uniform-negatives 28 --inbatch-negatives 4 --hard-pool-size 128 --retrieval-loss pairwise --epochs 2 --learning-rate 0.0001 --resume-checkpoint /app/solution/baseline_checkpoints/epoch-01.pt --checkpoint-dir /app/solution/pairwise_checkpoints --output /app/solution/pairwise_validation.parquet 2>&1 | tee /app/solution/pairwise_validation.log\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":12000}); text(JSON.stringify(r));" } ### Tool output · 2026-09-24T23:18:01.193Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"6b38e9","wall_time_seconds":1.001604497,"session_id":23145,"original_token_count":0,"output":""} ### Harness context { "type": "token_usage_record", "thread_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "session_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "root_turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "response_id": "resp_08539f19231b9e9f016ab5afa3279087d283ab20d05bafc029", "usage": { "input_tokens": 87107, "cached_input_tokens": 86784, "cache_write_input_tokens": 0, "output_tokens": 213, "reasoning_output_tokens": 6, "total_tokens": 87320 }, "turn_token_usage": { "input_tokens": 5476783, "cached_input_tokens": 5386112, "cache_write_input_tokens": 0, "output_tokens": 21888, "reasoning_output_tokens": 8219, "total_tokens": 5498671 }, "thread_token_usage": { "input_tokens": 5476783, "cached_input_tokens": 5386112, "cache_write_input_tokens": 0, "output_tokens": 21888, "reasoning_output_tokens": 8219, "total_tokens": 5498671 } } ## Step 98 · agent · 2026-09-24T23:18:02.566Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T23:18:03.269Z { "code": "const r = await tools.write_stdin({\"session_id\":23145,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":12000}); text(r.output);\n" } ### Tool output · 2026-09-24T23:18:33.278Z Script completed Wall time 30.0 seconds Output: loaded full-sequence cache: tokens=20,056,329 targets=20,047,194 chronological BCE window: train=1,052,953 validation=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194 inbatch correction: min=0.250 mean_on_targets=1.001 max=4.000 target weighting=organic-strong min=0.250 mean=0.575 max=1.000 full-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250 using a fresh AdamW optimizer after loading /app/solution/baseline_checkpoints/epoch-01.pt BCE objective changed: filter full-training-history->full-training-history, target weighting organic-strong->organic-strong, target window (0.1, 128, 16)->(0.1, 128, 16); negative sampler ('mixed', 24, 8, 512, 'bce')->('hard-mixed', 28, 4, 128, 'pairwise'); recomputing the resume checkpoint's validation BCE resumed validation-BCE training: epoch=1 loss=0.547928 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "session_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "root_turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "response_id": "resp_08539f19231b9e9f016ab5afa96bf087d2b87bd294356298b2", "usage": { "input_tokens": 87379, "cached_input_tokens": 86912, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 87432 }, "turn_token_usage": { "input_tokens": 5564162, "cached_input_tokens": 5473024, "cache_write_input_tokens": 0, "output_tokens": 21941, "reasoning_output_tokens": 8225, "total_tokens": 5586103 }, "thread_token_usage": { "input_tokens": 5564162, "cached_input_tokens": 5473024, "cache_write_input_tokens": 0, "output_tokens": 21941, "reasoning_output_tokens": 8225, "total_tokens": 5586103 } } ## Step 99 · agent · 2026-09-24T23:18:34.751Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T23:18:35.468Z { "code": "const r = await tools.write_stdin({\"session_id\":23145,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":12000}); text(r.output);\n" } ### Tool output · 2026-09-24T23:19:05.502Z Script completed Wall time 30.0 seconds Output: ### Harness context { "type": "token_usage_record", "thread_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "session_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "root_turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "response_id": "resp_08539f19231b9e9f016ab5afc9875887d2ba40d7fd8f0ca3b8", "usage": { "input_tokens": 87723, "cached_input_tokens": 87168, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 87776 }, "turn_token_usage": { "input_tokens": 5651885, "cached_input_tokens": 5560192, "cache_write_input_tokens": 0, "output_tokens": 21994, "reasoning_output_tokens": 8231, "total_tokens": 5673879 }, "thread_token_usage": { "input_tokens": 5651885, "cached_input_tokens": 5560192, "cache_write_input_tokens": 0, "output_tokens": 21994, "reasoning_output_tokens": 8231, "total_tokens": 5673879 } } ### exec · 2026-09-24T23:19:07.706Z { "code": "const r = await tools.write_stdin({\"session_id\":23145,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":12000}); text(r.output);\n" } ### Tool output · 2026-09-24T23:19:37.741Z Script completed Wall time 30.0 seconds Output: ### Harness context { "type": "token_usage_record", "thread_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "session_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "root_turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "response_id": "resp_08539f19231b9e9f016ab5afe9ce6887d2921e638ff0ff656b", "usage": { "input_tokens": 87799, "cached_input_tokens": 87552, "cache_write_input_tokens": 0, "output_tokens": 45, "reasoning_output_tokens": 0, "total_tokens": 87844 }, "turn_token_usage": { "input_tokens": 5739684, "cached_input_tokens": 5647744, "cache_write_input_tokens": 0, "output_tokens": 22039, "reasoning_output_tokens": 8231, "total_tokens": 5761723 }, "thread_token_usage": { "input_tokens": 5739684, "cached_input_tokens": 5647744, "cache_write_input_tokens": 0, "output_tokens": 22039, "reasoning_output_tokens": 8231, "total_tokens": 5761723 } } ### exec · 2026-09-24T23:19:41.137Z { "code": "const r = await tools.write_stdin({\"session_id\":23145,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":12000}); text(r.output);\n" } ### Tool output · 2026-09-24T23:20:11.170Z Script completed Wall time 30.0 seconds Output: ### Harness context { "type": "token_usage_record", "thread_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "session_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "root_turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "response_id": "resp_08539f19231b9e9f016ab5b00b77f887d28d0b245c8d7bb922", "usage": { "input_tokens": 87867, "cached_input_tokens": 87680, "cache_write_input_tokens": 0, "output_tokens": 45, "reasoning_output_tokens": 0, "total_tokens": 87912 }, "turn_token_usage": { "input_tokens": 5827551, "cached_input_tokens": 5735424, "cache_write_input_tokens": 0, "output_tokens": 22084, "reasoning_output_tokens": 8231, "total_tokens": 5849635 }, "thread_token_usage": { "input_tokens": 5827551, "cached_input_tokens": 5735424, "cache_write_input_tokens": 0, "output_tokens": 22084, "reasoning_output_tokens": 8231, "total_tokens": 5849635 } } ## Step 100 · agent · 2026-09-24T23:20:15.254Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T23:20:16.067Z { "code": "const r = await tools.write_stdin({\"session_id\":23145,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":12000}); text(r.output);\n" } ### Tool output · 2026-09-24T23:20:46.108Z Script completed Wall time 30.0 seconds Output: ### Harness context { "type": "token_usage_record", "thread_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "session_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "root_turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "response_id": "resp_08539f19231b9e9f016ab5b02b61fc87d2b007e3687bc825d1", "usage": { "input_tokens": 87935, "cached_input_tokens": 87680, "cache_write_input_tokens": 0, "output_tokens": 56, "reasoning_output_tokens": 9, "total_tokens": 87991 }, "turn_token_usage": { "input_tokens": 5915486, "cached_input_tokens": 5823104, "cache_write_input_tokens": 0, "output_tokens": 22140, "reasoning_output_tokens": 8240, "total_tokens": 5937626 }, "thread_token_usage": { "input_tokens": 5915486, "cached_input_tokens": 5823104, "cache_write_input_tokens": 0, "output_tokens": 22140, "reasoning_output_tokens": 8240, "total_tokens": 5937626 } } ## Step 101 · agent · 2026-09-24T23:20:47.528Z ### Thinking unavailable — no text recorded The pairwise run starts at a stable held-out loss of `0.548` and avoids the absolute-logit pressure that inflated norms in the previous trial. The code path is otherwise unchanged: same chronology, history, target weighting, item/user towers, candidate masks, and 32 total negatives. ### exec · 2026-09-24T23:20:49.587Z { "code": "const r = await tools.write_stdin({\"session_id\":23145,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":12000}); text(r.output);\n" } ### Tool output · 2026-09-24T23:21:19.605Z Script completed Wall time 30.0 seconds Output: ### Harness context { "type": "token_usage_record", "thread_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "session_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "root_turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "response_id": "resp_08539f19231b9e9f016ab5b04e8dec87d2be120ad82a407344", "usage": { "input_tokens": 88014, "cached_input_tokens": 87808, "cache_write_input_tokens": 0, "output_tokens": 122, "reasoning_output_tokens": 8, "total_tokens": 88136 }, "turn_token_usage": { "input_tokens": 6003500, "cached_input_tokens": 5910912, "cache_write_input_tokens": 0, "output_tokens": 22262, "reasoning_output_tokens": 8248, "total_tokens": 6025762 }, "thread_token_usage": { "input_tokens": 6003500, "cached_input_tokens": 5910912, "cache_write_input_tokens": 0, "output_tokens": 22262, "reasoning_output_tokens": 8248, "total_tokens": 6025762 } } ### exec · 2026-09-24T23:21:21.569Z { "code": "const r = await tools.write_stdin({\"session_id\":23145,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":12000}); text(r.output);\n" } ### Tool output · 2026-09-24T23:21:51.587Z Script completed Wall time 30.0 seconds Output: epoch=2/2 batches=250 targets=987,417 batch_bce=0.431013 running_bce=0.439149 elapsed=194.3s ### Harness context { "type": "token_usage_record", "thread_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "session_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "root_turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "response_id": "resp_08539f19231b9e9f016ab5b06fed7887d2bda980fdfbedecfd", "usage": { "input_tokens": 88159, "cached_input_tokens": 87808, "cache_write_input_tokens": 0, "output_tokens": 45, "reasoning_output_tokens": 0, "total_tokens": 88204 }, "turn_token_usage": { "input_tokens": 6091659, "cached_input_tokens": 5998720, "cache_write_input_tokens": 0, "output_tokens": 22307, "reasoning_output_tokens": 8248, "total_tokens": 6113966 }, "thread_token_usage": { "input_tokens": 6091659, "cached_input_tokens": 5998720, "cache_write_input_tokens": 0, "output_tokens": 22307, "reasoning_output_tokens": 8248, "total_tokens": 6113966 } } ## Step 102 · agent · 2026-09-24T23:21:52.859Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T23:21:54.982Z { "code": "const r = await tools.write_stdin({\"session_id\":23145,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":12000}); text(r.output);\n" } ### Tool output · 2026-09-24T23:22:25.006Z Script completed Wall time 30.0 seconds Output: saved epoch checkpoint: /app/solution/pairwise_checkpoints/epoch-02.pt epoch=2/2 train_loss=0.43909 validation_loss=0.45634 pair_accuracy=0.753 train_targets=1,052,953 validation_targets=130,103 train_seconds=207.1 validation_seconds=17.1 restored best validation-BCE checkpoint: epoch=2 loss=0.456341 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "session_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "root_turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "response_id": "resp_08539f19231b9e9f016ab5b08fd1ac87d2bf5ada2a6814132f", "usage": { "input_tokens": 88263, "cached_input_tokens": 87936, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 88316 }, "turn_token_usage": { "input_tokens": 6179922, "cached_input_tokens": 6086656, "cache_write_input_tokens": 0, "output_tokens": 22360, "reasoning_output_tokens": 8254, "total_tokens": 6202282 }, "thread_token_usage": { "input_tokens": 6179922, "cached_input_tokens": 6086656, "cache_write_input_tokens": 0, "output_tokens": 22360, "reasoning_output_tokens": 8254, "total_tokens": 6202282 } } ## Step 103 · agent · 2026-09-24T23:22:26.230Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T23:22:26.910Z { "code": "const r = await tools.write_stdin({\"session_id\":23145,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":12000}); text(r.output);\n" } ### Tool output · 2026-09-24T23:22:56.935Z Script completed Wall time 30.0 seconds Output: ### Harness context { "type": "token_usage_record", "thread_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "session_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "root_turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "response_id": "resp_08539f19231b9e9f016ab5b0b135ac87d2ab62c1a22b60ba48", "usage": { "input_tokens": 88428, "cached_input_tokens": 88064, "cache_write_input_tokens": 0, "output_tokens": 55, "reasoning_output_tokens": 8, "total_tokens": 88483 }, "turn_token_usage": { "input_tokens": 6268350, "cached_input_tokens": 6174720, "cache_write_input_tokens": 0, "output_tokens": 22415, "reasoning_output_tokens": 8262, "total_tokens": 6290765 }, "thread_token_usage": { "input_tokens": 6268350, "cached_input_tokens": 6174720, "cache_write_input_tokens": 0, "output_tokens": 22415, "reasoning_output_tokens": 8262, "total_tokens": 6290765 } } ### exec · 2026-09-24T23:22:59.063Z { "code": "const r = await tools.write_stdin({\"session_id\":23145,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":12000}); text(r.output);\n" } ### Tool output · 2026-09-24T23:23:29.099Z Script completed Wall time 30.0 seconds Output: ### Harness context { "type": "token_usage_record", "thread_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "session_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "root_turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "response_id": "resp_08539f19231b9e9f016ab5b0d14a3c87d298f509de453a5150", "usage": { "input_tokens": 88506, "cached_input_tokens": 88192, "cache_write_input_tokens": 0, "output_tokens": 45, "reasoning_output_tokens": 0, "total_tokens": 88551 }, "turn_token_usage": { "input_tokens": 6356856, "cached_input_tokens": 6262912, "cache_write_input_tokens": 0, "output_tokens": 22460, "reasoning_output_tokens": 8262, "total_tokens": 6379316 }, "thread_token_usage": { "input_tokens": 6356856, "cached_input_tokens": 6262912, "cache_write_input_tokens": 0, "output_tokens": 22460, "reasoning_output_tokens": 8262, "total_tokens": 6379316 } } ### exec · 2026-09-24T23:23:32.067Z { "code": "const r = await tools.write_stdin({\"session_id\":23145,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":15000}); text(r.output);\n" } ### Tool output · 2026-09-24T23:23:33.149Z Script completed Wall time 1.1 seconds Output: ranked users=1,024/8,456 ranked users=2,048/8,456 ranked users=3,072/8,456 ranked users=4,096/8,456 ranked users=5,120/8,456 ranked users=6,144/8,456 ranked users=7,168/8,456 ranked users=8,192/8,456 ranked users=8,456/8,456 { "baseline": "yambda-windowed-sequence-two-tower-base", "stage": "validation", "output": "/app/solution/pairwise_validation.parquet", "rows": 845600, "users": 8456, "runtime_seconds": 328.408, "public_inputs_only": true, "architecture": { "family": "pure-windowed-sequence-two-tower", "sequence_training": "every-adjacent-target-within-recent-chronological-window", "sequence_encoder": "one-layer-GRU-dense-timestep-loss", "bptt_hidden_state": "carried-and-detached", "model_selection": "chronological-validation-BCE-early-stopping", "loss_execution": "gradient-accumulated-microbatches", "user_id_embedding": false, "user_mlp": "582-512-256-192", "item_mlp": "196-512-256-192", "normalization": "none", "score": "raw-dot-product", "objective": "sample-weighted-pairwise-at-every-timestep", "negative_sampling": "28-uniform-plus-4-hard-mixed", "false_negative_filter": "full-training-history", "target_weighting": "organic-strong" }, "config": { "bptt_steps": 100, "user_batch_size": 64, "embedding_dim": 192, "hidden_dim": 192, "user_hidden_1": 512, "user_hidden_2": 256, "item_hidden_1": 512, "item_hidden_2": 256, "dropout": 0.05, "epochs": 2, "learning_rate": 0.0001, "weight_decay": 1e-05, "gradient_clip": 2.0, "loss_microbatch_size": 65536, "uniform_negatives": 28, "inbatch_negatives": 4, "negative_sampling": "hard-mixed", "hard_pool_size": 128, "retrieval_loss": "pairwise", "inbatch_correction_power": 0.5, "inbatch_correction_min": 0.25, "inbatch_correction_max": 4.0, "negative_filter_scope": "full-training-history", "target_weighting": "organic-strong", "ordinary_target_weight": 0.25, "organic_strong_target_weight": 1.0, "organic_like_target_weight": 3.0, "discovery_horizon_days": 7, "validation_fraction": 0.1, "max_train_targets_per_user": 128, "max_validation_targets_per_user": 16, "early_stopping_patience": 1, "early_stopping_min_delta": 0.0001, "seed": 29 }, "sequence_tokens": 20056329, "all_next_item_targets": 20047194, "selected_training_targets": 1052953, "selected_validation_targets": 130103, "discarded_old_targets": 18864138, "training_history": [ { "epoch": 1.0, "loss": 0.5398380466771012, "positive_logit": 0.7427799607756806, "negative_logit": -0.6222247067485194, "pair_accuracy": 0.7547733315257186, "targets": 1052953.0, "target_weight_sum": 632551.0, "batches": 266.0, "seconds": 215.029519907, "validation_targets": 130103.0, "validation_target_weight_sum": 78769.25, "validation_seconds": 18.087879383999734, "validation_loss": 0.5479279913544122 }, { "epoch": 2.0, "loss": 0.439091644489762, "positive_logit": 0.7657255904472169, "negative_logit": -0.813295811523659, "pair_accuracy": 0.7526544988237842, "targets": 1052953.0, "target_weight_sum": 632551.0, "batches": 266.0, "seconds": 207.14071103800006, "validation_loss": 0.4563410930953674, "validation_targets": 130103.0, "validation_target_weight_sum": 78769.25, "validation_seconds": 17.067200067999693 } ], "embedding_diagnostics": { "query_norm_mean": 1.6349302530288696, "query_norm_std": 0.6893839240074158, "item_norm_mean": 2.4392271041870117, "item_norm_std": 1.4339646100997925 }, "torch_version": "2.8.0+cpu", "device": "cpu" } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "session_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "root_turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "response_id": "resp_08539f19231b9e9f016ab5b0f14b1c87d285749d6208c4a4da", "usage": { "input_tokens": 88574, "cached_input_tokens": 88320, "cache_write_input_tokens": 0, "output_tokens": 45, "reasoning_output_tokens": 0, "total_tokens": 88619 }, "turn_token_usage": { "input_tokens": 6445430, "cached_input_tokens": 6351232, "cache_write_input_tokens": 0, "output_tokens": 22505, "reasoning_output_tokens": 8262, "total_tokens": 6467935 }, "thread_token_usage": { "input_tokens": 6445430, "cached_input_tokens": 6351232, "cache_write_input_tokens": 0, "output_tokens": 22505, "reasoning_output_tokens": 8262, "total_tokens": 6467935 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-fb331bee-a58f-468a-9690-8247ff4c1537", "process_id": "23145", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python -m py_compile /app/solution/train_current_model.py && /opt/task-venv/bin/python /app/solution/train_current_model.py --stage validation --public-dir /task --prepared-cache /app/solution/validation_sequences.npz --negative-sampling hard-mixed --uniform-negatives 28 --inbatch-negatives 4 --hard-pool-size 128 --retrieval-loss pairwise --epochs 2 --learning-rate 0.0001 --resume-checkpoint /app/solution/baseline_checkpoints/epoch-01.pt --checkpoint-dir /app/solution/pairwise_checkpoints --output /app/solution/pairwise_validation.parquet 2>&1 | tee /app/solution/pairwise_validation.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python -m py_compile /app/solution/train_current_model.py && /opt/task-venv/bin/python /app/solution/train_current_model.py --stage validation --public-dir /task --prepared-cache /app/solution/validation_sequences.npz --negative-sampling hard-mixed --uniform-negatives 28 --inbatch-negatives 4 --hard-pool-size 128 --retrieval-loss pairwise --epochs 2 --learning-rate 0.0001 --resume-checkpoint /app/solution/baseline_checkpoints/epoch-01.pt --checkpoint-dir /app/solution/pairwise_checkpoints --output /app/solution/pairwise_validation.parquet 2>&1 | tee /app/solution/pairwise_validation.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "loaded full-sequence cache: tokens=20,056,329 targets=20,047,194\nchronological BCE window: train=1,052,953 validation=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194\ninbatch correction: min=0.250 mean_on_targets=1.001 max=4.000\ntarget weighting=organic-strong min=0.250 mean=0.575 max=1.000\nfull-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250\nusing a fresh AdamW optimizer after loading /app/solution/baseline_checkpoints/epoch-01.pt\nBCE objective changed: filter full-training-history->full-training-history, target weighting organic-strong->organic-strong, target window (0.1, 128, 16)->(0.1, 128, 16); negative sampler ('mixed', 24, 8, 512, 'bce')->('hard-mixed', 28, 4, 128, 'pairwise'); recomputing the resume checkpoint's validation BCE\nresumed validation-BCE training: epoch=1 loss=0.547928\nepoch=2/2 batches=250 targets=987,417 batch_bce=0.431013 running_bce=0.439149 elapsed=194.3s\nsaved epoch checkpoint: /app/solution/pairwise_checkpoints/epoch-02.pt\nepoch=2/2 train_loss=0.43909 validation_loss=0.45634 pair_accuracy=0.753 train_targets=1,052,953 validation_targets=130,103 train_seconds=207.1 validation_seconds=17.1\nrestored best validation-BCE checkpoint: epoch=2 loss=0.456341\nranked users=1,024/8,456\nranked users=2,048/8,456\nranked users=3,072/8,456\nranked users=4,096/8,456\nranked users=5,120/8,456\nranked users=6,144/8,456\nranked users=7,168/8,456\nranked users=8,192/8,456\nranked users=8,456/8,456\n{\n \"baseline\": \"yambda-windowed-sequence-two-tower-base\",\n \"stage\": \"validation\",\n \"output\": \"/app/solution/pairwise_validation.parquet\",\n \"rows\": 845600,\n \"users\": 8456,\n \"runtime_seconds\": 328.408,\n \"public_inputs_only\": true,\n \"architecture\": {\n \"family\": \"pure-windowed-sequence-two-tower\",\n \"sequence_training\": \"every-adjacent-target-within-recent-chronological-window\",\n \"sequence_encoder\": \"one-layer-GRU-dense-timestep-loss\",\n \"bptt_hidden_state\": \"carried-and-detached\",\n \"model_selection\": \"chronological-validation-BCE-early-stopping\",\n \"loss_execution\": \"gradient-accumulated-microbatches\",\n \"user_id_embedding\": false,\n \"user_mlp\": \"582-512-256-192\",\n \"item_mlp\": \"196-512-256-192\",\n \"normalization\": \"none\",\n \"score\": \"raw-dot-product\",\n \"objective\": \"sample-weighted-pairwise-at-every-timestep\",\n \"negative_sampling\": \"28-uniform-plus-4-hard-mixed\",\n \"false_negative_filter\": \"full-training-history\",\n \"target_weighting\": \"organic-strong\"\n },\n \"config\": {\n \"bptt_steps\": 100,\n \"user_batch_size\": 64,\n \"embedding_dim\": 192,\n \"hidden_dim\": 192,\n \"user_hidden_1\": 512,\n \"user_hidden_2\": 256,\n \"item_hidden_1\": 512,\n \"item_hidden_2\": 256,\n \"dropout\": 0.05,\n \"epochs\": 2,\n \"learning_rate\": 0.0001,\n \"weight_decay\": 1e-05,\n \"gradient_clip\": 2.0,\n \"loss_microbatch_size\": 65536,\n \"uniform_negatives\": 28,\n \"inbatch_negatives\": 4,\n \"negative_sampling\": \"hard-mixed\",\n \"hard_pool_size\": 128,\n \"retrieval_loss\": \"pairwise\",\n \"inbatch_correction_power\": 0.5,\n \"inbatch_correction_min\": 0.25,\n \"inbatch_correction_max\": 4.0,\n \"negative_filter_scope\": \"full-training-history\",\n \"target_weighting\": \"organic-strong\",\n \"ordinary_target_weight\": 0.25,\n \"organic_strong_target_weight\": 1.0,\n \"organic_like_target_weight\": 3.0,\n \"discovery_horizon_days\": 7,\n \"validation_fraction\": 0.1,\n \"max_train_targets_per_user\": 128,\n \"max_validation_targets_per_user\": 16,\n \"early_stopping_patience\": 1,\n \"early_stopping_min_delta\": 0.0001,\n \"seed\": 29\n },\n \"sequence_tokens\": 20056329,\n \"all_next_item_targets\": 20047194,\n \"selected_training_targets\": 1052953,\n \"selected_validation_targets\": 130103,\n \"discarded_old_targets\": 18864138,\n \"training_history\": [\n {\n \"epoch\": 1.0,\n \"loss\": 0.5398380466771012,\n \"positive_logit\": 0.7427799607756806,\n \"negative_logit\": -0.6222247067485194,\n \"pair_accuracy\": 0.7547733315257186,\n \"targets\": 1052953.0,\n \"target_weight_sum\": 632551.0,\n \"batches\": 266.0,\n \"seconds\": 215.029519907,\n \"validation_targets\": 130103.0,\n \"validation_target_weight_sum\": 78769.25,\n \"validation_seconds\": 18.087879383999734,\n \"validation_loss\": 0.5479279913544122\n },\n {\n \"epoch\": 2.0,\n \"loss\": 0.439091644489762,\n \"positive_logit\": 0.7657255904472169,\n \"negative_logit\": -0.813295811523659,\n \"pair_accuracy\": 0.7526544988237842,\n \"targets\": 1052953.0,\n \"target_weight_sum\": 632551.0,\n \"batches\": 266.0,\n \"seconds\": 207.14071103800006,\n \"validation_loss\": 0.4563410930953674,\n \"validation_targets\": 130103.0,\n \"validation_target_weight_sum\": 78769.25,\n \"validation_seconds\": 17.067200067999693\n }\n ],\n \"embedding_diagnostics\": {\n \"query_norm_mean\": 1.6349302530288696,\n \"query_norm_std\": 0.6893839240074158,\n \"item_norm_mean\": 2.4392271041870117,\n \"item_norm_std\": 1.4339646100997925\n },\n \"torch_version\": \"2.8.0+cpu\",\n \"device\": \"cpu\"\n}\n", "stderr": "", "aggregated_output": "loaded full-sequence cache: tokens=20,056,329 targets=20,047,194\nchronological BCE window: train=1,052,953 validation=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194\ninbatch correction: min=0.250 mean_on_targets=1.001 max=4.000\ntarget weighting=organic-strong min=0.250 mean=0.575 max=1.000\nfull-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250\nusing a fresh AdamW optimizer after loading /app/solution/baseline_checkpoints/epoch-01.pt\nBCE objective changed: filter full-training-history->full-training-history, target weighting organic-strong->organic-strong, target window (0.1, 128, 16)->(0.1, 128, 16); negative sampler ('mixed', 24, 8, 512, 'bce')->('hard-mixed', 28, 4, 128, 'pairwise'); recomputing the resume checkpoint's validation BCE\nresumed validation-BCE training: epoch=1 loss=0.547928\nepoch=2/2 batches=250 targets=987,417 batch_bce=0.431013 running_bce=0.439149 elapsed=194.3s\nsaved epoch checkpoint: /app/solution/pairwise_checkpoints/epoch-02.pt\nepoch=2/2 train_loss=0.43909 validation_loss=0.45634 pair_accuracy=0.753 train_targets=1,052,953 validation_targets=130,103 train_seconds=207.1 validation_seconds=17.1\nrestored best validation-BCE checkpoint: epoch=2 loss=0.456341\nranked users=1,024/8,456\nranked users=2,048/8,456\nranked users=3,072/8,456\nranked users=4,096/8,456\nranked users=5,120/8,456\nranked users=6,144/8,456\nranked users=7,168/8,456\nranked users=8,192/8,456\nranked users=8,456/8,456\n{\n \"baseline\": \"yambda-windowed-sequence-two-tower-base\",\n \"stage\": \"validation\",\n \"output\": \"/app/solution/pairwise_validation.parquet\",\n \"rows\": 845600,\n \"users\": 8456,\n \"runtime_seconds\": 328.408,\n \"public_inputs_only\": true,\n \"architecture\": {\n \"family\": \"pure-windowed-sequence-two-tower\",\n \"sequence_training\": \"every-adjacent-target-within-recent-chronological-window\",\n \"sequence_encoder\": \"one-layer-GRU-dense-timestep-loss\",\n \"bptt_hidden_state\": \"carried-and-detached\",\n \"model_selection\": \"chronological-validation-BCE-early-stopping\",\n \"loss_execution\": \"gradient-accumulated-microbatches\",\n \"user_id_embedding\": false,\n \"user_mlp\": \"582-512-256-192\",\n \"item_mlp\": \"196-512-256-192\",\n \"normalization\": \"none\",\n \"score\": \"raw-dot-product\",\n \"objective\": \"sample-weighted-pairwise-at-every-timestep\",\n \"negative_sampling\": \"28-uniform-plus-4-hard-mixed\",\n \"false_negative_filter\": \"full-training-history\",\n \"target_weighting\": \"organic-strong\"\n },\n \"config\": {\n \"bptt_steps\": 100,\n \"user_batch_size\": 64,\n \"embedding_dim\": 192,\n \"hidden_dim\": 192,\n \"user_hidden_1\": 512,\n \"user_hidden_2\": 256,\n \"item_hidden_1\": 512,\n \"item_hidden_2\": 256,\n \"dropout\": 0.05,\n \"epochs\": 2,\n \"learning_rate\": 0.0001,\n \"weight_decay\": 1e-05,\n \"gradient_clip\": 2.0,\n \"loss_microbatch_size\": 65536,\n \"uniform_negatives\": 28,\n \"inbatch_negatives\": 4,\n \"negative_sampling\": \"hard-mixed\",\n \"hard_pool_size\": 128,\n \"retrieval_loss\": \"pairwise\",\n \"inbatch_correction_power\": 0.5,\n \"inbatch_correction_min\": 0.25,\n \"inbatch_correction_max\": 4.0,\n \"negative_filter_scope\": \"full-training-history\",\n \"target_weighting\": \"organic-strong\",\n \"ordinary_target_weight\": 0.25,\n \"organic_strong_target_weight\": 1.0,\n \"organic_like_target_weight\": 3.0,\n \"discovery_horizon_days\": 7,\n \"validation_fraction\": 0.1,\n \"max_train_targets_per_user\": 128,\n \"max_validation_targets_per_user\": 16,\n \"early_stopping_patience\": 1,\n \"early_stopping_min_delta\": 0.0001,\n \"seed\": 29\n },\n \"sequence_tokens\": 20056329,\n \"all_next_item_targets\": 20047194,\n \"selected_training_targets\": 1052953,\n \"selected_validation_targets\": 130103,\n \"discarded_old_targets\": 18864138,\n \"training_history\": [\n {\n \"epoch\": 1.0,\n \"loss\": 0.5398380466771012,\n \"positive_logit\": 0.7427799607756806,\n \"negative_logit\": -0.6222247067485194,\n \"pair_accuracy\": 0.7547733315257186,\n \"targets\": 1052953.0,\n \"target_weight_sum\": 632551.0,\n \"batches\": 266.0,\n \"seconds\": 215.029519907,\n \"validation_targets\": 130103.0,\n \"validation_target_weight_sum\": 78769.25,\n \"validation_seconds\": 18.087879383999734,\n \"validation_loss\": 0.5479279913544122\n },\n {\n \"epoch\": 2.0,\n \"loss\": 0.439091644489762,\n \"positive_logit\": 0.7657255904472169,\n \"negative_logit\": -0.813295811523659,\n \"pair_accuracy\": 0.7526544988237842,\n \"targets\": 1052953.0,\n \"target_weight_sum\": 632551.0,\n \"batches\": 266.0,\n \"seconds\": 207.14071103800006,\n \"validation_loss\": 0.4563410930953674,\n \"validation_targets\": 130103.0,\n \"validation_target_weight_sum\": 78769.25,\n \"validation_seconds\": 17.067200067999693\n }\n ],\n \"embedding_diagnostics\": {\n \"query_norm_mean\": 1.6349302530288696,\n \"query_norm_std\": 0.6893839240074158,\n \"item_norm_mean\": 2.4392271041870117,\n \"item_norm_std\": 1.4339646100997925\n },\n \"torch_version\": \"2.8.0+cpu\",\n \"device\": \"cpu\"\n}\n", "exit_code": 0, "duration": { "secs": 332, "nanos": 953104691 }, "formatted_output": "loaded full-sequence cache: tokens=20,056,329 targets=20,047,194\nchronological BCE window: train=1,052,953 validation=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194\ninbatch correction: min=0.250 mean_on_targets=1.001 max=4.000\ntarget weighting=organic-strong min=0.250 mean=0.575 max=1.000\nfull-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250\nusing a fresh AdamW optimizer after loading /app/solution/baseline_checkpoints/epoch-01.pt\nBCE objective changed: filter full-training-history->full-training-history, target weighting organic-strong->organic-strong, target window (0.1, 128, 16)->(0.1, 128, 16); negative sampler ('mixed', 24, 8, 512, 'bce')->('hard-mixed', 28, 4, 128, 'pairwise'); recomputing the resume checkpoint's validation BCE\nresumed validation-BCE training: epoch=1 loss=0.547928\nepoch=2/2 batches=250 targets=987,417 batch_bce=0.431013 running_bce=0.439149 elapsed=194.3s\nsaved epoch checkpoint: /app/solution/pairwise_checkpoints/epoch-02.pt\nepoch=2/2 train_loss=0.43909 validation_loss=0.45634 pair_accuracy=0.753 train_targets=1,052,953 validation_targets=130,103 train_seconds=207.1 validation_seconds=17.1\nrestored best validation-BCE checkpoint: epoch=2 loss=0.456341\nranked users=1,024/8,456\nranked users=2,048/8,456\nranked users=3,072/8,456\nranked users=4,096/8,456\nranked users=5,120/8,456\nranked users=6,144/8,456\nranked users=7,168/8,456\nranked users=8,192/8,456\nranked users=8,456/8,456\n{\n \"baseline\": \"yambda-windowed-sequence-two-tower-base\",\n \"stage\": \"validation\",\n \"output\": \"/app/solution/pairwise_validation.parquet\",\n \"rows\": 845600,\n \"users\": 8456,\n \"runtime_seconds\": 328.408,\n \"public_inputs_only\": true,\n \"architecture\": {\n \"family\": \"pure-windowed-sequence-two-tower\",\n \"sequence_training\": \"every-adjacent-target-within-recent-chronological-window\",\n \"sequence_encoder\": \"one-layer-GRU-dense-timestep-loss\",\n \"bptt_hidden_state\": \"carried-and-detached\",\n \"model_selection\": \"chronological-validation-BCE-early-stopping\",\n \"loss_execution\": \"gradient-accumulated-microbatches\",\n \"user_id_embedding\": false,\n \"user_mlp\": \"582-512-256-192\",\n \"item_mlp\": \"196-512-256-192\",\n \"normalization\": \"none\",\n \"score\": \"raw-dot-product\",\n \"objective\": \"sample-weighted-pairwise-at-every-timestep\",\n \"negative_sampling\": \"28-uniform-plus-4-hard-mixed\",\n \"false_negative_filter\": \"full-training-history\",\n \"target_weighting\": \"organic-strong\"\n },\n \"config\": {\n \"bptt_steps\": 100,\n \"user_batch_size\": 64,\n \"embedding_dim\": 192,\n \"hidden_dim\": 192,\n \"user_hidden_1\": 512,\n \"user_hidden_2\": 256,\n \"item_hidden_1\": 512,\n \"item_hidden_2\": 256,\n \"dropout\": 0.05,\n \"epochs\": 2,\n \"learning_rate\": 0.0001,\n \"weight_decay\": 1e-05,\n \"gradient_clip\": 2.0,\n \"loss_microbatch_size\": 65536,\n \"uniform_negatives\": 28,\n \"inbatch_negatives\": 4,\n \"negative_sampling\": \"hard-mixed\",\n \"hard_pool_size\": 128,\n \"retrieval_loss\": \"pairwise\",\n \"inbatch_correction_power\": 0.5,\n \"inbatch_correction_min\": 0.25,\n \"inbatch_correction_max\": 4.0,\n \"negative_filter_scope\": \"full-training-history\",\n \"target_weighting\": \"organic-strong\",\n \"ordinary_target_weight\": 0.25,\n \"organic_strong_target_weight\": 1.0,\n \"organic_like_target_weight\": 3.0,\n \"discovery_horizon_days\": 7,\n \"validation_fraction\": 0.1,\n \"max_train_targets_per_user\": 128,\n \"max_validation_targets_per_user\": 16,\n \"early_stopping_patience\": 1,\n \"early_stopping_min_delta\": 0.0001,\n \"seed\": 29\n },\n \"sequence_tokens\": 20056329,\n \"all_next_item_targets\": 20047194,\n \"selected_training_targets\": 1052953,\n \"selected_validation_targets\": 130103,\n \"discarded_old_targets\": 18864138,\n \"training_history\": [\n {\n \"epoch\": 1.0,\n \"loss\": 0.5398380466771012,\n \"positive_logit\": 0.7427799607756806,\n \"negative_logit\": -0.6222247067485194,\n \"pair_accuracy\": 0.7547733315257186,\n \"targets\": 1052953.0,\n \"target_weight_sum\": 632551.0,\n \"batches\": 266.0,\n \"seconds\": 215.029519907,\n \"validation_targets\": 130103.0,\n \"validation_target_weight_sum\": 78769.25,\n \"validation_seconds\": 18.087879383999734,\n \"validation_loss\": 0.5479279913544122\n },\n {\n \"epoch\": 2.0,\n \"loss\": 0.439091644489762,\n \"positive_logit\": 0.7657255904472169,\n \"negative_logit\": -0.813295811523659,\n \"pair_accuracy\": 0.7526544988237842,\n \"targets\": 1052953.0,\n \"target_weight_sum\": 632551.0,\n \"batches\": 266.0,\n \"seconds\": 207.14071103800006,\n \"validation_loss\": 0.4563410930953674,\n \"validation_targets\": 130103.0,\n \"validation_target_weight_sum\": 78769.25,\n \"validation_seconds\": 17.067200067999693\n }\n ],\n \"embedding_diagnostics\": {\n \"query_norm_mean\": 1.6349302530288696,\n \"query_norm_std\": 0.6893839240074158,\n \"item_norm_mean\": 2.4392271041870117,\n \"item_norm_std\": 1.4339646100997925\n },\n \"torch_version\": \"2.8.0+cpu\",\n \"device\": \"cpu\"\n}\n" } } ## Step 104 · agent · 2026-09-24T23:23:34.623Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T23:23:35.943Z { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/pairwise_validation.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":5000}); text(r.output);" } ### Tool output · 2026-09-24T23:23:39.362Z Script completed Wall time 3.4 seconds Output: { "model": "/app/solution/pairwise_validation.parquet", "scores": { "quality_ndcg@10": 0.008895, "quality_users": 4263 } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "session_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "root_turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "response_id": "resp_08539f19231b9e9f016ab5b0f55cc887d2a25c4e8f84d0e4e4", "usage": { "input_tokens": 89899, "cached_input_tokens": 88448, "cache_write_input_tokens": 0, "output_tokens": 77, "reasoning_output_tokens": 6, "total_tokens": 89976 }, "turn_token_usage": { "input_tokens": 6535329, "cached_input_tokens": 6439680, "cache_write_input_tokens": 0, "output_tokens": 22582, "reasoning_output_tokens": 8268, "total_tokens": 6557911 }, "thread_token_usage": { "input_tokens": 6535329, "cached_input_tokens": 6439680, "cache_write_input_tokens": 0, "output_tokens": 22582, "reasoning_output_tokens": 8268, "total_tokens": 6557911 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-3a24dc38-0815-4663-a21a-03988236f1c0", "process_id": "8720", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/pairwise_validation.parquet" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/pairwise_validation.parquet" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{\n \"model\": \"/app/solution/pairwise_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.008895,\n \"quality_users\": 4263\n }\n}\n", "stderr": "", "aggregated_output": "{\n \"model\": \"/app/solution/pairwise_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.008895,\n \"quality_users\": 4263\n }\n}\n", "exit_code": 0, "duration": { "secs": 3, "nanos": 245186263 }, "formatted_output": "{\n \"model\": \"/app/solution/pairwise_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.008895,\n \"quality_users\": 4263\n }\n}\n" } } ## Step 105 · agent · 2026-09-24T23:23:49.417Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T23:23:53.239Z { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python - <<'PY'\\nimport numpy as np,pandas as pd\\nz=np.load('/app/solution/baseline_vectors.npz'); n=np.linalg.norm(z['items'],axis=1); rank=np.arange(1,len(n)+1)\\nprint('norm rank corr',np.corrcoef(n,np.log(rank))[0,1])\\nfor name in ['production','baseline_norm025','semihard','pairwise','hard_ft256']:\\n p=f'/app/solution/{name}_validation.parquet'\\n try:r=pd.read_parquet(p); idx=pd.Index(pd.read_parquet('/task/data/candidate_catalog.parquet').sort_values('popularity_rank').item_id); x=idx.get_indexer(r[r['rank']<=10].item_id); print(name,'median poprank top10',np.median(x+1),'mean',np.mean(x+1),'unique',len(np.unique(x)))\\n except Exception as e:print(e)\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":5000}); text(r.output);" } ### Tool output · 2026-09-24T23:23:55.175Z Script completed Wall time 1.9 seconds Output: norm rank corr 0.1321514229286581 production median poprank top10 111.0 mean 2597.0539025543994 unique 592 baseline_norm025 median poprank top10 185.0 mean 2703.874243140965 unique 644 semihard median poprank top10 1590.0 mean 6045.440314569536 unique 1176 pairwise median poprank top10 1509.0 mean 5969.440811258278 unique 1147 hard_ft256 median poprank top10 3862.0 mean 6648.238339640492 unique 1257 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "session_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "root_turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "response_id": "resp_08539f19231b9e9f016ab5b0fb8f0887d2a86b2eee2c31c8b8", "usage": { "input_tokens": 90046, "cached_input_tokens": 89728, "cache_write_input_tokens": 0, "output_tokens": 709, "reasoning_output_tokens": 450, "total_tokens": 90755 }, "turn_token_usage": { "input_tokens": 6625375, "cached_input_tokens": 6529408, "cache_write_input_tokens": 0, "output_tokens": 23291, "reasoning_output_tokens": 8718, "total_tokens": 6648666 }, "thread_token_usage": { "input_tokens": 6625375, "cached_input_tokens": 6529408, "cache_write_input_tokens": 0, "output_tokens": 23291, "reasoning_output_tokens": 8718, "total_tokens": 6648666 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-26a52b9e-5acd-4444-8089-ec6f1cd43eb0", "process_id": "55246", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\nimport numpy as np,pandas as pd\nz=np.load('/app/solution/baseline_vectors.npz'); n=np.linalg.norm(z['items'],axis=1); rank=np.arange(1,len(n)+1)\nprint('norm rank corr',np.corrcoef(n,np.log(rank))[0,1])\nfor name in ['production','baseline_norm025','semihard','pairwise','hard_ft256']:\n p=f'/app/solution/{name}_validation.parquet'\n try:r=pd.read_parquet(p); idx=pd.Index(pd.read_parquet('/task/data/candidate_catalog.parquet').sort_values('popularity_rank').item_id); x=idx.get_indexer(r[r['rank']<=10].item_id); print(name,'median poprank top10',np.median(x+1),'mean',np.mean(x+1),'unique',len(np.unique(x)))\n except Exception as e:print(e)\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport numpy as np,pandas as pd\nz=np.load('/app/solution/baseline_vectors.npz'); n=np.linalg.norm(z['items'],axis=1); rank=np.arange(1,len(n)+1)\nprint('norm rank corr',np.corrcoef(n,np.log(rank))[0,1])\nfor name in ['production','baseline_norm025','semihard','pairwise','hard_ft256']:\n p=f'/app/solution/{name}_validation.parquet'\n try:r=pd.read_parquet(p); idx=pd.Index(pd.read_parquet('/task/data/candidate_catalog.parquet').sort_values('popularity_rank').item_id); x=idx.get_indexer(r[r['rank']<=10].item_id); print(name,'median poprank top10',np.median(x+1),'mean',np.mean(x+1),'unique',len(np.unique(x)))\n except Exception as e:print(e)\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "norm rank corr 0.1321514229286581\nproduction median poprank top10 111.0 mean 2597.0539025543994 unique 592\nbaseline_norm025 median poprank top10 185.0 mean 2703.874243140965 unique 644\nsemihard median poprank top10 1590.0 mean 6045.440314569536 unique 1176\npairwise median poprank top10 1509.0 mean 5969.440811258278 unique 1147\nhard_ft256 median poprank top10 3862.0 mean 6648.238339640492 unique 1257\n", "stderr": "", "aggregated_output": "norm rank corr 0.1321514229286581\nproduction median poprank top10 111.0 mean 2597.0539025543994 unique 592\nbaseline_norm025 median poprank top10 185.0 mean 2703.874243140965 unique 644\nsemihard median poprank top10 1590.0 mean 6045.440314569536 unique 1176\npairwise median poprank top10 1509.0 mean 5969.440811258278 unique 1147\nhard_ft256 median poprank top10 3862.0 mean 6648.238339640492 unique 1257\n", "exit_code": 0, "duration": { "secs": 1, "nanos": 743744672 }, "formatted_output": "norm rank corr 0.1321514229286581\nproduction median poprank top10 111.0 mean 2597.0539025543994 unique 592\nbaseline_norm025 median poprank top10 185.0 mean 2703.874243140965 unique 644\nsemihard median poprank top10 1590.0 mean 6045.440314569536 unique 1176\npairwise median poprank top10 1509.0 mean 5969.440811258278 unique 1147\nhard_ft256 median poprank top10 3862.0 mean 6648.238339640492 unique 1257\n" } } ## Step 106 · agent · 2026-09-24T23:24:03.055Z ### Thinking unavailable — no text recorded Pairwise training also over-diversifies: its top-10 median popularity rank shifts from 111 to about 1,509, explaining the public drop despite better pairwise validation loss. The successful norm probe only moved that median to 185. I’m therefore testing the smallest principled sampler correction: keep the original 24/8 mix and one epoch, but remove inverse-propensity weighting that currently protects popular in-batch negatives. This should gently penalize recurrent confusions without hard-mining’s distribution shock. ### exec · 2026-09-24T23:24:08.207Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/train_current_model.py\\n@@\\n parser.add_argument(\\n \\\"--inbatch-negatives\\\", type=int, default=Config.inbatch_negatives\\n )\\n+ parser.add_argument(\\n+ \\\"--inbatch-correction-power\\\",\\n+ type=float,\\n+ default=Config.inbatch_correction_power,\\n+ )\\n@@\\n or args.hard_pool_size <= args.inbatch_negatives\\n+ or args.inbatch_correction_power < 0\\n@@\\n inbatch_negatives=args.inbatch_negatives,\\n+ inbatch_correction_power=args.inbatch_correction_power,\\n*** End Patch\"; text(await tools.apply_patch(patch));" } ### Tool output · 2026-09-24T23:24:08.343Z Script completed Wall time 0.0 seconds Output: {} ### Harness context { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-2a956d75-4998-4f84-ab4c-3bb493ce9316", "changes": { "/app/solution/train_current_model.py": { "type": "update", "unified_diff": "@@ -2167,2 +2167,7 @@\n )\n+ parser.add_argument(\n+ \"--inbatch-correction-power\",\n+ type=float,\n+ default=Config.inbatch_correction_power,\n+ )\n parser.add_argument(\"--hard-pool-size\", type=int, default=Config.hard_pool_size)\n@@ -2249,2 +2254,3 @@\n or args.hard_pool_size <= args.inbatch_negatives\n+ or args.inbatch_correction_power < 0\n ):\n@@ -2264,2 +2270,3 @@\n inbatch_negatives=args.inbatch_negatives,\n+ inbatch_correction_power=args.inbatch_correction_power,\n negative_sampling=args.negative_sampling,\n", "move_path": null } }, "status": "completed", "stdout": "Success. Updated the following files:\nM /app/solution/train_current_model.py\n", "stderr": "" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "session_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "root_turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "response_id": "resp_08539f19231b9e9f016ab5b10b61ac87d2a04d710909064d74", "usage": { "input_tokens": 90920, "cached_input_tokens": 89856, "cache_write_input_tokens": 0, "output_tokens": 616, "reasoning_output_tokens": 327, "total_tokens": 91536 }, "turn_token_usage": { "input_tokens": 6716295, "cached_input_tokens": 6619264, "cache_write_input_tokens": 0, "output_tokens": 23907, "reasoning_output_tokens": 9045, "total_tokens": 6740202 }, "thread_token_usage": { "input_tokens": 6716295, "cached_input_tokens": 6619264, "cache_write_input_tokens": 0, "output_tokens": 23907, "reasoning_output_tokens": 9045, "total_tokens": 6740202 } } ## Step 107 · agent · 2026-09-24T23:24:09.872Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T23:24:12.010Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/train_current_model.py\\n@@\\n stored_config.get(\\\"hard_pool_size\\\", 512),\\n stored_config.get(\\\"retrieval_loss\\\", \\\"bce\\\"),\\n+ stored_config.get(\\\"inbatch_correction_power\\\", 0.5),\\n@@\\n config.hard_pool_size,\\n config.retrieval_loss,\\n+ config.inbatch_correction_power,\\n*** End Patch\"; text(await tools.apply_patch(patch));" } ### Tool output · 2026-09-24T23:24:12.095Z Script completed Wall time 0.0 seconds Output: {} ### Harness context { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-4b69a99b-49c2-42e9-8b0c-2c92abbeed98", "changes": { "/app/solution/train_current_model.py": { "type": "update", "unified_diff": "@@ -1825,2 +1825,3 @@\n stored_config.get(\"retrieval_loss\", \"bce\"),\n+ stored_config.get(\"inbatch_correction_power\", 0.5),\n )\n@@ -1832,2 +1833,3 @@\n config.retrieval_loss,\n+ config.inbatch_correction_power,\n )\n", "move_path": null } }, "status": "completed", "stdout": "Success. Updated the following files:\nM /app/solution/train_current_model.py\n", "stderr": "" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "session_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "root_turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "response_id": "resp_08539f19231b9e9f016ab5b1189ddc87d29813d8aca74ef8ae", "usage": { "input_tokens": 91560, "cached_input_tokens": 90752, "cache_write_input_tokens": 0, "output_tokens": 146, "reasoning_output_tokens": 21, "total_tokens": 91706 }, "turn_token_usage": { "input_tokens": 6807855, "cached_input_tokens": 6710016, "cache_write_input_tokens": 0, "output_tokens": 24053, "reasoning_output_tokens": 9066, "total_tokens": 6831908 }, "thread_token_usage": { "input_tokens": 6807855, "cached_input_tokens": 6710016, "cache_write_input_tokens": 0, "output_tokens": 24053, "reasoning_output_tokens": 9066, "total_tokens": 6831908 } } ## Step 108 · agent · 2026-09-24T23:24:14.333Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T23:24:17.497Z { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python -m py_compile /app/solution/train_current_model.py && /opt/task-venv/bin/python /app/solution/train_current_model.py --stage validation --public-dir /task --prepared-cache /app/solution/validation_sequences.npz --negative-sampling mixed --uniform-negatives 24 --inbatch-negatives 8 --inbatch-correction-power 0 --retrieval-loss bce --epochs 1 --checkpoint-dir /app/solution/uncorrected_checkpoints --output /app/solution/uncorrected_validation.parquet 2>&1 | tee /app/solution/uncorrected_validation.log\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":12000}); text(JSON.stringify(r));" } ### Tool output · 2026-09-24T23:24:18.665Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"d2745c","wall_time_seconds":1.000617216,"session_id":45395,"original_token_count":0,"output":""} ### Harness context { "type": "token_usage_record", "thread_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "session_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "root_turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "response_id": "resp_08539f19231b9e9f016ab5b11c6efc87d28bfb4ad66136fcc7", "usage": { "input_tokens": 91730, "cached_input_tokens": 91392, "cache_write_input_tokens": 0, "output_tokens": 202, "reasoning_output_tokens": 19, "total_tokens": 91932 }, "turn_token_usage": { "input_tokens": 6899585, "cached_input_tokens": 6801408, "cache_write_input_tokens": 0, "output_tokens": 24255, "reasoning_output_tokens": 9085, "total_tokens": 6923840 }, "thread_token_usage": { "input_tokens": 6899585, "cached_input_tokens": 6801408, "cache_write_input_tokens": 0, "output_tokens": 24255, "reasoning_output_tokens": 9085, "total_tokens": 6923840 } } ## Step 109 · agent · 2026-09-24T23:24:20.060Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T23:24:20.770Z { "code": "const r = await tools.write_stdin({\"session_id\":45395,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":12000}); text(r.output);\n" } ### Tool output · 2026-09-24T23:24:50.791Z Script completed Wall time 30.0 seconds Output: loaded full-sequence cache: tokens=20,056,329 targets=20,047,194 chronological BCE window: train=1,052,953 validation=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194 inbatch correction: min=1.000 mean_on_targets=1.000 max=1.000 target weighting=organic-strong min=0.250 mean=0.575 max=1.000 full-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "session_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "root_turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "response_id": "resp_08539f19231b9e9f016ab5b122da2887d2afe36cd944b5d997", "usage": { "input_tokens": 91990, "cached_input_tokens": 91520, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 92043 }, "turn_token_usage": { "input_tokens": 6991575, "cached_input_tokens": 6892928, "cache_write_input_tokens": 0, "output_tokens": 24308, "reasoning_output_tokens": 9091, "total_tokens": 7015883 }, "thread_token_usage": { "input_tokens": 6991575, "cached_input_tokens": 6892928, "cache_write_input_tokens": 0, "output_tokens": 24308, "reasoning_output_tokens": 9091, "total_tokens": 7015883 } } ## Step 110 · agent · 2026-09-24T23:24:52.084Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T23:24:52.759Z { "code": "const r = await tools.write_stdin({\"session_id\":45395,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":12000}); text(r.output);\n" } ### Tool output · 2026-09-24T23:25:22.799Z Script completed Wall time 30.0 seconds Output: ### Harness context { "type": "token_usage_record", "thread_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "session_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "root_turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "response_id": "resp_08539f19231b9e9f016ab5b14301b487d2b226d25f536a4614", "usage": { "input_tokens": 92200, "cached_input_tokens": 91776, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 92253 }, "turn_token_usage": { "input_tokens": 7083775, "cached_input_tokens": 6984704, "cache_write_input_tokens": 0, "output_tokens": 24361, "reasoning_output_tokens": 9097, "total_tokens": 7108136 }, "thread_token_usage": { "input_tokens": 7083775, "cached_input_tokens": 6984704, "cache_write_input_tokens": 0, "output_tokens": 24361, "reasoning_output_tokens": 9097, "total_tokens": 7108136 } } ### exec · 2026-09-24T23:25:25.201Z { "code": "const r = await tools.write_stdin({\"session_id\":45395,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":12000}); text(r.output);\n" } ### Tool output · 2026-09-24T23:25:55.231Z Script completed Wall time 30.0 seconds Output: ### Harness context { "type": "token_usage_record", "thread_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "session_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "root_turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "response_id": "resp_08539f19231b9e9f016ab5b1632b6887d2ab9c4a4c37b5bfe0", "usage": { "input_tokens": 92276, "cached_input_tokens": 92032, "cache_write_input_tokens": 0, "output_tokens": 45, "reasoning_output_tokens": 0, "total_tokens": 92321 }, "turn_token_usage": { "input_tokens": 7176051, "cached_input_tokens": 7076736, "cache_write_input_tokens": 0, "output_tokens": 24406, "reasoning_output_tokens": 9097, "total_tokens": 7200457 }, "thread_token_usage": { "input_tokens": 7176051, "cached_input_tokens": 7076736, "cache_write_input_tokens": 0, "output_tokens": 24406, "reasoning_output_tokens": 9097, "total_tokens": 7200457 } } ### exec · 2026-09-24T23:25:57.129Z { "code": "const r = await tools.write_stdin({\"session_id\":45395,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":12000}); text(r.output);\n" } ### Tool output · 2026-09-24T23:26:27.160Z Script completed Wall time 30.0 seconds Output: ### Harness context { "type": "token_usage_record", "thread_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "session_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "root_turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "response_id": "resp_08539f19231b9e9f016ab5b183792887d28fc6b6394a73a3c1", "usage": { "input_tokens": 92344, "cached_input_tokens": 92160, "cache_write_input_tokens": 0, "output_tokens": 45, "reasoning_output_tokens": 0, "total_tokens": 92389 }, "turn_token_usage": { "input_tokens": 7268395, "cached_input_tokens": 7168896, "cache_write_input_tokens": 0, "output_tokens": 24451, "reasoning_output_tokens": 9097, "total_tokens": 7292846 }, "thread_token_usage": { "input_tokens": 7268395, "cached_input_tokens": 7168896, "cache_write_input_tokens": 0, "output_tokens": 24451, "reasoning_output_tokens": 9097, "total_tokens": 7292846 } } ## Step 111 · agent · 2026-09-24T23:26:28.476Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T23:26:29.192Z { "code": "const r = await tools.write_stdin({\"session_id\":45395,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":12000}); text(r.output);\n" } ### Tool output · 2026-09-24T23:26:59.203Z Script completed Wall time 30.0 seconds Output: ### Harness context { "type": "token_usage_record", "thread_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "session_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "root_turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "response_id": "resp_08539f19231b9e9f016ab5b1a3605887d2966548ea10e424c4", "usage": { "input_tokens": 92412, "cached_input_tokens": 92160, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 92465 }, "turn_token_usage": { "input_tokens": 7360807, "cached_input_tokens": 7261056, "cache_write_input_tokens": 0, "output_tokens": 24504, "reasoning_output_tokens": 9103, "total_tokens": 7385311 }, "thread_token_usage": { "input_tokens": 7360807, "cached_input_tokens": 7261056, "cache_write_input_tokens": 0, "output_tokens": 24504, "reasoning_output_tokens": 9103, "total_tokens": 7385311 } } ## Step 112 · agent · 2026-09-24T23:27:00.674Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T23:27:01.496Z { "code": "const r = await tools.write_stdin({\"session_id\":45395,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":12000}); text(r.output);\n" } ### Tool output · 2026-09-24T23:27:31.530Z Script completed Wall time 30.0 seconds Output: ### Harness context { "type": "token_usage_record", "thread_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "session_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "root_turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "response_id": "resp_08539f19231b9e9f016ab5b1c37c2887d28bc6d80bc8b7dc82", "usage": { "input_tokens": 92488, "cached_input_tokens": 92288, "cache_write_input_tokens": 0, "output_tokens": 54, "reasoning_output_tokens": 7, "total_tokens": 92542 }, "turn_token_usage": { "input_tokens": 7453295, "cached_input_tokens": 7353344, "cache_write_input_tokens": 0, "output_tokens": 24558, "reasoning_output_tokens": 9110, "total_tokens": 7477853 }, "thread_token_usage": { "input_tokens": 7453295, "cached_input_tokens": 7353344, "cache_write_input_tokens": 0, "output_tokens": 24558, "reasoning_output_tokens": 9110, "total_tokens": 7477853 } } ## Step 113 · agent · 2026-09-24T23:27:32.842Z ### Thinking unavailable — no text recorded This final ablation is deliberately production-sized and cheap: 32 negatives total, no extra model scoring pass, and no architecture change. The only change is that all eight in-batch negatives now contribute equally instead of downweighting frequently occurring—typically more confusable—tracks by as much as 4×. ### exec · 2026-09-24T23:27:34.869Z { "code": "const r = await tools.write_stdin({\"session_id\":45395,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":12000}); text(r.output);\n" } ### Tool output · 2026-09-24T23:28:04.896Z Script completed Wall time 30.0 seconds Output: epoch=1/1 batches=250 targets=987,417 batch_bce=0.562498 running_bce=0.562160 elapsed=197.0s ### Harness context { "type": "token_usage_record", "thread_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "session_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "root_turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "response_id": "resp_08539f19231b9e9f016ab5b1e3b78c87d28f638b21f77a3e6d", "usage": { "input_tokens": 92565, "cached_input_tokens": 92288, "cache_write_input_tokens": 0, "output_tokens": 126, "reasoning_output_tokens": 9, "total_tokens": 92691 }, "turn_token_usage": { "input_tokens": 7545860, "cached_input_tokens": 7445632, "cache_write_input_tokens": 0, "output_tokens": 24684, "reasoning_output_tokens": 9119, "total_tokens": 7570544 }, "thread_token_usage": { "input_tokens": 7545860, "cached_input_tokens": 7445632, "cache_write_input_tokens": 0, "output_tokens": 24684, "reasoning_output_tokens": 9119, "total_tokens": 7570544 } } ## Step 114 · agent · 2026-09-24T23:28:06.600Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T23:28:07.340Z { "code": "const r = await tools.write_stdin({\"session_id\":45395,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":12000}); text(r.output);\n" } ### Tool output · 2026-09-24T23:28:37.359Z Script completed Wall time 30.0 seconds Output: saved epoch checkpoint: /app/solution/uncorrected_checkpoints/epoch-01.pt epoch=1/1 train_loss=0.56012 validation_loss=0.51430 pair_accuracy=0.757 train_targets=1,052,953 validation_targets=130,103 train_seconds=210.6 validation_seconds=16.4 restored best validation-BCE checkpoint: epoch=1 loss=0.514304 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "session_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "root_turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "response_id": "resp_08539f19231b9e9f016ab5b205283887d2acdff9babeb2ced2", "usage": { "input_tokens": 92750, "cached_input_tokens": 92416, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 92803 }, "turn_token_usage": { "input_tokens": 7638610, "cached_input_tokens": 7538048, "cache_write_input_tokens": 0, "output_tokens": 24737, "reasoning_output_tokens": 9125, "total_tokens": 7663347 }, "thread_token_usage": { "input_tokens": 7638610, "cached_input_tokens": 7538048, "cache_write_input_tokens": 0, "output_tokens": 24737, "reasoning_output_tokens": 9125, "total_tokens": 7663347 } } ## Step 115 · agent · 2026-09-24T23:28:38.617Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T23:28:39.334Z { "code": "const r = await tools.write_stdin({\"session_id\":45395,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":12000}); text(r.output);\n" } ### Tool output · 2026-09-24T23:29:09.383Z Script completed Wall time 30.0 seconds Output: ### Harness context { "type": "token_usage_record", "thread_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "session_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "root_turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "response_id": "resp_08539f19231b9e9f016ab5b225982887d29792cd4f77f68c5e", "usage": { "input_tokens": 92916, "cached_input_tokens": 92544, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 92969 }, "turn_token_usage": { "input_tokens": 7731526, "cached_input_tokens": 7630592, "cache_write_input_tokens": 0, "output_tokens": 24790, "reasoning_output_tokens": 9131, "total_tokens": 7756316 }, "thread_token_usage": { "input_tokens": 7731526, "cached_input_tokens": 7630592, "cache_write_input_tokens": 0, "output_tokens": 24790, "reasoning_output_tokens": 9131, "total_tokens": 7756316 } } ### exec · 2026-09-24T23:29:11.321Z { "code": "const r = await tools.write_stdin({\"session_id\":45395,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":12000}); text(r.output);\n" } ### Tool output · 2026-09-24T23:29:33.551Z Script completed Wall time 22.2 seconds Output: ranked users=1,024/8,456 ranked users=2,048/8,456 ranked users=3,072/8,456 ranked users=4,096/8,456 ranked users=5,120/8,456 ranked users=6,144/8,456 ranked users=7,168/8,456 ranked users=8,192/8,456 ranked users=8,456/8,456 { "baseline": "yambda-windowed-sequence-two-tower-base", "stage": "validation", "output": "/app/solution/uncorrected_validation.parquet", "rows": 845600, "users": 8456, "runtime_seconds": 311.343, "public_inputs_only": true, "architecture": { "family": "pure-windowed-sequence-two-tower", "sequence_training": "every-adjacent-target-within-recent-chronological-window", "sequence_encoder": "one-layer-GRU-dense-timestep-loss", "bptt_hidden_state": "carried-and-detached", "model_selection": "chronological-validation-BCE-early-stopping", "loss_execution": "gradient-accumulated-microbatches", "user_id_embedding": false, "user_mlp": "582-512-256-192", "item_mlp": "196-512-256-192", "normalization": "none", "score": "raw-dot-product", "objective": "sample-weighted-bce-at-every-timestep", "negative_sampling": "24-uniform-plus-8-mixed", "false_negative_filter": "full-training-history", "target_weighting": "organic-strong" }, "config": { "bptt_steps": 100, "user_batch_size": 64, "embedding_dim": 192, "hidden_dim": 192, "user_hidden_1": 512, "user_hidden_2": 256, "item_hidden_1": 512, "item_hidden_2": 256, "dropout": 0.05, "epochs": 1, "learning_rate": 0.001, "weight_decay": 1e-05, "gradient_clip": 2.0, "loss_microbatch_size": 65536, "uniform_negatives": 24, "inbatch_negatives": 8, "negative_sampling": "mixed", "hard_pool_size": 512, "retrieval_loss": "bce", "inbatch_correction_power": 0.0, "inbatch_correction_min": 0.25, "inbatch_correction_max": 4.0, "negative_filter_scope": "full-training-history", "target_weighting": "organic-strong", "ordinary_target_weight": 0.25, "organic_strong_target_weight": 1.0, "organic_like_target_weight": 3.0, "discovery_horizon_days": 7, "validation_fraction": 0.1, "max_train_targets_per_user": 128, "max_validation_targets_per_user": 16, "early_stopping_patience": 1, "early_stopping_min_delta": 0.0001, "seed": 29 }, "sequence_tokens": 20056329, "all_next_item_targets": 20047194, "selected_training_targets": 1052953, "selected_validation_targets": 130103, "discarded_old_targets": 18864138, "training_history": [ { "epoch": 1.0, "loss": 0.5601169335431502, "positive_logit": 0.5843293811122111, "negative_logit": -0.6334363910318817, "pair_accuracy": 0.7566698133724867, "targets": 1052953.0, "target_weight_sum": 632551.0, "batches": 266.0, "seconds": 210.61621221699988, "validation_loss": 0.5143042046973899, "validation_targets": 130103.0, "validation_target_weight_sum": 78769.25, "validation_seconds": 16.38135922099991 } ], "embedding_diagnostics": { "query_norm_mean": 1.6756763458251953, "query_norm_std": 0.8129032254219055, "item_norm_mean": 1.8248878717422485, "item_norm_std": 0.8556308150291443 }, "torch_version": "2.8.0+cpu", "device": "cpu" } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "session_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "root_turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "response_id": "resp_08539f19231b9e9f016ab5b2459f5087d280763b20574b1d52", "usage": { "input_tokens": 92992, "cached_input_tokens": 92800, "cache_write_input_tokens": 0, "output_tokens": 45, "reasoning_output_tokens": 0, "total_tokens": 93037 }, "turn_token_usage": { "input_tokens": 7824518, "cached_input_tokens": 7723392, "cache_write_input_tokens": 0, "output_tokens": 24835, "reasoning_output_tokens": 9131, "total_tokens": 7849353 }, "thread_token_usage": { "input_tokens": 7824518, "cached_input_tokens": 7723392, "cache_write_input_tokens": 0, "output_tokens": 24835, "reasoning_output_tokens": 9131, "total_tokens": 7849353 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-9cfb8b52-a4c8-4b65-9c56-b00b4198defb", "process_id": "45395", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python -m py_compile /app/solution/train_current_model.py && /opt/task-venv/bin/python /app/solution/train_current_model.py --stage validation --public-dir /task --prepared-cache /app/solution/validation_sequences.npz --negative-sampling mixed --uniform-negatives 24 --inbatch-negatives 8 --inbatch-correction-power 0 --retrieval-loss bce --epochs 1 --checkpoint-dir /app/solution/uncorrected_checkpoints --output /app/solution/uncorrected_validation.parquet 2>&1 | tee /app/solution/uncorrected_validation.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python -m py_compile /app/solution/train_current_model.py && /opt/task-venv/bin/python /app/solution/train_current_model.py --stage validation --public-dir /task --prepared-cache /app/solution/validation_sequences.npz --negative-sampling mixed --uniform-negatives 24 --inbatch-negatives 8 --inbatch-correction-power 0 --retrieval-loss bce --epochs 1 --checkpoint-dir /app/solution/uncorrected_checkpoints --output /app/solution/uncorrected_validation.parquet 2>&1 | tee /app/solution/uncorrected_validation.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "loaded full-sequence cache: tokens=20,056,329 targets=20,047,194\nchronological BCE window: train=1,052,953 validation=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194\ninbatch correction: min=1.000 mean_on_targets=1.000 max=1.000\ntarget weighting=organic-strong min=0.250 mean=0.575 max=1.000\nfull-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250\nepoch=1/1 batches=250 targets=987,417 batch_bce=0.562498 running_bce=0.562160 elapsed=197.0s\nsaved epoch checkpoint: /app/solution/uncorrected_checkpoints/epoch-01.pt\nepoch=1/1 train_loss=0.56012 validation_loss=0.51430 pair_accuracy=0.757 train_targets=1,052,953 validation_targets=130,103 train_seconds=210.6 validation_seconds=16.4\nrestored best validation-BCE checkpoint: epoch=1 loss=0.514304\nranked users=1,024/8,456\nranked users=2,048/8,456\nranked users=3,072/8,456\nranked users=4,096/8,456\nranked users=5,120/8,456\nranked users=6,144/8,456\nranked users=7,168/8,456\nranked users=8,192/8,456\nranked users=8,456/8,456\n{\n \"baseline\": \"yambda-windowed-sequence-two-tower-base\",\n \"stage\": \"validation\",\n \"output\": \"/app/solution/uncorrected_validation.parquet\",\n \"rows\": 845600,\n \"users\": 8456,\n \"runtime_seconds\": 311.343,\n \"public_inputs_only\": true,\n \"architecture\": {\n \"family\": \"pure-windowed-sequence-two-tower\",\n \"sequence_training\": \"every-adjacent-target-within-recent-chronological-window\",\n \"sequence_encoder\": \"one-layer-GRU-dense-timestep-loss\",\n \"bptt_hidden_state\": \"carried-and-detached\",\n \"model_selection\": \"chronological-validation-BCE-early-stopping\",\n \"loss_execution\": \"gradient-accumulated-microbatches\",\n \"user_id_embedding\": false,\n \"user_mlp\": \"582-512-256-192\",\n \"item_mlp\": \"196-512-256-192\",\n \"normalization\": \"none\",\n \"score\": \"raw-dot-product\",\n \"objective\": \"sample-weighted-bce-at-every-timestep\",\n \"negative_sampling\": \"24-uniform-plus-8-mixed\",\n \"false_negative_filter\": \"full-training-history\",\n \"target_weighting\": \"organic-strong\"\n },\n \"config\": {\n \"bptt_steps\": 100,\n \"user_batch_size\": 64,\n \"embedding_dim\": 192,\n \"hidden_dim\": 192,\n \"user_hidden_1\": 512,\n \"user_hidden_2\": 256,\n \"item_hidden_1\": 512,\n \"item_hidden_2\": 256,\n \"dropout\": 0.05,\n \"epochs\": 1,\n \"learning_rate\": 0.001,\n \"weight_decay\": 1e-05,\n \"gradient_clip\": 2.0,\n \"loss_microbatch_size\": 65536,\n \"uniform_negatives\": 24,\n \"inbatch_negatives\": 8,\n \"negative_sampling\": \"mixed\",\n \"hard_pool_size\": 512,\n \"retrieval_loss\": \"bce\",\n \"inbatch_correction_power\": 0.0,\n \"inbatch_correction_min\": 0.25,\n \"inbatch_correction_max\": 4.0,\n \"negative_filter_scope\": \"full-training-history\",\n \"target_weighting\": \"organic-strong\",\n \"ordinary_target_weight\": 0.25,\n \"organic_strong_target_weight\": 1.0,\n \"organic_like_target_weight\": 3.0,\n \"discovery_horizon_days\": 7,\n \"validation_fraction\": 0.1,\n \"max_train_targets_per_user\": 128,\n \"max_validation_targets_per_user\": 16,\n \"early_stopping_patience\": 1,\n \"early_stopping_min_delta\": 0.0001,\n \"seed\": 29\n },\n \"sequence_tokens\": 20056329,\n \"all_next_item_targets\": 20047194,\n \"selected_training_targets\": 1052953,\n \"selected_validation_targets\": 130103,\n \"discarded_old_targets\": 18864138,\n \"training_history\": [\n {\n \"epoch\": 1.0,\n \"loss\": 0.5601169335431502,\n \"positive_logit\": 0.5843293811122111,\n \"negative_logit\": -0.6334363910318817,\n \"pair_accuracy\": 0.7566698133724867,\n \"targets\": 1052953.0,\n \"target_weight_sum\": 632551.0,\n \"batches\": 266.0,\n \"seconds\": 210.61621221699988,\n \"validation_loss\": 0.5143042046973899,\n \"validation_targets\": 130103.0,\n \"validation_target_weight_sum\": 78769.25,\n \"validation_seconds\": 16.38135922099991\n }\n ],\n \"embedding_diagnostics\": {\n \"query_norm_mean\": 1.6756763458251953,\n \"query_norm_std\": 0.8129032254219055,\n \"item_norm_mean\": 1.8248878717422485,\n \"item_norm_std\": 0.8556308150291443\n },\n \"torch_version\": \"2.8.0+cpu\",\n \"device\": \"cpu\"\n}\n", "stderr": "", "aggregated_output": "loaded full-sequence cache: tokens=20,056,329 targets=20,047,194\nchronological BCE window: train=1,052,953 validation=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194\ninbatch correction: min=1.000 mean_on_targets=1.000 max=1.000\ntarget weighting=organic-strong min=0.250 mean=0.575 max=1.000\nfull-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250\nepoch=1/1 batches=250 targets=987,417 batch_bce=0.562498 running_bce=0.562160 elapsed=197.0s\nsaved epoch checkpoint: /app/solution/uncorrected_checkpoints/epoch-01.pt\nepoch=1/1 train_loss=0.56012 validation_loss=0.51430 pair_accuracy=0.757 train_targets=1,052,953 validation_targets=130,103 train_seconds=210.6 validation_seconds=16.4\nrestored best validation-BCE checkpoint: epoch=1 loss=0.514304\nranked users=1,024/8,456\nranked users=2,048/8,456\nranked users=3,072/8,456\nranked users=4,096/8,456\nranked users=5,120/8,456\nranked users=6,144/8,456\nranked users=7,168/8,456\nranked users=8,192/8,456\nranked users=8,456/8,456\n{\n \"baseline\": \"yambda-windowed-sequence-two-tower-base\",\n \"stage\": \"validation\",\n \"output\": \"/app/solution/uncorrected_validation.parquet\",\n \"rows\": 845600,\n \"users\": 8456,\n \"runtime_seconds\": 311.343,\n \"public_inputs_only\": true,\n \"architecture\": {\n \"family\": \"pure-windowed-sequence-two-tower\",\n \"sequence_training\": \"every-adjacent-target-within-recent-chronological-window\",\n \"sequence_encoder\": \"one-layer-GRU-dense-timestep-loss\",\n \"bptt_hidden_state\": \"carried-and-detached\",\n \"model_selection\": \"chronological-validation-BCE-early-stopping\",\n \"loss_execution\": \"gradient-accumulated-microbatches\",\n \"user_id_embedding\": false,\n \"user_mlp\": \"582-512-256-192\",\n \"item_mlp\": \"196-512-256-192\",\n \"normalization\": \"none\",\n \"score\": \"raw-dot-product\",\n \"objective\": \"sample-weighted-bce-at-every-timestep\",\n \"negative_sampling\": \"24-uniform-plus-8-mixed\",\n \"false_negative_filter\": \"full-training-history\",\n \"target_weighting\": \"organic-strong\"\n },\n \"config\": {\n \"bptt_steps\": 100,\n \"user_batch_size\": 64,\n \"embedding_dim\": 192,\n \"hidden_dim\": 192,\n \"user_hidden_1\": 512,\n \"user_hidden_2\": 256,\n \"item_hidden_1\": 512,\n \"item_hidden_2\": 256,\n \"dropout\": 0.05,\n \"epochs\": 1,\n \"learning_rate\": 0.001,\n \"weight_decay\": 1e-05,\n \"gradient_clip\": 2.0,\n \"loss_microbatch_size\": 65536,\n \"uniform_negatives\": 24,\n \"inbatch_negatives\": 8,\n \"negative_sampling\": \"mixed\",\n \"hard_pool_size\": 512,\n \"retrieval_loss\": \"bce\",\n \"inbatch_correction_power\": 0.0,\n \"inbatch_correction_min\": 0.25,\n \"inbatch_correction_max\": 4.0,\n \"negative_filter_scope\": \"full-training-history\",\n \"target_weighting\": \"organic-strong\",\n \"ordinary_target_weight\": 0.25,\n \"organic_strong_target_weight\": 1.0,\n \"organic_like_target_weight\": 3.0,\n \"discovery_horizon_days\": 7,\n \"validation_fraction\": 0.1,\n \"max_train_targets_per_user\": 128,\n \"max_validation_targets_per_user\": 16,\n \"early_stopping_patience\": 1,\n \"early_stopping_min_delta\": 0.0001,\n \"seed\": 29\n },\n \"sequence_tokens\": 20056329,\n \"all_next_item_targets\": 20047194,\n \"selected_training_targets\": 1052953,\n \"selected_validation_targets\": 130103,\n \"discarded_old_targets\": 18864138,\n \"training_history\": [\n {\n \"epoch\": 1.0,\n \"loss\": 0.5601169335431502,\n \"positive_logit\": 0.5843293811122111,\n \"negative_logit\": -0.6334363910318817,\n \"pair_accuracy\": 0.7566698133724867,\n \"targets\": 1052953.0,\n \"target_weight_sum\": 632551.0,\n \"batches\": 266.0,\n \"seconds\": 210.61621221699988,\n \"validation_loss\": 0.5143042046973899,\n \"validation_targets\": 130103.0,\n \"validation_target_weight_sum\": 78769.25,\n \"validation_seconds\": 16.38135922099991\n }\n ],\n \"embedding_diagnostics\": {\n \"query_norm_mean\": 1.6756763458251953,\n \"query_norm_std\": 0.8129032254219055,\n \"item_norm_mean\": 1.8248878717422485,\n \"item_norm_std\": 0.8556308150291443\n },\n \"torch_version\": \"2.8.0+cpu\",\n \"device\": \"cpu\"\n}\n", "exit_code": 0, "duration": { "secs": 315, "nanos": 877759436 }, "formatted_output": "loaded full-sequence cache: tokens=20,056,329 targets=20,047,194\nchronological BCE window: train=1,052,953 validation=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194\ninbatch correction: min=1.000 mean_on_targets=1.000 max=1.000\ntarget weighting=organic-strong min=0.250 mean=0.575 max=1.000\nfull-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250\nepoch=1/1 batches=250 targets=987,417 batch_bce=0.562498 running_bce=0.562160 elapsed=197.0s\nsaved epoch checkpoint: /app/solution/uncorrected_checkpoints/epoch-01.pt\nepoch=1/1 train_loss=0.56012 validation_loss=0.51430 pair_accuracy=0.757 train_targets=1,052,953 validation_targets=130,103 train_seconds=210.6 validation_seconds=16.4\nrestored best validation-BCE checkpoint: epoch=1 loss=0.514304\nranked users=1,024/8,456\nranked users=2,048/8,456\nranked users=3,072/8,456\nranked users=4,096/8,456\nranked users=5,120/8,456\nranked users=6,144/8,456\nranked users=7,168/8,456\nranked users=8,192/8,456\nranked users=8,456/8,456\n{\n \"baseline\": \"yambda-windowed-sequence-two-tower-base\",\n \"stage\": \"validation\",\n \"output\": \"/app/solution/uncorrected_validation.parquet\",\n \"rows\": 845600,\n \"users\": 8456,\n \"runtime_seconds\": 311.343,\n \"public_inputs_only\": true,\n \"architecture\": {\n \"family\": \"pure-windowed-sequence-two-tower\",\n \"sequence_training\": \"every-adjacent-target-within-recent-chronological-window\",\n \"sequence_encoder\": \"one-layer-GRU-dense-timestep-loss\",\n \"bptt_hidden_state\": \"carried-and-detached\",\n \"model_selection\": \"chronological-validation-BCE-early-stopping\",\n \"loss_execution\": \"gradient-accumulated-microbatches\",\n \"user_id_embedding\": false,\n \"user_mlp\": \"582-512-256-192\",\n \"item_mlp\": \"196-512-256-192\",\n \"normalization\": \"none\",\n \"score\": \"raw-dot-product\",\n \"objective\": \"sample-weighted-bce-at-every-timestep\",\n \"negative_sampling\": \"24-uniform-plus-8-mixed\",\n \"false_negative_filter\": \"full-training-history\",\n \"target_weighting\": \"organic-strong\"\n },\n \"config\": {\n \"bptt_steps\": 100,\n \"user_batch_size\": 64,\n \"embedding_dim\": 192,\n \"hidden_dim\": 192,\n \"user_hidden_1\": 512,\n \"user_hidden_2\": 256,\n \"item_hidden_1\": 512,\n \"item_hidden_2\": 256,\n \"dropout\": 0.05,\n \"epochs\": 1,\n \"learning_rate\": 0.001,\n \"weight_decay\": 1e-05,\n \"gradient_clip\": 2.0,\n \"loss_microbatch_size\": 65536,\n \"uniform_negatives\": 24,\n \"inbatch_negatives\": 8,\n \"negative_sampling\": \"mixed\",\n \"hard_pool_size\": 512,\n \"retrieval_loss\": \"bce\",\n \"inbatch_correction_power\": 0.0,\n \"inbatch_correction_min\": 0.25,\n \"inbatch_correction_max\": 4.0,\n \"negative_filter_scope\": \"full-training-history\",\n \"target_weighting\": \"organic-strong\",\n \"ordinary_target_weight\": 0.25,\n \"organic_strong_target_weight\": 1.0,\n \"organic_like_target_weight\": 3.0,\n \"discovery_horizon_days\": 7,\n \"validation_fraction\": 0.1,\n \"max_train_targets_per_user\": 128,\n \"max_validation_targets_per_user\": 16,\n \"early_stopping_patience\": 1,\n \"early_stopping_min_delta\": 0.0001,\n \"seed\": 29\n },\n \"sequence_tokens\": 20056329,\n \"all_next_item_targets\": 20047194,\n \"selected_training_targets\": 1052953,\n \"selected_validation_targets\": 130103,\n \"discarded_old_targets\": 18864138,\n \"training_history\": [\n {\n \"epoch\": 1.0,\n \"loss\": 0.5601169335431502,\n \"positive_logit\": 0.5843293811122111,\n \"negative_logit\": -0.6334363910318817,\n \"pair_accuracy\": 0.7566698133724867,\n \"targets\": 1052953.0,\n \"target_weight_sum\": 632551.0,\n \"batches\": 266.0,\n \"seconds\": 210.61621221699988,\n \"validation_loss\": 0.5143042046973899,\n \"validation_targets\": 130103.0,\n \"validation_target_weight_sum\": 78769.25,\n \"validation_seconds\": 16.38135922099991\n }\n ],\n \"embedding_diagnostics\": {\n \"query_norm_mean\": 1.6756763458251953,\n \"query_norm_std\": 0.8129032254219055,\n \"item_norm_mean\": 1.8248878717422485,\n \"item_norm_std\": 0.8556308150291443\n },\n \"torch_version\": \"2.8.0+cpu\",\n \"device\": \"cpu\"\n}\n" } } ## Step 116 · agent · 2026-09-24T23:29:34.993Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T23:29:36.232Z { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/uncorrected_validation.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":5000}); text(r.output);" } ### Tool output · 2026-09-24T23:29:39.687Z Script completed Wall time 3.5 seconds Output: { "model": "/app/solution/uncorrected_validation.parquet", "scores": { "quality_ndcg@10": 0.018522, "quality_users": 4263 } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "session_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "root_turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "response_id": "resp_08539f19231b9e9f016ab5b25dc12087d29cc268ef660211a6", "usage": { "input_tokens": 94144, "cached_input_tokens": 92800, "cache_write_input_tokens": 0, "output_tokens": 78, "reasoning_output_tokens": 6, "total_tokens": 94222 }, "turn_token_usage": { "input_tokens": 7918662, "cached_input_tokens": 7816192, "cache_write_input_tokens": 0, "output_tokens": 24913, "reasoning_output_tokens": 9137, "total_tokens": 7943575 }, "thread_token_usage": { "input_tokens": 7918662, "cached_input_tokens": 7816192, "cache_write_input_tokens": 0, "output_tokens": 24913, "reasoning_output_tokens": 9137, "total_tokens": 7943575 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-c478394b-cd5a-4386-b0ca-7b52a617495a", "process_id": "93221", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/uncorrected_validation.parquet" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/uncorrected_validation.parquet" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{\n \"model\": \"/app/solution/uncorrected_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.018522,\n \"quality_users\": 4263\n }\n}\n", "stderr": "", "aggregated_output": "{\n \"model\": \"/app/solution/uncorrected_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.018522,\n \"quality_users\": 4263\n }\n}\n", "exit_code": 0, "duration": { "secs": 3, "nanos": 271605533 }, "formatted_output": "{\n \"model\": \"/app/solution/uncorrected_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.018522,\n \"quality_users\": 4263\n }\n}\n" } } ## Step 117 · agent · 2026-09-24T23:29:50.170Z ### Thinking unavailable — no text recorded ## Step 118 · agent · 2026-09-24T23:29:51.113Z ### Thinking unavailable — no text recorded Equal-weight in-batch sampling scores `0.018522`, so the baseline’s propensity correction is beneficial and should remain. Across the measured variants, the only validated gain is the conservative offline item-vector scaling (`0.021565` vs `0.021280`), which preserves the exact one-query/one-indexed-dot-product serving path. I’m locking that safe gain in and starting the final cutoff-correct refit now; the more aggressive negative-objective variants will remain documented as rejected ablations. ### exec · 2026-09-24T23:30:00.299Z { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python - <<'PY'\\nimport numpy as np,pandas as pd,torch\\nD='/task/data'; S='/app/solution'; z=np.load(f'{S}/baseline_vectors.npz'); q=torch.from_numpy(z['queries']); it=torch.from_numpy(z['items']); norm=torch.linalg.vector_norm(it,dim=1).clamp_min(1e-8); a=np.load(f'{D}/validation_eligible_candidates.npz'); ids=a['item_ids']; uids=a['uids']; packed=a['packed_eligible']\\nfor power in [.10,.15,.20,.30,.35,.40]:\\n rows=[]; iv=it/norm[:,None].pow(power)\\n for start in range(0,len(uids),64):\\n end=min(start+64,len(uids)); scores=q[start:end]@iv.T; eligible=np.unpackbits(packed[start:end],axis=1,count=len(ids),bitorder='little').astype(bool); scores.masked_fill_(~torch.from_numpy(eligible),-torch.inf); top=torch.topk(scores,100,dim=1).indices.numpy()\\n for lr,cols in enumerate(top): rows.extend((int(uids[start+lr]),int(ids[c]),r) for r,c in enumerate(cols,1))\\n pd.DataFrame(rows,columns=['uid','item_id','rank']).to_parquet(f'{S}/norm{int(power*100):02d}_validation.parquet',index=False)\\nPY\\nfor f in /app/solution/norm*_validation.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \\\"$f\\\"; done\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":12000}); text(r.output);" } ### Tool output · 2026-09-24T23:30:30.473Z Script completed Wall time 30.2 seconds Output: { "model": "/app/solution/norm10_validation.parquet", "scores": { "quality_ndcg@10": 0.021369, "quality_users": 4263 } } { "model": "/app/solution/norm15_validation.parquet", "scores": { "quality_ndcg@10": 0.021652, "quality_users": 4263 } } { "model": "/app/solution/norm20_validation.parquet", "scores": { "quality_ndcg@10": 0.021804, "quality_users": 4263 } } { "model": "/app/solution/norm30_validation.parquet", "scores": { "quality_ndcg@10": 0.02155, "quality_users": 4263 } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "session_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "root_turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "response_id": "resp_08539f19231b9e9f016ab5b263fa4087d2abf72375fd8ab3f3", "usage": { "input_tokens": 94293, "cached_input_tokens": 93952, "cache_write_input_tokens": 0, "output_tokens": 1077, "reasoning_output_tokens": 568, "total_tokens": 95370 }, "turn_token_usage": { "input_tokens": 8012955, "cached_input_tokens": 7910144, "cache_write_input_tokens": 0, "output_tokens": 25990, "reasoning_output_tokens": 9705, "total_tokens": 8038945 }, "thread_token_usage": { "input_tokens": 8012955, "cached_input_tokens": 7910144, "cache_write_input_tokens": 0, "output_tokens": 25990, "reasoning_output_tokens": 9705, "total_tokens": 8038945 } } ## Step 119 · agent · 2026-09-24T23:30:33.188Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T23:30:35.060Z { "code": "const r = await tools.exec_command({\"cmd\":\"ls /app/solution/norm*_validation.parquet; for f in /app/solution/norm35_validation.parquet /app/solution/norm40_validation.parquet; do [ -f \\\"$f\\\" ] && /opt/task-venv/bin/python /task/evaluate_public.py \\\"$f\\\"; done\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":5000}); text(r.output);" } ### Tool output · 2026-09-24T23:30:41.902Z Script completed Wall time 6.8 seconds Output: /app/solution/norm10_validation.parquet /app/solution/norm15_validation.parquet /app/solution/norm20_validation.parquet /app/solution/norm30_validation.parquet /app/solution/norm35_validation.parquet /app/solution/norm40_validation.parquet { "model": "/app/solution/norm35_validation.parquet", "scores": { "quality_ndcg@10": 0.021444, "quality_users": 4263 } } { "model": "/app/solution/norm40_validation.parquet", "scores": { "quality_ndcg@10": 0.02112, "quality_users": 4263 } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "session_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "root_turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "response_id": "resp_08539f19231b9e9f016ab5b296b66887d2a4f743acc3efcb38", "usage": { "input_tokens": 95581, "cached_input_tokens": 94080, "cache_write_input_tokens": 0, "output_tokens": 197, "reasoning_output_tokens": 86, "total_tokens": 95778 }, "turn_token_usage": { "input_tokens": 8108536, "cached_input_tokens": 8004224, "cache_write_input_tokens": 0, "output_tokens": 26187, "reasoning_output_tokens": 9791, "total_tokens": 8134723 }, "thread_token_usage": { "input_tokens": 8108536, "cached_input_tokens": 8004224, "cache_write_input_tokens": 0, "output_tokens": 26187, "reasoning_output_tokens": 9791, "total_tokens": 8134723 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-c65f0d46-841a-4b2a-bedf-9ec41a787109", "process_id": "6899", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\nimport numpy as np,pandas as pd,torch\nD='/task/data'; S='/app/solution'; z=np.load(f'{S}/baseline_vectors.npz'); q=torch.from_numpy(z['queries']); it=torch.from_numpy(z['items']); norm=torch.linalg.vector_norm(it,dim=1).clamp_min(1e-8); a=np.load(f'{D}/validation_eligible_candidates.npz'); ids=a['item_ids']; uids=a['uids']; packed=a['packed_eligible']\nfor power in [.10,.15,.20,.30,.35,.40]:\n rows=[]; iv=it/norm[:,None].pow(power)\n for start in range(0,len(uids),64):\n end=min(start+64,len(uids)); scores=q[start:end]@iv.T; eligible=np.unpackbits(packed[start:end],axis=1,count=len(ids),bitorder='little').astype(bool); scores.masked_fill_(~torch.from_numpy(eligible),-torch.inf); top=torch.topk(scores,100,dim=1).indices.numpy()\n for lr,cols in enumerate(top): rows.extend((int(uids[start+lr]),int(ids[c]),r) for r,c in enumerate(cols,1))\n pd.DataFrame(rows,columns=['uid','item_id','rank']).to_parquet(f'{S}/norm{int(power*100):02d}_validation.parquet',index=False)\nPY\nfor f in /app/solution/norm*_validation.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \"$f\"; done" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport numpy as np,pandas as pd,torch\nD='/task/data'; S='/app/solution'; z=np.load(f'{S}/baseline_vectors.npz'); q=torch.from_numpy(z['queries']); it=torch.from_numpy(z['items']); norm=torch.linalg.vector_norm(it,dim=1).clamp_min(1e-8); a=np.load(f'{D}/validation_eligible_candidates.npz'); ids=a['item_ids']; uids=a['uids']; packed=a['packed_eligible']\nfor power in [.10,.15,.20,.30,.35,.40]:\n rows=[]; iv=it/norm[:,None].pow(power)\n for start in range(0,len(uids),64):\n end=min(start+64,len(uids)); scores=q[start:end]@iv.T; eligible=np.unpackbits(packed[start:end],axis=1,count=len(ids),bitorder='little').astype(bool); scores.masked_fill_(~torch.from_numpy(eligible),-torch.inf); top=torch.topk(scores,100,dim=1).indices.numpy()\n for lr,cols in enumerate(top): rows.extend((int(uids[start+lr]),int(ids[c]),r) for r,c in enumerate(cols,1))\n pd.DataFrame(rows,columns=['uid','item_id','rank']).to_parquet(f'{S}/norm{int(power*100):02d}_validation.parquet',index=False)\nPY\nfor f in /app/solution/norm*_validation.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \"$f\"; done" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{\n \"model\": \"/app/solution/norm10_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.021369,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/norm15_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.021652,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/norm20_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.021804,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/norm30_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.02155,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/norm35_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.021444,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/norm40_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.02112,\n \"quality_users\": 4263\n }\n}\n", "stderr": "", "aggregated_output": "{\n \"model\": \"/app/solution/norm10_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.021369,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/norm15_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.021652,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/norm20_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.021804,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/norm30_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.02155,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/norm35_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.021444,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/norm40_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.02112,\n \"quality_users\": 4263\n }\n}\n", "exit_code": 0, "duration": { "secs": 35, "nanos": 129277981 }, "formatted_output": "{\n \"model\": \"/app/solution/norm10_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.021369,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/norm15_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.021652,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/norm20_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.021804,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/norm30_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.02155,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/norm35_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.021444,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/norm40_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.02112,\n \"quality_users\": 4263\n }\n}\n" } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-13e15110-0ff8-45ba-9e68-cdd3c34aebee", "process_id": "8628", "command": [ "/bin/sh", "-lc", "ls /app/solution/norm*_validation.parquet; for f in /app/solution/norm35_validation.parquet /app/solution/norm40_validation.parquet; do [ -f \"$f\" ] && /opt/task-venv/bin/python /task/evaluate_public.py \"$f\"; done" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "ls /app/solution/norm*_validation.parquet; for f in /app/solution/norm35_validation.parquet /app/solution/norm40_validation.parquet; do [ -f \"$f\" ] && /opt/task-venv/bin/python /task/evaluate_public.py \"$f\"; done" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{\n \"model\": \"/app/solution/norm35_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.021444,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/norm40_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.02112,\n \"quality_users\": 4263\n }\n}\n", "stderr": "", "aggregated_output": "{\n \"model\": \"/app/solution/norm35_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.021444,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/norm40_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.02112,\n \"quality_users\": 4263\n }\n}\n", "exit_code": 0, "duration": { "secs": 6, "nanos": 662356621 }, "formatted_output": "{\n \"model\": \"/app/solution/norm35_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.021444,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/norm40_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.02112,\n \"quality_users\": 4263\n }\n}\n" } } ## Step 120 · agent · 2026-09-24T23:30:47.746Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T23:30:53.740Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/train_current_model.py\\n@@\\n class Config:\\n@@\\n retrieval_loss: str = \\\"bce\\\"\\n+ item_norm_power: float = 0.20\\n@@\\n items = torch.cat(item_parts)\\n+ if config.item_norm_power:\\n+ item_norms = torch.linalg.vector_norm(items, dim=1).clamp_min(1.0e-8)\\n+ items = items / item_norms[:, None].pow(config.item_norm_power)\\n@@\\n parser.add_argument(\\n \\\"--retrieval-loss\\\",\\n@@\\n default=Config.retrieval_loss,\\n )\\n+ parser.add_argument(\\n+ \\\"--item-norm-power\\\", type=float, default=Config.item_norm_power\\n+ )\\n@@\\n or args.inbatch_correction_power < 0\\n+ or not 0.0 <= args.item_norm_power <= 1.0\\n@@\\n retrieval_loss=args.retrieval_loss,\\n+ item_norm_power=args.item_norm_power,\\n@@\\n- \\\"normalization\\\": \\\"none\\\",\\n- \\\"score\\\": \\\"raw-dot-product\\\",\\n+ \\\"normalization\\\": (\\n+ f\\\"offline-item-norm-power-{config.item_norm_power:g}\\\"\\n+ ),\\n+ \\\"score\\\": \\\"raw-dot-product-with-precomputed-scaled-items\\\",\\n*** End Patch\"; text(await tools.apply_patch(patch));" } ### Tool output · 2026-09-24T23:30:53.861Z Script completed Wall time 0.0 seconds Output: {} ### Harness context { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-9caf4206-538e-4024-9a35-15c63d383f2f", "changes": { "/app/solution/train_current_model.py": { "type": "update", "unified_diff": "@@ -60,2 +60,3 @@\n retrieval_loss: str = \"bce\"\n+ item_norm_power: float = 0.20\n inbatch_correction_power: float = 0.5\n@@ -2091,2 +2092,5 @@\n items = torch.cat(item_parts)\n+ if config.item_norm_power:\n+ item_norms = torch.linalg.vector_norm(items, dim=1).clamp_min(1.0e-8)\n+ items = items / item_norms[:, None].pow(config.item_norm_power)\n rows: list[tuple[int, int, int]] = []\n@@ -2181,2 +2185,5 @@\n parser.add_argument(\n+ \"--item-norm-power\", type=float, default=Config.item_norm_power\n+ )\n+ parser.add_argument(\n \"--target-weighting\",\n@@ -2257,2 +2264,3 @@\n or args.inbatch_correction_power < 0\n+ or not 0.0 <= args.item_norm_power <= 1.0\n ):\n@@ -2276,2 +2284,3 @@\n retrieval_loss=args.retrieval_loss,\n+ item_norm_power=args.item_norm_power,\n negative_filter_scope=args.negative_filter_scope,\n@@ -2375,4 +2384,6 @@\n \"item_mlp\": \"196-512-256-192\",\n- \"normalization\": \"none\",\n- \"score\": \"raw-dot-product\",\n+ \"normalization\": (\n+ f\"offline-item-norm-power-{config.item_norm_power:g}\"\n+ ),\n+ \"score\": \"raw-dot-product-with-precomputed-scaled-items\",\n \"objective\": (\n", "move_path": null } }, "status": "completed", "stdout": "Success. Updated the following files:\nM /app/solution/train_current_model.py\n", "stderr": "" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "session_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "root_turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "response_id": "resp_08539f19231b9e9f016ab5b2a214dc87d2805663cc9d658445", "usage": { "input_tokens": 95955, "cached_input_tokens": 95360, "cache_write_input_tokens": 0, "output_tokens": 587, "reasoning_output_tokens": 246, "total_tokens": 96542 }, "turn_token_usage": { "input_tokens": 8204491, "cached_input_tokens": 8099584, "cache_write_input_tokens": 0, "output_tokens": 26774, "reasoning_output_tokens": 10037, "total_tokens": 8231265 }, "thread_token_usage": { "input_tokens": 8204491, "cached_input_tokens": 8099584, "cache_write_input_tokens": 0, "output_tokens": 26774, "reasoning_output_tokens": 10037, "total_tokens": 8231265 } } ## Step 121 · agent · 2026-09-24T23:30:55.478Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T23:30:59.693Z { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python -m py_compile /app/solution/train_current_model.py /app/solution/two_tower_model.py\\n/opt/task-venv/bin/python - <<'PY'\\nimport numpy as np,pandas as pd\\np='/task/data/test_eligible_candidates.npz'; a=np.load(p); ids=a['item_ids']; rows=[]\\nfor i,u in enumerate(a['uids']):\\n e=np.unpackbits(a['packed_eligible'][i],count=len(ids),bitorder='little').astype(bool); cols=np.flatnonzero(e)[:100]; rows.extend((int(u),int(ids[c]),r) for r,c in enumerate(cols,1))\\npd.DataFrame(rows,columns=['uid','item_id','rank']).to_parquet('/app/solution/submission.parquet',index=False)\\nPY\\nls -l /app/solution/submission.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":5000}); text(r.output);" } ### Tool output · 2026-09-24T23:31:29.861Z Script completed Wall time 30.2 seconds Output: ### Harness context { "type": "token_usage_record", "thread_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "session_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "root_turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "response_id": "resp_08539f19231b9e9f016ab5b2ae32c887d284820e850715410c", "usage": { "input_tokens": 96566, "cached_input_tokens": 95744, "cache_write_input_tokens": 0, "output_tokens": 247, "reasoning_output_tokens": 8, "total_tokens": 96813 }, "turn_token_usage": { "input_tokens": 8301057, "cached_input_tokens": 8195328, "cache_write_input_tokens": 0, "output_tokens": 27021, "reasoning_output_tokens": 10045, "total_tokens": 8328078 }, "thread_token_usage": { "input_tokens": 8301057, "cached_input_tokens": 8195328, "cache_write_input_tokens": 0, "output_tokens": 27021, "reasoning_output_tokens": 10045, "total_tokens": 8328078 } } ## Step 122 · agent · 2026-09-24T23:31:31.784Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T23:31:33.173Z { "code": "const r = await tools.exec_command({\"cmd\":\"ls -l /app/solution/submission.parquet 2>/dev/null || true; /opt/task-venv/bin/python -m py_compile /app/solution/train_current_model.py\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":2000}); text(r.output);" } ### Tool output · 2026-09-24T23:31:33.360Z Script completed Wall time 0.2 seconds Output: ### Harness context { "type": "token_usage_record", "thread_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "session_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "root_turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "response_id": "resp_08539f19231b9e9f016ab5b2d2127487d2952fd062beefdc33", "usage": { "input_tokens": 96836, "cached_input_tokens": 96384, "cache_write_input_tokens": 0, "output_tokens": 123, "reasoning_output_tokens": 37, "total_tokens": 96959 }, "turn_token_usage": { "input_tokens": 8397893, "cached_input_tokens": 8291712, "cache_write_input_tokens": 0, "output_tokens": 27144, "reasoning_output_tokens": 10082, "total_tokens": 8425037 }, "thread_token_usage": { "input_tokens": 8397893, "cached_input_tokens": 8291712, "cache_write_input_tokens": 0, "output_tokens": 27144, "reasoning_output_tokens": 10082, "total_tokens": 8425037 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-88df60fe-f76d-489c-8b8f-49796a2537dd", "process_id": "98612", "command": [ "/bin/sh", "-lc", "ls -l /app/solution/submission.parquet 2>/dev/null || true; /opt/task-venv/bin/python -m py_compile /app/solution/train_current_model.py" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "ls -l /app/solution/submission.parquet 2>/dev/null || true; /opt/task-venv/bin/python -m py_compile /app/solution/train_current_model.py" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 0, "nanos": 3629 }, "formatted_output": "" } } ## Step 123 · agent · 2026-09-24T23:31:38.519Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T23:31:42.232Z { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python - <<'PY'\\nimport numpy as np,pandas as pd\\na=np.load('/task/data/test_eligible_candidates.npz'); ids=a['item_ids']; n=len(a['uids']); out_u=np.repeat(a['uids'].astype(np.int64),100); out_i=np.empty(n*100,np.int64); out_r=np.tile(np.arange(1,101,dtype=np.int64),n)\\nfor j in range(n):\\n m=np.unpackbits(a['packed_eligible'][j],count=len(ids),bitorder='little'); out_i[j*100:(j+1)*100]=ids[np.flatnonzero(m)[:100]]\\npd.DataFrame({'uid':out_u,'item_id':out_i,'rank':out_r}).to_parquet('/app/solution/submission.parquet',index=False)\\nPY\\nls -lh /app/solution/submission.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":2000}); text(r.output);" } ### Tool output · 2026-09-24T23:32:12.402Z Script completed Wall time 30.2 seconds Output: ### Harness context { "type": "token_usage_record", "thread_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "session_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "root_turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "response_id": "resp_08539f19231b9e9f016ab5b2d5d5c887d2b5664d233b6025a2", "usage": { "input_tokens": 96982, "cached_input_tokens": 96640, "cache_write_input_tokens": 0, "output_tokens": 353, "reasoning_output_tokens": 111, "total_tokens": 97335 }, "turn_token_usage": { "input_tokens": 8494875, "cached_input_tokens": 8388352, "cache_write_input_tokens": 0, "output_tokens": 27497, "reasoning_output_tokens": 10193, "total_tokens": 8522372 }, "thread_token_usage": { "input_tokens": 8494875, "cached_input_tokens": 8388352, "cache_write_input_tokens": 0, "output_tokens": 27497, "reasoning_output_tokens": 10193, "total_tokens": 8522372 } } ## Step 124 · agent · 2026-09-24T23:32:15.986Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T23:32:20.257Z { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python - <<'PY'\\nimport numpy as np,pandas as pd\\na=np.load('/task/data/test_eligible_candidates.npz'); ids=a['item_ids']; n=len(a['uids']); out_u=np.repeat(a['uids'].astype(np.int64),100); out_i=np.empty(n*100,np.int64); out_r=np.tile(np.arange(1,101,dtype=np.int64),n)\\nfor j in range(n):\\n m=np.unpackbits(a['packed_eligible'][j],count=len(ids),bitorder='little'); out_i[j*100:(j+1)*100]=ids[np.flatnonzero(m)[:100]]\\npd.DataFrame({'uid':out_u,'item_id':out_i,'rank':out_r}).to_parquet('/app/solution/submission.parquet',index=False)\\nPY\\nls -lh /app/solution/submission.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":2000}); text(JSON.stringify(r));" } ### Tool output · 2026-09-24T23:32:21.430Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"bfe401","wall_time_seconds":1.00068096,"session_id":18745,"original_token_count":0,"output":""} ### Harness context { "type": "token_usage_record", "thread_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "session_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "root_turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "response_id": "resp_08539f19231b9e9f016ab5b2fc9df087d280f8ff9a7f0c573d", "usage": { "input_tokens": 97358, "cached_input_tokens": 96768, "cache_write_input_tokens": 0, "output_tokens": 289, "reasoning_output_tokens": 46, "total_tokens": 97647 }, "turn_token_usage": { "input_tokens": 8592233, "cached_input_tokens": 8485120, "cache_write_input_tokens": 0, "output_tokens": 27786, "reasoning_output_tokens": 10239, "total_tokens": 8620019 }, "thread_token_usage": { "input_tokens": 8592233, "cached_input_tokens": 8485120, "cache_write_input_tokens": 0, "output_tokens": 27786, "reasoning_output_tokens": 10239, "total_tokens": 8620019 } } ## Step 125 · agent · 2026-09-24T23:32:22.925Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T23:32:23.653Z { "code": "const r = await tools.write_stdin({\"session_id\":18745,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":2000}); text(r.output);\n" } ### Tool output · 2026-09-24T23:32:53.662Z Script completed Wall time 30.0 seconds Output: ### Harness context { "type": "token_usage_record", "thread_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "session_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "root_turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "response_id": "resp_08539f19231b9e9f016ab5b305ac4c87d2904d7615effbb172", "usage": { "input_tokens": 97704, "cached_input_tokens": 97152, "cache_write_input_tokens": 0, "output_tokens": 55, "reasoning_output_tokens": 8, "total_tokens": 97759 }, "turn_token_usage": { "input_tokens": 8689937, "cached_input_tokens": 8582272, "cache_write_input_tokens": 0, "output_tokens": 27841, "reasoning_output_tokens": 10247, "total_tokens": 8717778 }, "thread_token_usage": { "input_tokens": 8689937, "cached_input_tokens": 8582272, "cache_write_input_tokens": 0, "output_tokens": 27841, "reasoning_output_tokens": 10247, "total_tokens": 8717778 } } ## Step 126 · agent · 2026-09-24T23:32:55.468Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T23:32:56.176Z { "code": "const r = await tools.write_stdin({\"session_id\":18745,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":2000}); text(r.output);\n" } ### Tool output · 2026-09-24T23:33:26.184Z Script completed Wall time 30.0 seconds Output: ### Harness context { "type": "token_usage_record", "thread_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "session_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "root_turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "response_id": "resp_08539f19231b9e9f016ab5b325f2e487d2b7a0f03d6a2e7317", "usage": { "input_tokens": 97782, "cached_input_tokens": 97536, "cache_write_input_tokens": 0, "output_tokens": 80, "reasoning_output_tokens": 33, "total_tokens": 97862 }, "turn_token_usage": { "input_tokens": 8787719, "cached_input_tokens": 8679808, "cache_write_input_tokens": 0, "output_tokens": 27921, "reasoning_output_tokens": 10280, "total_tokens": 8815640 }, "thread_token_usage": { "input_tokens": 8787719, "cached_input_tokens": 8679808, "cache_write_input_tokens": 0, "output_tokens": 27921, "reasoning_output_tokens": 10280, "total_tokens": 8815640 } } ## Step 127 · agent · 2026-09-24T23:33:28.317Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T23:33:28.973Z { "code": "const r = await tools.write_stdin({\"session_id\":18745,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":2000}); text(r.output);\n" } ### Tool output · 2026-09-24T23:33:58.983Z Script completed Wall time 30.0 seconds Output: ### Harness context { "type": "token_usage_record", "thread_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "session_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "root_turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "response_id": "resp_08539f19231b9e9f016ab5b34666f087d2adb58cca999e46da", "usage": { "input_tokens": 97885, "cached_input_tokens": 97664, "cache_write_input_tokens": 0, "output_tokens": 99, "reasoning_output_tokens": 52, "total_tokens": 97984 }, "turn_token_usage": { "input_tokens": 8885604, "cached_input_tokens": 8777472, "cache_write_input_tokens": 0, "output_tokens": 28020, "reasoning_output_tokens": 10332, "total_tokens": 8913624 }, "thread_token_usage": { "input_tokens": 8885604, "cached_input_tokens": 8777472, "cache_write_input_tokens": 0, "output_tokens": 28020, "reasoning_output_tokens": 10332, "total_tokens": 8913624 } } ## Step 128 · agent · 2026-09-24T23:34:01.375Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T23:34:03.150Z { "code": "const r = await tools.exec_command({\"cmd\":\"for d in /proc/[0-9]*; do [ \\\"$(cat \\\"$d/comm\\\" 2>/dev/null)\\\" = python ] && { echo -n \\\"$d \\\"; tr '\\\\0' ' ' < \\\"$d/cmdline\\\"; echo; }; done\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":5000}); text(r.output);" } ### Tool output · 2026-09-24T23:34:03.295Z Script completed Wall time 0.1 seconds Output: /proc/10744 /opt/task-venv/bin/python - /proc/10763 /opt/task-venv/bin/python - /proc/10778 /opt/task-venv/bin/python - ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-43694c48-21f9-4c93-8572-79a8d9a0c8c5", "process_id": "99309", "command": [ "/bin/sh", "-lc", "for d in /proc/[0-9]*; do [ \"$(cat \"$d/comm\" 2>/dev/null)\" = python ] && { echo -n \"$d \"; tr '\\0' ' ' < \"$d/cmdline\"; echo; }; done" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "for d in /proc/[0-9]*; do [ \"$(cat \"$d/comm\" 2>/dev/null)\" = python ] && { echo -n \"$d \"; tr '\\0' ' ' < \"$d/cmdline\"; echo; }; done" } ], "source": "unified_exec_startup", "status": "failed", "stdout": "/proc/10744 /opt/task-venv/bin/python - \n/proc/10763 /opt/task-venv/bin/python - \n/proc/10778 /opt/task-venv/bin/python - \n", "stderr": "", "aggregated_output": "/proc/10744 /opt/task-venv/bin/python - \n/proc/10763 /opt/task-venv/bin/python - \n/proc/10778 /opt/task-venv/bin/python - \n", "exit_code": 1, "duration": { "secs": 0, "nanos": 4820 }, "formatted_output": "/proc/10744 /opt/task-venv/bin/python - \n/proc/10763 /opt/task-venv/bin/python - \n/proc/10778 /opt/task-venv/bin/python - \n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "session_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "root_turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "response_id": "resp_08539f19231b9e9f016ab5b3674c6087d29fc98443e18b3fb3", "usage": { "input_tokens": 98007, "cached_input_tokens": 97664, "cache_write_input_tokens": 0, "output_tokens": 165, "reasoning_output_tokens": 60, "total_tokens": 98172 }, "turn_token_usage": { "input_tokens": 8983611, "cached_input_tokens": 8875136, "cache_write_input_tokens": 0, "output_tokens": 28185, "reasoning_output_tokens": 10392, "total_tokens": 9011796 }, "thread_token_usage": { "input_tokens": 8983611, "cached_input_tokens": 8875136, "cache_write_input_tokens": 0, "output_tokens": 28185, "reasoning_output_tokens": 10392, "total_tokens": 9011796 } } ## Step 129 · agent · 2026-09-24T23:34:05.804Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T23:34:07.692Z { "code": "const r = await tools.exec_command({\"cmd\":\"kill 10744 10763\\nfor d in /proc/[0-9]*; do [ \\\"$(cat \\\"$d/comm\\\" 2>/dev/null)\\\" = python ] && { echo -n \\\"$d \\\"; tr '\\\\0' ' ' < \\\"$d/cmdline\\\"; echo; }; done\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":2000}); text(r.output);" } ### Tool output · 2026-09-24T23:34:07.834Z Script completed Wall time 0.1 seconds Output: /proc/10744 /proc/10763 /proc/10778 /opt/task-venv/bin/python - ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-1441763c-f189-4793-bc53-8db8c4387d04", "process_id": "86083", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\nimport numpy as np,pandas as pd\na=np.load('/task/data/test_eligible_candidates.npz'); ids=a['item_ids']; n=len(a['uids']); out_u=np.repeat(a['uids'].astype(np.int64),100); out_i=np.empty(n*100,np.int64); out_r=np.tile(np.arange(1,101,dtype=np.int64),n)\nfor j in range(n):\n m=np.unpackbits(a['packed_eligible'][j],count=len(ids),bitorder='little'); out_i[j*100:(j+1)*100]=ids[np.flatnonzero(m)[:100]]\npd.DataFrame({'uid':out_u,'item_id':out_i,'rank':out_r}).to_parquet('/app/solution/submission.parquet',index=False)\nPY\nls -lh /app/solution/submission.parquet" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport numpy as np,pandas as pd\na=np.load('/task/data/test_eligible_candidates.npz'); ids=a['item_ids']; n=len(a['uids']); out_u=np.repeat(a['uids'].astype(np.int64),100); out_i=np.empty(n*100,np.int64); out_r=np.tile(np.arange(1,101,dtype=np.int64),n)\nfor j in range(n):\n m=np.unpackbits(a['packed_eligible'][j],count=len(ids),bitorder='little'); out_i[j*100:(j+1)*100]=ids[np.flatnonzero(m)[:100]]\npd.DataFrame({'uid':out_u,'item_id':out_i,'rank':out_r}).to_parquet('/app/solution/submission.parquet',index=False)\nPY\nls -lh /app/solution/submission.parquet" } ], "source": "unified_exec_startup", "status": "failed", "stdout": "Terminated\nls: cannot access '/app/solution/submission.parquet': No such file or directory\n", "stderr": "", "aggregated_output": "Terminated\nls: cannot access '/app/solution/submission.parquet': No such file or directory\n", "exit_code": 2, "duration": { "secs": 145, "nanos": 322171536 }, "formatted_output": "Terminated\nls: cannot access '/app/solution/submission.parquet': No such file or directory\n" } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-18ea64ec-796e-4f9c-96cb-6b112bb5b911", "process_id": "62478", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python -m py_compile /app/solution/train_current_model.py /app/solution/two_tower_model.py\n/opt/task-venv/bin/python - <<'PY'\nimport numpy as np,pandas as pd\np='/task/data/test_eligible_candidates.npz'; a=np.load(p); ids=a['item_ids']; rows=[]\nfor i,u in enumerate(a['uids']):\n e=np.unpackbits(a['packed_eligible'][i],count=len(ids),bitorder='little').astype(bool); cols=np.flatnonzero(e)[:100]; rows.extend((int(u),int(ids[c]),r) for r,c in enumerate(cols,1))\npd.DataFrame(rows,columns=['uid','item_id','rank']).to_parquet('/app/solution/submission.parquet',index=False)\nPY\nls -l /app/solution/submission.parquet" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python -m py_compile /app/solution/train_current_model.py /app/solution/two_tower_model.py\n/opt/task-venv/bin/python - <<'PY'\nimport numpy as np,pandas as pd\np='/task/data/test_eligible_candidates.npz'; a=np.load(p); ids=a['item_ids']; rows=[]\nfor i,u in enumerate(a['uids']):\n e=np.unpackbits(a['packed_eligible'][i],count=len(ids),bitorder='little').astype(bool); cols=np.flatnonzero(e)[:100]; rows.extend((int(u),int(ids[c]),r) for r,c in enumerate(cols,1))\npd.DataFrame(rows,columns=['uid','item_id','rank']).to_parquet('/app/solution/submission.parquet',index=False)\nPY\nls -l /app/solution/submission.parquet" } ], "source": "unified_exec_startup", "status": "failed", "stdout": "Terminated\nls: cannot access '/app/solution/submission.parquet': No such file or directory\n", "stderr": "", "aggregated_output": "Terminated\nls: cannot access '/app/solution/submission.parquet': No such file or directory\n", "exit_code": 2, "duration": { "secs": 187, "nanos": 867410173 }, "formatted_output": "Terminated\nls: cannot access '/app/solution/submission.parquet': No such file or directory\n" } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-6a368ca3-807e-4af3-9341-07305ab7d7a1", "process_id": "21112", "command": [ "/bin/sh", "-lc", "kill 10744 10763\nfor d in /proc/[0-9]*; do [ \"$(cat \"$d/comm\" 2>/dev/null)\" = python ] && { echo -n \"$d \"; tr '\\0' ' ' < \"$d/cmdline\"; echo; }; done" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "kill 10744 10763\nfor d in /proc/[0-9]*; do [ \"$(cat \"$d/comm\" 2>/dev/null)\" = python ] && { echo -n \"$d \"; tr '\\0' ' ' < \"$d/cmdline\"; echo; }; done" } ], "source": "unified_exec_startup", "status": "failed", "stdout": "/proc/10744 \n/proc/10763 \n/proc/10778 /opt/task-venv/bin/python - \n", "stderr": "", "aggregated_output": "/proc/10744 \n/proc/10763 \n/proc/10778 /opt/task-venv/bin/python - \n", "exit_code": 1, "duration": { "secs": 0, "nanos": 3980 }, "formatted_output": "/proc/10744 \n/proc/10763 \n/proc/10778 /opt/task-venv/bin/python - \n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "session_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "root_turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "response_id": "resp_08539f19231b9e9f016ab5b36bdc9087d2bb9df916a421bd85", "usage": { "input_tokens": 98239, "cached_input_tokens": 97792, "cache_write_input_tokens": 0, "output_tokens": 165, "reasoning_output_tokens": 52, "total_tokens": 98404 }, "turn_token_usage": { "input_tokens": 9081850, "cached_input_tokens": 8972928, "cache_write_input_tokens": 0, "output_tokens": 28350, "reasoning_output_tokens": 10444, "total_tokens": 9110200 }, "thread_token_usage": { "input_tokens": 9081850, "cached_input_tokens": 8972928, "cache_write_input_tokens": 0, "output_tokens": 28350, "reasoning_output_tokens": 10444, "total_tokens": 9110200 } } ## Step 130 · agent · 2026-09-24T23:34:09.407Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T23:34:10.147Z { "code": "const r = await tools.write_stdin({\"session_id\":18745,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":2000}); text(r.output);\n" } ### Tool output · 2026-09-24T23:34:40.156Z Script completed Wall time 30.0 seconds Output: ### Harness context { "type": "token_usage_record", "thread_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "session_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "root_turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "response_id": "resp_08539f19231b9e9f016ab5b370191487d29d0b896aee5653c3", "usage": { "input_tokens": 98453, "cached_input_tokens": 98048, "cache_write_input_tokens": 0, "output_tokens": 57, "reasoning_output_tokens": 10, "total_tokens": 98510 }, "turn_token_usage": { "input_tokens": 9180303, "cached_input_tokens": 9070976, "cache_write_input_tokens": 0, "output_tokens": 28407, "reasoning_output_tokens": 10454, "total_tokens": 9208710 }, "thread_token_usage": { "input_tokens": 9180303, "cached_input_tokens": 9070976, "cache_write_input_tokens": 0, "output_tokens": 28407, "reasoning_output_tokens": 10454, "total_tokens": 9208710 } } ## Step 131 · agent · 2026-09-24T23:34:43.779Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T23:34:44.574Z { "code": "const r = await tools.write_stdin({\"session_id\":18745,\"chars\":\"\\u0003\",\"yield_time_ms\":1000,\"max_output_tokens\":2000}); text(r.output);\n" } ### Tool output · 2026-09-24T23:34:44.739Z Script completed Wall time 0.1 seconds Output: Traceback (most recent call last): File "", line 4, in File "/opt/task-venv/lib/python3.12/site-packages/numpy/lib/_npyio_impl.py", line 254, in __getitem__ return format.read_array(bytes, ^^^^^^^^^^^^^^^^^^^^^^^^ File "/opt/task-venv/lib/python3.12/site-packages/numpy/lib/format.py", line 851, in read_array data = _read_bytes(fp, read_size, "array data") ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/opt/task-venv/lib/python3.12/site-packages/numpy/lib/format.py", line 986, in _read_bytes r = fp.read(size - len(data)) ^^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/local/lib/python3.12/zipfile/__init__.py", line 1008, in read data = self._read1(n) ^^^^^^^^^^^^^^ File "/usr/local/lib/python3.12/zipfile/__init__.py", line 1084, in _read1 data = self._decompressor.decompress(data, n) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ KeyboardInterrupt ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-3c8643e6-7353-48d2-8adc-8748513e5bc9", "process_id": "18745", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\nimport numpy as np,pandas as pd\na=np.load('/task/data/test_eligible_candidates.npz'); ids=a['item_ids']; n=len(a['uids']); out_u=np.repeat(a['uids'].astype(np.int64),100); out_i=np.empty(n*100,np.int64); out_r=np.tile(np.arange(1,101,dtype=np.int64),n)\nfor j in range(n):\n m=np.unpackbits(a['packed_eligible'][j],count=len(ids),bitorder='little'); out_i[j*100:(j+1)*100]=ids[np.flatnonzero(m)[:100]]\npd.DataFrame({'uid':out_u,'item_id':out_i,'rank':out_r}).to_parquet('/app/solution/submission.parquet',index=False)\nPY\nls -lh /app/solution/submission.parquet" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport numpy as np,pandas as pd\na=np.load('/task/data/test_eligible_candidates.npz'); ids=a['item_ids']; n=len(a['uids']); out_u=np.repeat(a['uids'].astype(np.int64),100); out_i=np.empty(n*100,np.int64); out_r=np.tile(np.arange(1,101,dtype=np.int64),n)\nfor j in range(n):\n m=np.unpackbits(a['packed_eligible'][j],count=len(ids),bitorder='little'); out_i[j*100:(j+1)*100]=ids[np.flatnonzero(m)[:100]]\npd.DataFrame({'uid':out_u,'item_id':out_i,'rank':out_r}).to_parquet('/app/solution/submission.parquet',index=False)\nPY\nls -lh /app/solution/submission.parquet" } ], "source": "unified_exec_startup", "status": "failed", "stdout": "Traceback (most recent call last):\n File \"\", line 4, in \n File \"/opt/task-venv/lib/python3.12/site-packages/numpy/lib/_npyio_impl.py\", line 254, in __getitem__\n return format.read_array(bytes,\n ^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/numpy/lib/format.py\", line 851, in read_array\n data = _read_bytes(fp, read_size, \"array data\")\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/numpy/lib/format.py\", line 986, in _read_bytes\n r = fp.read(size - len(data))\n ^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/usr/local/lib/python3.12/zipfile/__init__.py\", line 1008, in read\n data = self._read1(n)\n ^^^^^^^^^^^^^^\n File \"/usr/local/lib/python3.12/zipfile/__init__.py\", line 1084, in _read1\n data = self._decompressor.decompress(data, n)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nKeyboardInterrupt\n", "stderr": "", "aggregated_output": "Traceback (most recent call last):\n File \"\", line 4, in \n File \"/opt/task-venv/lib/python3.12/site-packages/numpy/lib/_npyio_impl.py\", line 254, in __getitem__\n return format.read_array(bytes,\n ^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/numpy/lib/format.py\", line 851, in read_array\n data = _read_bytes(fp, read_size, \"array data\")\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/numpy/lib/format.py\", line 986, in _read_bytes\n r = fp.read(size - len(data))\n ^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/usr/local/lib/python3.12/zipfile/__init__.py\", line 1008, in read\n data = self._read1(n)\n ^^^^^^^^^^^^^^\n File \"/usr/local/lib/python3.12/zipfile/__init__.py\", line 1084, in _read1\n data = self._decompressor.decompress(data, n)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nKeyboardInterrupt\n", "exit_code": 130, "duration": { "secs": 144, "nanos": 206647611 }, "formatted_output": "Traceback (most recent call last):\n File \"\", line 4, in \n File \"/opt/task-venv/lib/python3.12/site-packages/numpy/lib/_npyio_impl.py\", line 254, in __getitem__\n return format.read_array(bytes,\n ^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/numpy/lib/format.py\", line 851, in read_array\n data = _read_bytes(fp, read_size, \"array data\")\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/numpy/lib/format.py\", line 986, in _read_bytes\n r = fp.read(size - len(data))\n ^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/usr/local/lib/python3.12/zipfile/__init__.py\", line 1008, in read\n data = self._read1(n)\n ^^^^^^^^^^^^^^\n File \"/usr/local/lib/python3.12/zipfile/__init__.py\", line 1084, in _read1\n data = self._decompressor.decompress(data, n)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nKeyboardInterrupt\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "session_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "root_turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "response_id": "resp_08539f19231b9e9f016ab5b390612487d28d07eb6390b40772", "usage": { "input_tokens": 98533, "cached_input_tokens": 98304, "cache_write_input_tokens": 0, "output_tokens": 157, "reasoning_output_tokens": 105, "total_tokens": 98690 }, "turn_token_usage": { "input_tokens": 9278836, "cached_input_tokens": 9169280, "cache_write_input_tokens": 0, "output_tokens": 28564, "reasoning_output_tokens": 10559, "total_tokens": 9307400 }, "thread_token_usage": { "input_tokens": 9278836, "cached_input_tokens": 9169280, "cache_write_input_tokens": 0, "output_tokens": 28564, "reasoning_output_tokens": 10559, "total_tokens": 9307400 } } ## Step 132 · agent · 2026-09-24T23:34:50.870Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T23:34:55.399Z { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python - <<'PY'\\nimport numpy as np,pandas as pd\\nwith np.load('/task/data/test_eligible_candidates.npz') as a:\\n ids=a['item_ids'].copy(); uids=a['uids'].copy(); packed=a['packed_eligible'].copy()\\nn=len(uids); out_u=np.repeat(uids.astype(np.int64),100); out_i=np.empty(n*100,np.int64); out_r=np.tile(np.arange(1,101,dtype=np.int64),n)\\nfor j in range(n):\\n m=np.unpackbits(packed[j,:32],count=256,bitorder='little'); out_i[j*100:(j+1)*100]=ids[np.flatnonzero(m)[:100]]\\npd.DataFrame({'uid':out_u,'item_id':out_i,'rank':out_r}).to_parquet('/app/solution/submission.parquet',index=False)\\nPY\\nls -lh /app/solution/submission.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":2000}); text(r.output);" } ### Tool output · 2026-09-24T23:34:57.043Z Script completed Wall time 1.6 seconds Output: Traceback (most recent call last): File "", line 6, in ValueError: could not broadcast input array from shape (87,) into shape (100,) ls: cannot access '/app/solution/submission.parquet': No such file or directory ### Harness context { "type": "token_usage_record", "thread_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "session_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "root_turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "response_id": "resp_08539f19231b9e9f016ab5b3951f9887d29cd0d1ab6acd67e6", "usage": { "input_tokens": 98982, "cached_input_tokens": 98304, "cache_write_input_tokens": 0, "output_tokens": 297, "reasoning_output_tokens": 37, "total_tokens": 99279 }, "turn_token_usage": { "input_tokens": 9377818, "cached_input_tokens": 9267584, "cache_write_input_tokens": 0, "output_tokens": 28861, "reasoning_output_tokens": 10596, "total_tokens": 9406679 }, "thread_token_usage": { "input_tokens": 9377818, "cached_input_tokens": 9267584, "cache_write_input_tokens": 0, "output_tokens": 28861, "reasoning_output_tokens": 10596, "total_tokens": 9406679 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-ba966210-a6c2-4e18-adee-b018ce4eb6df", "process_id": "14998", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\nimport numpy as np,pandas as pd\nwith np.load('/task/data/test_eligible_candidates.npz') as a:\n ids=a['item_ids'].copy(); uids=a['uids'].copy(); packed=a['packed_eligible'].copy()\nn=len(uids); out_u=np.repeat(uids.astype(np.int64),100); out_i=np.empty(n*100,np.int64); out_r=np.tile(np.arange(1,101,dtype=np.int64),n)\nfor j in range(n):\n m=np.unpackbits(packed[j,:32],count=256,bitorder='little'); out_i[j*100:(j+1)*100]=ids[np.flatnonzero(m)[:100]]\npd.DataFrame({'uid':out_u,'item_id':out_i,'rank':out_r}).to_parquet('/app/solution/submission.parquet',index=False)\nPY\nls -lh /app/solution/submission.parquet" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport numpy as np,pandas as pd\nwith np.load('/task/data/test_eligible_candidates.npz') as a:\n ids=a['item_ids'].copy(); uids=a['uids'].copy(); packed=a['packed_eligible'].copy()\nn=len(uids); out_u=np.repeat(uids.astype(np.int64),100); out_i=np.empty(n*100,np.int64); out_r=np.tile(np.arange(1,101,dtype=np.int64),n)\nfor j in range(n):\n m=np.unpackbits(packed[j,:32],count=256,bitorder='little'); out_i[j*100:(j+1)*100]=ids[np.flatnonzero(m)[:100]]\npd.DataFrame({'uid':out_u,'item_id':out_i,'rank':out_r}).to_parquet('/app/solution/submission.parquet',index=False)\nPY\nls -lh /app/solution/submission.parquet" } ], "source": "unified_exec_startup", "status": "failed", "stdout": "Traceback (most recent call last):\n File \"\", line 6, in \nValueError: could not broadcast input array from shape (87,) into shape (100,)\nls: cannot access '/app/solution/submission.parquet': No such file or directory\n", "stderr": "", "aggregated_output": "Traceback (most recent call last):\n File \"\", line 6, in \nValueError: could not broadcast input array from shape (87,) into shape (100,)\nls: cannot access '/app/solution/submission.parquet': No such file or directory\n", "exit_code": 2, "duration": { "secs": 1, "nanos": 475624309 }, "formatted_output": "Traceback (most recent call last):\n File \"\", line 6, in \nValueError: could not broadcast input array from shape (87,) into shape (100,)\nls: cannot access '/app/solution/submission.parquet': No such file or directory\n" } } ## Step 133 · agent · 2026-09-24T23:34:58.914Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T23:35:03.432Z { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python - <<'PY'\\nimport numpy as np,pandas as pd\\nwith np.load('/task/data/test_eligible_candidates.npz') as a:\\n ids=a['item_ids'].copy(); uids=a['uids'].copy(); packed=a['packed_eligible'].copy()\\nn=len(uids); out_u=np.repeat(uids.astype(np.int64),100); out_i=np.empty(n*100,np.int64); out_r=np.tile(np.arange(1,101,dtype=np.int64),n)\\nfor j in range(n):\\n m=np.unpackbits(packed[j],count=len(ids),bitorder='little'); out_i[j*100:(j+1)*100]=ids[np.flatnonzero(m)[:100]]\\npd.DataFrame({'uid':out_u,'item_id':out_i,'rank':out_r}).to_parquet('/app/solution/submission.parquet',index=False)\\nPY\\nls -lh /app/solution/submission.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":2000}); text(r.output);" } ### Tool output · 2026-09-24T23:35:05.919Z Script completed Wall time 2.5 seconds Output: -rw-r--r-- 1 agent agent 719K Sep 24 23:35 /app/solution/submission.parquet ### Harness context { "type": "token_usage_record", "thread_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "session_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "root_turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "response_id": "resp_08539f19231b9e9f016ab5b3a13eb087d2a6d5f251c37d8f2d", "usage": { "input_tokens": 99360, "cached_input_tokens": 98816, "cache_write_input_tokens": 0, "output_tokens": 294, "reasoning_output_tokens": 36, "total_tokens": 99654 }, "turn_token_usage": { "input_tokens": 9477178, "cached_input_tokens": 9366400, "cache_write_input_tokens": 0, "output_tokens": 29155, "reasoning_output_tokens": 10632, "total_tokens": 9506333 }, "thread_token_usage": { "input_tokens": 9477178, "cached_input_tokens": 9366400, "cache_write_input_tokens": 0, "output_tokens": 29155, "reasoning_output_tokens": 10632, "total_tokens": 9506333 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-a853168f-3225-435d-8e45-0cfd466531c5", "process_id": "70927", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\nimport numpy as np,pandas as pd\nwith np.load('/task/data/test_eligible_candidates.npz') as a:\n ids=a['item_ids'].copy(); uids=a['uids'].copy(); packed=a['packed_eligible'].copy()\nn=len(uids); out_u=np.repeat(uids.astype(np.int64),100); out_i=np.empty(n*100,np.int64); out_r=np.tile(np.arange(1,101,dtype=np.int64),n)\nfor j in range(n):\n m=np.unpackbits(packed[j],count=len(ids),bitorder='little'); out_i[j*100:(j+1)*100]=ids[np.flatnonzero(m)[:100]]\npd.DataFrame({'uid':out_u,'item_id':out_i,'rank':out_r}).to_parquet('/app/solution/submission.parquet',index=False)\nPY\nls -lh /app/solution/submission.parquet" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport numpy as np,pandas as pd\nwith np.load('/task/data/test_eligible_candidates.npz') as a:\n ids=a['item_ids'].copy(); uids=a['uids'].copy(); packed=a['packed_eligible'].copy()\nn=len(uids); out_u=np.repeat(uids.astype(np.int64),100); out_i=np.empty(n*100,np.int64); out_r=np.tile(np.arange(1,101,dtype=np.int64),n)\nfor j in range(n):\n m=np.unpackbits(packed[j],count=len(ids),bitorder='little'); out_i[j*100:(j+1)*100]=ids[np.flatnonzero(m)[:100]]\npd.DataFrame({'uid':out_u,'item_id':out_i,'rank':out_r}).to_parquet('/app/solution/submission.parquet',index=False)\nPY\nls -lh /app/solution/submission.parquet" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "-rw-r--r-- 1 agent agent 719K Sep 24 23:35 /app/solution/submission.parquet\n", "stderr": "", "aggregated_output": "-rw-r--r-- 1 agent agent 719K Sep 24 23:35 /app/solution/submission.parquet\n", "exit_code": 0, "duration": { "secs": 2, "nanos": 313006553 }, "formatted_output": "-rw-r--r-- 1 agent agent 719K Sep 24 23:35 /app/solution/submission.parquet\n" } } ## Step 134 · agent · 2026-09-24T23:35:08.287Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T23:35:11.142Z { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python /app/solution/train_current_model.py --stage submission --public-dir /task --prepared-cache /app/solution/submission_sequences.npz --negative-sampling mixed --uniform-negatives 24 --inbatch-negatives 8 --inbatch-correction-power 0.5 --retrieval-loss bce --item-norm-power 0.20 --epochs 1 --checkpoint-dir /app/solution/final_checkpoints --output /app/solution/submission.parquet 2>&1 | tee /app/solution/final_training.log\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":12000}); text(JSON.stringify(r));" } ### Tool output · 2026-09-24T23:35:12.320Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"a335d4","wall_time_seconds":1.00077679,"session_id":45894,"original_token_count":0,"output":""} ### Harness context { "type": "token_usage_record", "thread_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "session_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "root_turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "response_id": "resp_08539f19231b9e9f016ab5b3aa215487d2aa6cf5eb8fb71623", "usage": { "input_tokens": 99706, "cached_input_tokens": 99200, "cache_write_input_tokens": 0, "output_tokens": 233, "reasoning_output_tokens": 67, "total_tokens": 99939 }, "turn_token_usage": { "input_tokens": 9576884, "cached_input_tokens": 9465600, "cache_write_input_tokens": 0, "output_tokens": 29388, "reasoning_output_tokens": 10699, "total_tokens": 9606272 }, "thread_token_usage": { "input_tokens": 9576884, "cached_input_tokens": 9465600, "cache_write_input_tokens": 0, "output_tokens": 29388, "reasoning_output_tokens": 10699, "total_tokens": 9606272 } } ## Step 135 · agent · 2026-09-24T23:35:13.705Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T23:35:14.398Z { "code": "const r = await tools.write_stdin({\"session_id\":45894,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":12000}); text(r.output);\n" } ### Tool output · 2026-09-24T23:35:44.406Z Script completed Wall time 30.0 seconds Output: loaded explicit events: rows=746,504 users=8,307 prepared users=1,000 tokens=2,167,338 states=37,073 elapsed=5.1s prepared users=2,000 tokens=4,450,246 states=73,784 elapsed=10.4s prepared users=3,000 tokens=6,703,970 states=113,190 elapsed=15.7s prepared users=4,000 tokens=9,087,187 states=153,283 elapsed=21.4s ### Harness context { "type": "token_usage_record", "thread_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "session_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "root_turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "response_id": "resp_08539f19231b9e9f016ab5b3b09e0487d286a60907d5e9f7be", "usage": { "input_tokens": 99997, "cached_input_tokens": 99584, "cache_write_input_tokens": 0, "output_tokens": 56, "reasoning_output_tokens": 9, "total_tokens": 100053 }, "turn_token_usage": { "input_tokens": 9676881, "cached_input_tokens": 9565184, "cache_write_input_tokens": 0, "output_tokens": 29444, "reasoning_output_tokens": 10708, "total_tokens": 9706325 }, "thread_token_usage": { "input_tokens": 9676881, "cached_input_tokens": 9565184, "cache_write_input_tokens": 0, "output_tokens": 29444, "reasoning_output_tokens": 10708, "total_tokens": 9706325 } } ## Step 136 · agent · 2026-09-24T23:35:45.794Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T23:35:46.511Z { "code": "const r = await tools.write_stdin({\"session_id\":45894,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":12000}); text(r.output);\n" } ### Tool output · 2026-09-24T23:36:16.528Z Script completed Wall time 30.0 seconds Output: prepared users=5,000 tokens=11,354,844 states=188,776 elapsed=26.4s prepared users=6,000 tokens=13,485,786 states=224,384 elapsed=31.4s prepared users=7,000 tokens=15,826,114 states=265,318 elapsed=37.1s prepared users=8,000 tokens=18,028,622 states=306,474 elapsed=42.6s prepared users=9,000 tokens=20,195,749 states=342,272 elapsed=47.6s target users without qualifying catalog sequence=6 prepared stage=submission sequence_users=9,153 tokens=20,712,971 next_item_targets=20,703,818 states=350,322 saved public-derived full-sequence cache: /app/solution/submission_sequences.npz chronological BCE window: train=1,067,606 validation=131,997 selected=1,199,603 discarded_old_targets=19,504,215 all=20,703,818 inbatch correction: min=0.250 mean_on_targets=1.001 max=4.000 target weighting=organic-strong min=0.250 mean=0.576 max=1.000 full-history false-negative filter: users=9,153 unique_user_items=5,682,910 bytes=34,323,750 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "session_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "root_turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "response_id": "resp_08539f19231b9e9f016ab5b3d0adcc87d2b2f23d3f4a89c9a5", "usage": { "input_tokens": 100191, "cached_input_tokens": 99840, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 100244 }, "turn_token_usage": { "input_tokens": 9777072, "cached_input_tokens": 9665024, "cache_write_input_tokens": 0, "output_tokens": 29497, "reasoning_output_tokens": 10714, "total_tokens": 9806569 }, "thread_token_usage": { "input_tokens": 9777072, "cached_input_tokens": 9665024, "cache_write_input_tokens": 0, "output_tokens": 29497, "reasoning_output_tokens": 10714, "total_tokens": 9806569 } } ## Step 137 · agent · 2026-09-24T23:36:18.155Z ### Thinking unavailable — no text recorded A valid fallback submission is already in place. The final selected refit is now using `train_* + val_*` only, with test-cutoff preference state and eligibility. Its sole serving change is absorbed into the offline item vectors (`v_i / ||v_i||^0.2`), so request-time computation remains one user tower followed by batched dot products. ### exec · 2026-09-24T23:36:20.466Z { "code": "const r = await tools.write_stdin({\"session_id\":45894,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":12000}); text(r.output);\n" } ### Tool output · 2026-09-24T23:36:50.475Z Script completed Wall time 30.0 seconds Output: ### Harness context { "type": "token_usage_record", "thread_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "session_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "root_turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "response_id": "resp_08539f19231b9e9f016ab5b3f0bffc87d2b5b058ddcd86fd6a", "usage": { "input_tokens": 100564, "cached_input_tokens": 99968, "cache_write_input_tokens": 0, "output_tokens": 148, "reasoning_output_tokens": 19, "total_tokens": 100712 }, "turn_token_usage": { "input_tokens": 9877636, "cached_input_tokens": 9764992, "cache_write_input_tokens": 0, "output_tokens": 29645, "reasoning_output_tokens": 10733, "total_tokens": 9907281 }, "thread_token_usage": { "input_tokens": 9877636, "cached_input_tokens": 9764992, "cache_write_input_tokens": 0, "output_tokens": 29645, "reasoning_output_tokens": 10733, "total_tokens": 9907281 } } ### exec · 2026-09-24T23:36:55.439Z { "code": "const r = await tools.write_stdin({\"session_id\":45894,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":12000}); text(r.output);\n" } ### Tool output · 2026-09-24T23:37:25.448Z Script completed Wall time 30.0 seconds Output: ### Harness context { "type": "token_usage_record", "thread_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "session_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "root_turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "response_id": "resp_08539f19231b9e9f016ab5b412aeb487d2b549ad401b8c7946", "usage": { "input_tokens": 100735, "cached_input_tokens": 100352, "cache_write_input_tokens": 0, "output_tokens": 45, "reasoning_output_tokens": 0, "total_tokens": 100780 }, "turn_token_usage": { "input_tokens": 9978371, "cached_input_tokens": 9865344, "cache_write_input_tokens": 0, "output_tokens": 29690, "reasoning_output_tokens": 10733, "total_tokens": 10008061 }, "thread_token_usage": { "input_tokens": 9978371, "cached_input_tokens": 9865344, "cache_write_input_tokens": 0, "output_tokens": 29690, "reasoning_output_tokens": 10733, "total_tokens": 10008061 } } ### exec · 2026-09-24T23:37:27.380Z { "code": "const r = await tools.write_stdin({\"session_id\":45894,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":12000}); text(r.output);\n" } ### Tool output · 2026-09-24T23:37:57.414Z Script completed Wall time 30.0 seconds Output: ### Harness context { "type": "token_usage_record", "thread_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "session_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "root_turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "response_id": "resp_08539f19231b9e9f016ab5b435bd8487d2a134611eba937b11", "usage": { "input_tokens": 100803, "cached_input_tokens": 100608, "cache_write_input_tokens": 0, "output_tokens": 45, "reasoning_output_tokens": 0, "total_tokens": 100848 }, "turn_token_usage": { "input_tokens": 10079174, "cached_input_tokens": 9965952, "cache_write_input_tokens": 0, "output_tokens": 29735, "reasoning_output_tokens": 10733, "total_tokens": 10108909 }, "thread_token_usage": { "input_tokens": 10079174, "cached_input_tokens": 9965952, "cache_write_input_tokens": 0, "output_tokens": 29735, "reasoning_output_tokens": 10733, "total_tokens": 10108909 } } ## Step 138 · agent · 2026-09-24T23:37:58.698Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T23:37:59.577Z { "code": "const r = await tools.write_stdin({\"session_id\":45894,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":12000}); text(r.output);\n" } ### Tool output · 2026-09-24T23:38:29.597Z Script completed Wall time 30.0 seconds Output: ### Harness context { "type": "token_usage_record", "thread_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "session_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "root_turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "response_id": "resp_08539f19231b9e9f016ab5b4559b3087d2ba618299bcda358e", "usage": { "input_tokens": 100871, "cached_input_tokens": 100608, "cache_write_input_tokens": 0, "output_tokens": 54, "reasoning_output_tokens": 7, "total_tokens": 100925 }, "turn_token_usage": { "input_tokens": 10180045, "cached_input_tokens": 10066560, "cache_write_input_tokens": 0, "output_tokens": 29789, "reasoning_output_tokens": 10740, "total_tokens": 10209834 }, "thread_token_usage": { "input_tokens": 10180045, "cached_input_tokens": 10066560, "cache_write_input_tokens": 0, "output_tokens": 29789, "reasoning_output_tokens": 10740, "total_tokens": 10209834 } } ## Step 139 · agent · 2026-09-24T23:38:31.029Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T23:38:31.773Z { "code": "const r = await tools.write_stdin({\"session_id\":45894,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":12000}); text(r.output);\n" } ### Tool output · 2026-09-24T23:39:01.781Z Script completed Wall time 30.0 seconds Output: ### Harness context { "type": "token_usage_record", "thread_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "session_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "root_turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "response_id": "resp_08539f19231b9e9f016ab5b475dcb487d28a8f3e08af47db92", "usage": { "input_tokens": 100948, "cached_input_tokens": 100736, "cache_write_input_tokens": 0, "output_tokens": 56, "reasoning_output_tokens": 9, "total_tokens": 101004 }, "turn_token_usage": { "input_tokens": 10280993, "cached_input_tokens": 10167296, "cache_write_input_tokens": 0, "output_tokens": 29845, "reasoning_output_tokens": 10749, "total_tokens": 10310838 }, "thread_token_usage": { "input_tokens": 10280993, "cached_input_tokens": 10167296, "cache_write_input_tokens": 0, "output_tokens": 29845, "reasoning_output_tokens": 10749, "total_tokens": 10310838 } } ## Step 140 · agent · 2026-09-24T23:39:04.406Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T23:39:05.092Z { "code": "const r = await tools.write_stdin({\"session_id\":45894,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":12000}); text(r.output);\n" } ### Tool output · 2026-09-24T23:39:35.119Z Script completed Wall time 30.0 seconds Output: epoch=1/1 batches=250 targets=994,228 batch_bce=0.460538 running_bce=0.541708 elapsed=200.7s ### Harness context { "type": "token_usage_record", "thread_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "session_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "root_turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "response_id": "resp_08539f19231b9e9f016ab5b4961af887d2afad65e501ba38b5", "usage": { "input_tokens": 101027, "cached_input_tokens": 100736, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 101080 }, "turn_token_usage": { "input_tokens": 10382020, "cached_input_tokens": 10268032, "cache_write_input_tokens": 0, "output_tokens": 29898, "reasoning_output_tokens": 10755, "total_tokens": 10411918 }, "thread_token_usage": { "input_tokens": 10382020, "cached_input_tokens": 10268032, "cache_write_input_tokens": 0, "output_tokens": 29898, "reasoning_output_tokens": 10755, "total_tokens": 10411918 } } ## Step 141 · agent · 2026-09-24T23:39:36.456Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T23:39:37.157Z { "code": "const r = await tools.write_stdin({\"session_id\":45894,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":12000}); text(r.output);\n" } ### Tool output · 2026-09-24T23:40:07.168Z Script completed Wall time 30.0 seconds Output: saved epoch checkpoint: /app/solution/final_checkpoints/epoch-01.pt epoch=1/1 train_loss=0.53835 validation_loss=0.49795 pair_accuracy=0.755 train_targets=1,067,606 validation_targets=131,997 train_seconds=215.7 validation_seconds=15.7 restored best validation-BCE checkpoint: epoch=1 loss=0.497949 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "session_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "root_turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "response_id": "resp_08539f19231b9e9f016ab5b4b7541487d28dfa76108a0ab9d9", "usage": { "input_tokens": 101139, "cached_input_tokens": 100864, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 101192 }, "turn_token_usage": { "input_tokens": 10483159, "cached_input_tokens": 10368896, "cache_write_input_tokens": 0, "output_tokens": 29951, "reasoning_output_tokens": 10761, "total_tokens": 10513110 }, "thread_token_usage": { "input_tokens": 10483159, "cached_input_tokens": 10368896, "cache_write_input_tokens": 0, "output_tokens": 29951, "reasoning_output_tokens": 10761, "total_tokens": 10513110 } } ## Step 142 · agent · 2026-09-24T23:40:08.786Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T23:40:09.553Z { "code": "const r = await tools.write_stdin({\"session_id\":45894,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":12000}); text(r.output);\n" } ### Tool output · 2026-09-24T23:40:39.581Z Script completed Wall time 30.0 seconds Output: ### Harness context { "type": "token_usage_record", "thread_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "session_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "root_turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "response_id": "resp_08539f19231b9e9f016ab5b4d779d487d2935b78f842607c4a", "usage": { "input_tokens": 101303, "cached_input_tokens": 100992, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 101356 }, "turn_token_usage": { "input_tokens": 10584462, "cached_input_tokens": 10469888, "cache_write_input_tokens": 0, "output_tokens": 30004, "reasoning_output_tokens": 10767, "total_tokens": 10614466 }, "thread_token_usage": { "input_tokens": 10584462, "cached_input_tokens": 10469888, "cache_write_input_tokens": 0, "output_tokens": 30004, "reasoning_output_tokens": 10767, "total_tokens": 10614466 } } ### exec · 2026-09-24T23:40:41.677Z { "code": "const r = await tools.write_stdin({\"session_id\":45894,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":12000}); text(r.output);\n" } ### Tool output · 2026-09-24T23:41:11.709Z Script completed Wall time 30.0 seconds Output: ### Harness context { "type": "token_usage_record", "thread_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "session_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "root_turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "response_id": "resp_08539f19231b9e9f016ab5b4f7d74c87d2b99960d2a9914f0e", "usage": { "input_tokens": 101379, "cached_input_tokens": 101120, "cache_write_input_tokens": 0, "output_tokens": 45, "reasoning_output_tokens": 0, "total_tokens": 101424 }, "turn_token_usage": { "input_tokens": 10685841, "cached_input_tokens": 10571008, "cache_write_input_tokens": 0, "output_tokens": 30049, "reasoning_output_tokens": 10767, "total_tokens": 10715890 }, "thread_token_usage": { "input_tokens": 10685841, "cached_input_tokens": 10571008, "cache_write_input_tokens": 0, "output_tokens": 30049, "reasoning_output_tokens": 10767, "total_tokens": 10715890 } } ### exec · 2026-09-24T23:41:13.887Z { "code": "const r = await tools.write_stdin({\"session_id\":45894,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":15000}); text(r.output);\n" } ### Tool output · 2026-09-24T23:41:25.662Z Script completed Wall time 11.8 seconds Output: ranked users=1,024/8,456 ranked users=2,048/8,456 ranked users=3,072/8,456 ranked users=4,096/8,456 ranked users=5,120/8,456 ranked users=6,144/8,456 ranked users=7,168/8,456 ranked users=8,192/8,456 ranked users=8,456/8,456 { "baseline": "yambda-windowed-sequence-two-tower-base", "stage": "submission", "output": "/app/solution/submission.parquet", "rows": 845600, "users": 8456, "runtime_seconds": 369.967, "public_inputs_only": true, "architecture": { "family": "pure-windowed-sequence-two-tower", "sequence_training": "every-adjacent-target-within-recent-chronological-window", "sequence_encoder": "one-layer-GRU-dense-timestep-loss", "bptt_hidden_state": "carried-and-detached", "model_selection": "chronological-validation-BCE-early-stopping", "loss_execution": "gradient-accumulated-microbatches", "user_id_embedding": false, "user_mlp": "582-512-256-192", "item_mlp": "196-512-256-192", "normalization": "offline-item-norm-power-0.2", "score": "raw-dot-product-with-precomputed-scaled-items", "objective": "sample-weighted-bce-at-every-timestep", "negative_sampling": "24-uniform-plus-8-mixed", "false_negative_filter": "full-training-history", "target_weighting": "organic-strong" }, "config": { "bptt_steps": 100, "user_batch_size": 64, "embedding_dim": 192, "hidden_dim": 192, "user_hidden_1": 512, "user_hidden_2": 256, "item_hidden_1": 512, "item_hidden_2": 256, "dropout": 0.05, "epochs": 1, "learning_rate": 0.001, "weight_decay": 1e-05, "gradient_clip": 2.0, "loss_microbatch_size": 65536, "uniform_negatives": 24, "inbatch_negatives": 8, "negative_sampling": "mixed", "hard_pool_size": 512, "retrieval_loss": "bce", "item_norm_power": 0.2, "inbatch_correction_power": 0.5, "inbatch_correction_min": 0.25, "inbatch_correction_max": 4.0, "negative_filter_scope": "full-training-history", "target_weighting": "organic-strong", "ordinary_target_weight": 0.25, "organic_strong_target_weight": 1.0, "organic_like_target_weight": 3.0, "discovery_horizon_days": 7, "validation_fraction": 0.1, "max_train_targets_per_user": 128, "max_validation_targets_per_user": 16, "early_stopping_patience": 1, "early_stopping_min_delta": 0.0001, "seed": 29 }, "sequence_tokens": 20712971, "all_next_item_targets": 20703818, "selected_training_targets": 1067606, "selected_validation_targets": 131997, "discarded_old_targets": 19504215, "training_history": [ { "epoch": 1.0, "loss": 0.5383531188796526, "positive_logit": 0.7500169995404112, "negative_logit": -0.6300986848549689, "pair_accuracy": 0.7552423658634365, "targets": 1067606.0, "target_weight_sum": 641383.25, "batches": 270.0, "seconds": 215.69760479800016, "validation_loss": 0.4979487096606844, "validation_targets": 131997.0, "validation_target_weight_sum": 80328.75, "validation_seconds": 15.682870391000051 } ], "embedding_diagnostics": { "query_norm_mean": 1.6518515348434448, "query_norm_std": 0.8145266175270081, "item_norm_mean": 1.6446387767791748, "item_norm_std": 0.6429429650306702 }, "torch_version": "2.8.0+cpu", "device": "cpu" } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "session_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "root_turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "response_id": "resp_08539f19231b9e9f016ab5b518206087d2ae2f537278af2c3e", "usage": { "input_tokens": 101447, "cached_input_tokens": 101248, "cache_write_input_tokens": 0, "output_tokens": 45, "reasoning_output_tokens": 0, "total_tokens": 101492 }, "turn_token_usage": { "input_tokens": 10787288, "cached_input_tokens": 10672256, "cache_write_input_tokens": 0, "output_tokens": 30094, "reasoning_output_tokens": 10767, "total_tokens": 10817382 }, "thread_token_usage": { "input_tokens": 10787288, "cached_input_tokens": 10672256, "cache_write_input_tokens": 0, "output_tokens": 30094, "reasoning_output_tokens": 10767, "total_tokens": 10817382 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-3326d9d6-885a-423b-afec-56d67b460d2f", "process_id": "45894", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python /app/solution/train_current_model.py --stage submission --public-dir /task --prepared-cache /app/solution/submission_sequences.npz --negative-sampling mixed --uniform-negatives 24 --inbatch-negatives 8 --inbatch-correction-power 0.5 --retrieval-loss bce --item-norm-power 0.20 --epochs 1 --checkpoint-dir /app/solution/final_checkpoints --output /app/solution/submission.parquet 2>&1 | tee /app/solution/final_training.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python /app/solution/train_current_model.py --stage submission --public-dir /task --prepared-cache /app/solution/submission_sequences.npz --negative-sampling mixed --uniform-negatives 24 --inbatch-negatives 8 --inbatch-correction-power 0.5 --retrieval-loss bce --item-norm-power 0.20 --epochs 1 --checkpoint-dir /app/solution/final_checkpoints --output /app/solution/submission.parquet 2>&1 | tee /app/solution/final_training.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "loaded explicit events: rows=746,504 users=8,307\nprepared users=1,000 tokens=2,167,338 states=37,073 elapsed=5.1s\nprepared users=2,000 tokens=4,450,246 states=73,784 elapsed=10.4s\nprepared users=3,000 tokens=6,703,970 states=113,190 elapsed=15.7s\nprepared users=4,000 tokens=9,087,187 states=153,283 elapsed=21.4s\nprepared users=5,000 tokens=11,354,844 states=188,776 elapsed=26.4s\nprepared users=6,000 tokens=13,485,786 states=224,384 elapsed=31.4s\nprepared users=7,000 tokens=15,826,114 states=265,318 elapsed=37.1s\nprepared users=8,000 tokens=18,028,622 states=306,474 elapsed=42.6s\nprepared users=9,000 tokens=20,195,749 states=342,272 elapsed=47.6s\ntarget users without qualifying catalog sequence=6\nprepared stage=submission sequence_users=9,153 tokens=20,712,971 next_item_targets=20,703,818 states=350,322\nsaved public-derived full-sequence cache: /app/solution/submission_sequences.npz\nchronological BCE window: train=1,067,606 validation=131,997 selected=1,199,603 discarded_old_targets=19,504,215 all=20,703,818\ninbatch correction: min=0.250 mean_on_targets=1.001 max=4.000\ntarget weighting=organic-strong min=0.250 mean=0.576 max=1.000\nfull-history false-negative filter: users=9,153 unique_user_items=5,682,910 bytes=34,323,750\nepoch=1/1 batches=250 targets=994,228 batch_bce=0.460538 running_bce=0.541708 elapsed=200.7s\nsaved epoch checkpoint: /app/solution/final_checkpoints/epoch-01.pt\nepoch=1/1 train_loss=0.53835 validation_loss=0.49795 pair_accuracy=0.755 train_targets=1,067,606 validation_targets=131,997 train_seconds=215.7 validation_seconds=15.7\nrestored best validation-BCE checkpoint: epoch=1 loss=0.497949\nranked users=1,024/8,456\nranked users=2,048/8,456\nranked users=3,072/8,456\nranked users=4,096/8,456\nranked users=5,120/8,456\nranked users=6,144/8,456\nranked users=7,168/8,456\nranked users=8,192/8,456\nranked users=8,456/8,456\n{\n \"baseline\": \"yambda-windowed-sequence-two-tower-base\",\n \"stage\": \"submission\",\n \"output\": \"/app/solution/submission.parquet\",\n \"rows\": 845600,\n \"users\": 8456,\n \"runtime_seconds\": 369.967,\n \"public_inputs_only\": true,\n \"architecture\": {\n \"family\": \"pure-windowed-sequence-two-tower\",\n \"sequence_training\": \"every-adjacent-target-within-recent-chronological-window\",\n \"sequence_encoder\": \"one-layer-GRU-dense-timestep-loss\",\n \"bptt_hidden_state\": \"carried-and-detached\",\n \"model_selection\": \"chronological-validation-BCE-early-stopping\",\n \"loss_execution\": \"gradient-accumulated-microbatches\",\n \"user_id_embedding\": false,\n \"user_mlp\": \"582-512-256-192\",\n \"item_mlp\": \"196-512-256-192\",\n \"normalization\": \"offline-item-norm-power-0.2\",\n \"score\": \"raw-dot-product-with-precomputed-scaled-items\",\n \"objective\": \"sample-weighted-bce-at-every-timestep\",\n \"negative_sampling\": \"24-uniform-plus-8-mixed\",\n \"false_negative_filter\": \"full-training-history\",\n \"target_weighting\": \"organic-strong\"\n },\n \"config\": {\n \"bptt_steps\": 100,\n \"user_batch_size\": 64,\n \"embedding_dim\": 192,\n \"hidden_dim\": 192,\n \"user_hidden_1\": 512,\n \"user_hidden_2\": 256,\n \"item_hidden_1\": 512,\n \"item_hidden_2\": 256,\n \"dropout\": 0.05,\n \"epochs\": 1,\n \"learning_rate\": 0.001,\n \"weight_decay\": 1e-05,\n \"gradient_clip\": 2.0,\n \"loss_microbatch_size\": 65536,\n \"uniform_negatives\": 24,\n \"inbatch_negatives\": 8,\n \"negative_sampling\": \"mixed\",\n \"hard_pool_size\": 512,\n \"retrieval_loss\": \"bce\",\n \"item_norm_power\": 0.2,\n \"inbatch_correction_power\": 0.5,\n \"inbatch_correction_min\": 0.25,\n \"inbatch_correction_max\": 4.0,\n \"negative_filter_scope\": \"full-training-history\",\n \"target_weighting\": \"organic-strong\",\n \"ordinary_target_weight\": 0.25,\n \"organic_strong_target_weight\": 1.0,\n \"organic_like_target_weight\": 3.0,\n \"discovery_horizon_days\": 7,\n \"validation_fraction\": 0.1,\n \"max_train_targets_per_user\": 128,\n \"max_validation_targets_per_user\": 16,\n \"early_stopping_patience\": 1,\n \"early_stopping_min_delta\": 0.0001,\n \"seed\": 29\n },\n \"sequence_tokens\": 20712971,\n \"all_next_item_targets\": 20703818,\n \"selected_training_targets\": 1067606,\n \"selected_validation_targets\": 131997,\n \"discarded_old_targets\": 19504215,\n \"training_history\": [\n {\n \"epoch\": 1.0,\n \"loss\": 0.5383531188796526,\n \"positive_logit\": 0.7500169995404112,\n \"negative_logit\": -0.6300986848549689,\n \"pair_accuracy\": 0.7552423658634365,\n \"targets\": 1067606.0,\n \"target_weight_sum\": 641383.25,\n \"batches\": 270.0,\n \"seconds\": 215.69760479800016,\n \"validation_loss\": 0.4979487096606844,\n \"validation_targets\": 131997.0,\n \"validation_target_weight_sum\": 80328.75,\n \"validation_seconds\": 15.682870391000051\n }\n ],\n \"embedding_diagnostics\": {\n \"query_norm_mean\": 1.6518515348434448,\n \"query_norm_std\": 0.8145266175270081,\n \"item_norm_mean\": 1.6446387767791748,\n \"item_norm_std\": 0.6429429650306702\n },\n \"torch_version\": \"2.8.0+cpu\",\n \"device\": \"cpu\"\n}\n", "stderr": "", "aggregated_output": "loaded explicit events: rows=746,504 users=8,307\nprepared users=1,000 tokens=2,167,338 states=37,073 elapsed=5.1s\nprepared users=2,000 tokens=4,450,246 states=73,784 elapsed=10.4s\nprepared users=3,000 tokens=6,703,970 states=113,190 elapsed=15.7s\nprepared users=4,000 tokens=9,087,187 states=153,283 elapsed=21.4s\nprepared users=5,000 tokens=11,354,844 states=188,776 elapsed=26.4s\nprepared users=6,000 tokens=13,485,786 states=224,384 elapsed=31.4s\nprepared users=7,000 tokens=15,826,114 states=265,318 elapsed=37.1s\nprepared users=8,000 tokens=18,028,622 states=306,474 elapsed=42.6s\nprepared users=9,000 tokens=20,195,749 states=342,272 elapsed=47.6s\ntarget users without qualifying catalog sequence=6\nprepared stage=submission sequence_users=9,153 tokens=20,712,971 next_item_targets=20,703,818 states=350,322\nsaved public-derived full-sequence cache: /app/solution/submission_sequences.npz\nchronological BCE window: train=1,067,606 validation=131,997 selected=1,199,603 discarded_old_targets=19,504,215 all=20,703,818\ninbatch correction: min=0.250 mean_on_targets=1.001 max=4.000\ntarget weighting=organic-strong min=0.250 mean=0.576 max=1.000\nfull-history false-negative filter: users=9,153 unique_user_items=5,682,910 bytes=34,323,750\nepoch=1/1 batches=250 targets=994,228 batch_bce=0.460538 running_bce=0.541708 elapsed=200.7s\nsaved epoch checkpoint: /app/solution/final_checkpoints/epoch-01.pt\nepoch=1/1 train_loss=0.53835 validation_loss=0.49795 pair_accuracy=0.755 train_targets=1,067,606 validation_targets=131,997 train_seconds=215.7 validation_seconds=15.7\nrestored best validation-BCE checkpoint: epoch=1 loss=0.497949\nranked users=1,024/8,456\nranked users=2,048/8,456\nranked users=3,072/8,456\nranked users=4,096/8,456\nranked users=5,120/8,456\nranked users=6,144/8,456\nranked users=7,168/8,456\nranked users=8,192/8,456\nranked users=8,456/8,456\n{\n \"baseline\": \"yambda-windowed-sequence-two-tower-base\",\n \"stage\": \"submission\",\n \"output\": \"/app/solution/submission.parquet\",\n \"rows\": 845600,\n \"users\": 8456,\n \"runtime_seconds\": 369.967,\n \"public_inputs_only\": true,\n \"architecture\": {\n \"family\": \"pure-windowed-sequence-two-tower\",\n \"sequence_training\": \"every-adjacent-target-within-recent-chronological-window\",\n \"sequence_encoder\": \"one-layer-GRU-dense-timestep-loss\",\n \"bptt_hidden_state\": \"carried-and-detached\",\n \"model_selection\": \"chronological-validation-BCE-early-stopping\",\n \"loss_execution\": \"gradient-accumulated-microbatches\",\n \"user_id_embedding\": false,\n \"user_mlp\": \"582-512-256-192\",\n \"item_mlp\": \"196-512-256-192\",\n \"normalization\": \"offline-item-norm-power-0.2\",\n \"score\": \"raw-dot-product-with-precomputed-scaled-items\",\n \"objective\": \"sample-weighted-bce-at-every-timestep\",\n \"negative_sampling\": \"24-uniform-plus-8-mixed\",\n \"false_negative_filter\": \"full-training-history\",\n \"target_weighting\": \"organic-strong\"\n },\n \"config\": {\n \"bptt_steps\": 100,\n \"user_batch_size\": 64,\n \"embedding_dim\": 192,\n \"hidden_dim\": 192,\n \"user_hidden_1\": 512,\n \"user_hidden_2\": 256,\n \"item_hidden_1\": 512,\n \"item_hidden_2\": 256,\n \"dropout\": 0.05,\n \"epochs\": 1,\n \"learning_rate\": 0.001,\n \"weight_decay\": 1e-05,\n \"gradient_clip\": 2.0,\n \"loss_microbatch_size\": 65536,\n \"uniform_negatives\": 24,\n \"inbatch_negatives\": 8,\n \"negative_sampling\": \"mixed\",\n \"hard_pool_size\": 512,\n \"retrieval_loss\": \"bce\",\n \"item_norm_power\": 0.2,\n \"inbatch_correction_power\": 0.5,\n \"inbatch_correction_min\": 0.25,\n \"inbatch_correction_max\": 4.0,\n \"negative_filter_scope\": \"full-training-history\",\n \"target_weighting\": \"organic-strong\",\n \"ordinary_target_weight\": 0.25,\n \"organic_strong_target_weight\": 1.0,\n \"organic_like_target_weight\": 3.0,\n \"discovery_horizon_days\": 7,\n \"validation_fraction\": 0.1,\n \"max_train_targets_per_user\": 128,\n \"max_validation_targets_per_user\": 16,\n \"early_stopping_patience\": 1,\n \"early_stopping_min_delta\": 0.0001,\n \"seed\": 29\n },\n \"sequence_tokens\": 20712971,\n \"all_next_item_targets\": 20703818,\n \"selected_training_targets\": 1067606,\n \"selected_validation_targets\": 131997,\n \"discarded_old_targets\": 19504215,\n \"training_history\": [\n {\n \"epoch\": 1.0,\n \"loss\": 0.5383531188796526,\n \"positive_logit\": 0.7500169995404112,\n \"negative_logit\": -0.6300986848549689,\n \"pair_accuracy\": 0.7552423658634365,\n \"targets\": 1067606.0,\n \"target_weight_sum\": 641383.25,\n \"batches\": 270.0,\n \"seconds\": 215.69760479800016,\n \"validation_loss\": 0.4979487096606844,\n \"validation_targets\": 131997.0,\n \"validation_target_weight_sum\": 80328.75,\n \"validation_seconds\": 15.682870391000051\n }\n ],\n \"embedding_diagnostics\": {\n \"query_norm_mean\": 1.6518515348434448,\n \"query_norm_std\": 0.8145266175270081,\n \"item_norm_mean\": 1.6446387767791748,\n \"item_norm_std\": 0.6429429650306702\n },\n \"torch_version\": \"2.8.0+cpu\",\n \"device\": \"cpu\"\n}\n", "exit_code": 0, "duration": { "secs": 374, "nanos": 340736967 }, "formatted_output": "loaded explicit events: rows=746,504 users=8,307\nprepared users=1,000 tokens=2,167,338 states=37,073 elapsed=5.1s\nprepared users=2,000 tokens=4,450,246 states=73,784 elapsed=10.4s\nprepared users=3,000 tokens=6,703,970 states=113,190 elapsed=15.7s\nprepared users=4,000 tokens=9,087,187 states=153,283 elapsed=21.4s\nprepared users=5,000 tokens=11,354,844 states=188,776 elapsed=26.4s\nprepared users=6,000 tokens=13,485,786 states=224,384 elapsed=31.4s\nprepared users=7,000 tokens=15,826,114 states=265,318 elapsed=37.1s\nprepared users=8,000 tokens=18,028,622 states=306,474 elapsed=42.6s\nprepared users=9,000 tokens=20,195,749 states=342,272 elapsed=47.6s\ntarget users without qualifying catalog sequence=6\nprepared stage=submission sequence_users=9,153 tokens=20,712,971 next_item_targets=20,703,818 states=350,322\nsaved public-derived full-sequence cache: /app/solution/submission_sequences.npz\nchronological BCE window: train=1,067,606 validation=131,997 selected=1,199,603 discarded_old_targets=19,504,215 all=20,703,818\ninbatch correction: min=0.250 mean_on_targets=1.001 max=4.000\ntarget weighting=organic-strong min=0.250 mean=0.576 max=1.000\nfull-history false-negative filter: users=9,153 unique_user_items=5,682,910 bytes=34,323,750\nepoch=1/1 batches=250 targets=994,228 batch_bce=0.460538 running_bce=0.541708 elapsed=200.7s\nsaved epoch checkpoint: /app/solution/final_checkpoints/epoch-01.pt\nepoch=1/1 train_loss=0.53835 validation_loss=0.49795 pair_accuracy=0.755 train_targets=1,067,606 validation_targets=131,997 train_seconds=215.7 validation_seconds=15.7\nrestored best validation-BCE checkpoint: epoch=1 loss=0.497949\nranked users=1,024/8,456\nranked users=2,048/8,456\nranked users=3,072/8,456\nranked users=4,096/8,456\nranked users=5,120/8,456\nranked users=6,144/8,456\nranked users=7,168/8,456\nranked users=8,192/8,456\nranked users=8,456/8,456\n{\n \"baseline\": \"yambda-windowed-sequence-two-tower-base\",\n \"stage\": \"submission\",\n \"output\": \"/app/solution/submission.parquet\",\n \"rows\": 845600,\n \"users\": 8456,\n \"runtime_seconds\": 369.967,\n \"public_inputs_only\": true,\n \"architecture\": {\n \"family\": \"pure-windowed-sequence-two-tower\",\n \"sequence_training\": \"every-adjacent-target-within-recent-chronological-window\",\n \"sequence_encoder\": \"one-layer-GRU-dense-timestep-loss\",\n \"bptt_hidden_state\": \"carried-and-detached\",\n \"model_selection\": \"chronological-validation-BCE-early-stopping\",\n \"loss_execution\": \"gradient-accumulated-microbatches\",\n \"user_id_embedding\": false,\n \"user_mlp\": \"582-512-256-192\",\n \"item_mlp\": \"196-512-256-192\",\n \"normalization\": \"offline-item-norm-power-0.2\",\n \"score\": \"raw-dot-product-with-precomputed-scaled-items\",\n \"objective\": \"sample-weighted-bce-at-every-timestep\",\n \"negative_sampling\": \"24-uniform-plus-8-mixed\",\n \"false_negative_filter\": \"full-training-history\",\n \"target_weighting\": \"organic-strong\"\n },\n \"config\": {\n \"bptt_steps\": 100,\n \"user_batch_size\": 64,\n \"embedding_dim\": 192,\n \"hidden_dim\": 192,\n \"user_hidden_1\": 512,\n \"user_hidden_2\": 256,\n \"item_hidden_1\": 512,\n \"item_hidden_2\": 256,\n \"dropout\": 0.05,\n \"epochs\": 1,\n \"learning_rate\": 0.001,\n \"weight_decay\": 1e-05,\n \"gradient_clip\": 2.0,\n \"loss_microbatch_size\": 65536,\n \"uniform_negatives\": 24,\n \"inbatch_negatives\": 8,\n \"negative_sampling\": \"mixed\",\n \"hard_pool_size\": 512,\n \"retrieval_loss\": \"bce\",\n \"item_norm_power\": 0.2,\n \"inbatch_correction_power\": 0.5,\n \"inbatch_correction_min\": 0.25,\n \"inbatch_correction_max\": 4.0,\n \"negative_filter_scope\": \"full-training-history\",\n \"target_weighting\": \"organic-strong\",\n \"ordinary_target_weight\": 0.25,\n \"organic_strong_target_weight\": 1.0,\n \"organic_like_target_weight\": 3.0,\n \"discovery_horizon_days\": 7,\n \"validation_fraction\": 0.1,\n \"max_train_targets_per_user\": 128,\n \"max_validation_targets_per_user\": 16,\n \"early_stopping_patience\": 1,\n \"early_stopping_min_delta\": 0.0001,\n \"seed\": 29\n },\n \"sequence_tokens\": 20712971,\n \"all_next_item_targets\": 20703818,\n \"selected_training_targets\": 1067606,\n \"selected_validation_targets\": 131997,\n \"discarded_old_targets\": 19504215,\n \"training_history\": [\n {\n \"epoch\": 1.0,\n \"loss\": 0.5383531188796526,\n \"positive_logit\": 0.7500169995404112,\n \"negative_logit\": -0.6300986848549689,\n \"pair_accuracy\": 0.7552423658634365,\n \"targets\": 1067606.0,\n \"target_weight_sum\": 641383.25,\n \"batches\": 270.0,\n \"seconds\": 215.69760479800016,\n \"validation_loss\": 0.4979487096606844,\n \"validation_targets\": 131997.0,\n \"validation_target_weight_sum\": 80328.75,\n \"validation_seconds\": 15.682870391000051\n }\n ],\n \"embedding_diagnostics\": {\n \"query_norm_mean\": 1.6518515348434448,\n \"query_norm_std\": 0.8145266175270081,\n \"item_norm_mean\": 1.6446387767791748,\n \"item_norm_std\": 0.6429429650306702\n },\n \"torch_version\": \"2.8.0+cpu\",\n \"device\": \"cpu\"\n}\n" } } ## Step 143 · agent · 2026-09-24T23:41:35.603Z ### Thinking unavailable — no text recorded The final refit completed successfully: 1.068M training targets, 132k chronological internal-validation targets, and 20.7M observed sequence events through the submission cutoff. I’m doing the last schema/eligibility audit now and recording both the successful public result (`0.021804`) and the rejected negative-sampling ablations for reproducibility. ### exec · 2026-09-24T23:41:49.612Z { "code": "const r = await tools.exec_command({\"cmd\":\"cp /app/solution/norm20_validation.parquet /app/solution/improved_validation.parquet\\n/opt/task-venv/bin/python - <<'PY'\\nfrom pathlib import Path\\nimport numpy as np,pandas as pd\\np=Path('/app/solution/submission.parquet'); assert p.is_file() and not p.is_symlink()\\nr=pd.read_parquet(p); req=['uid','item_id','rank']; assert all(c in r for c in req); assert len(r)==845600\\nfor c in req:\\n assert pd.api.types.is_integer_dtype(r[c]); assert np.isfinite(r[c].to_numpy()).all()\\nt=pd.read_parquet('/task/data/target_users.parquet'); assert set(r.uid)==set(t.uid)\\ng=r.groupby('uid',sort=False); assert (g.size()==100).all(); assert (g.item_id.nunique()==100).all(); assert g['rank'].apply(lambda x:set(x)==set(range(1,101))).all()\\nwith np.load('/task/data/test_eligible_candidates.npz') as a:\\n u=a['uids'].copy(); ids=a['item_ids'].copy(); packed=a['packed_eligible'].copy()\\nuidrow={int(x):i for i,x in enumerate(u)}; itemcol={int(x):i for i,x in enumerate(ids)}\\nfor uid,item in r[['uid','item_id']].itertuples(index=False,name=None):\\n row=uidrow[int(uid)]; col=itemcol[int(item)]; assert (int(packed[row,col>>3])>>(col&7))&1\\nprint({'file':str(p),'regular':True,'rows':len(r),'users':r.uid.nunique(),'rows_per_user':[int(g.size().min()),int(g.size().max())],'distinct_items_per_user':[int(g.item_id.nunique().min()),int(g.item_id.nunique().max())],'ranks':'1..100 exactly','all_test_eligible':True,'dtypes':{c:str(r[c].dtype) for c in req}})\\nPY\\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/improved_validation.parquet\\nls -lh /app/solution/submission.parquet /app/solution/improved_validation.parquet /app/solution/train_current_model.py /app/solution/two_tower_model.py\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":8000}); text(r.output);" } ### Tool output · 2026-09-24T23:41:55.790Z Script completed Wall time 6.2 seconds Output: {'file': '/app/solution/submission.parquet', 'regular': True, 'rows': 845600, 'users': 8456, 'rows_per_user': [100, 100], 'distinct_items_per_user': [100, 100], 'ranks': '1..100 exactly', 'all_test_eligible': True, 'dtypes': {'uid': 'int64', 'item_id': 'int64', 'rank': 'int64'}} { "model": "/app/solution/improved_validation.parquet", "scores": { "quality_ndcg@10": 0.021804, "quality_users": 4263 } } -rw-r--r-- 1 agent agent 1.4M Sep 24 23:41 /app/solution/improved_validation.parquet -rw-r--r-- 1 agent agent 1.2M Sep 24 23:41 /app/solution/submission.parquet -rw-r--r-- 1 agent agent 88K Sep 24 23:30 /app/solution/train_current_model.py -rw-r--r-- 1 agent agent 16K Sep 24 22:49 /app/solution/two_tower_model.py ### Harness context { "type": "token_usage_record", "thread_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "session_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "root_turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "response_id": "resp_08539f19231b9e9f016ab5b525dd0c87d2a56b4b4a725c457e", "usage": { "input_tokens": 102621, "cached_input_tokens": 101248, "cache_write_input_tokens": 0, "output_tokens": 1112, "reasoning_output_tokens": 488, "total_tokens": 103733 }, "turn_token_usage": { "input_tokens": 10889909, "cached_input_tokens": 10773504, "cache_write_input_tokens": 0, "output_tokens": 31206, "reasoning_output_tokens": 11255, "total_tokens": 10921115 }, "thread_token_usage": { "input_tokens": 10889909, "cached_input_tokens": 10773504, "cache_write_input_tokens": 0, "output_tokens": 31206, "reasoning_output_tokens": 11255, "total_tokens": 10921115 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-ce0c501c-c226-40b6-9a23-05507b901b91", "process_id": "98097", "command": [ "/bin/sh", "-lc", "cp /app/solution/norm20_validation.parquet /app/solution/improved_validation.parquet\n/opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nimport numpy as np,pandas as pd\np=Path('/app/solution/submission.parquet'); assert p.is_file() and not p.is_symlink()\nr=pd.read_parquet(p); req=['uid','item_id','rank']; assert all(c in r for c in req); assert len(r)==845600\nfor c in req:\n assert pd.api.types.is_integer_dtype(r[c]); assert np.isfinite(r[c].to_numpy()).all()\nt=pd.read_parquet('/task/data/target_users.parquet'); assert set(r.uid)==set(t.uid)\ng=r.groupby('uid',sort=False); assert (g.size()==100).all(); assert (g.item_id.nunique()==100).all(); assert g['rank'].apply(lambda x:set(x)==set(range(1,101))).all()\nwith np.load('/task/data/test_eligible_candidates.npz') as a:\n u=a['uids'].copy(); ids=a['item_ids'].copy(); packed=a['packed_eligible'].copy()\nuidrow={int(x):i for i,x in enumerate(u)}; itemcol={int(x):i for i,x in enumerate(ids)}\nfor uid,item in r[['uid','item_id']].itertuples(index=False,name=None):\n row=uidrow[int(uid)]; col=itemcol[int(item)]; assert (int(packed[row,col>>3])>>(col&7))&1\nprint({'file':str(p),'regular':True,'rows':len(r),'users':r.uid.nunique(),'rows_per_user':[int(g.size().min()),int(g.size().max())],'distinct_items_per_user':[int(g.item_id.nunique().min()),int(g.item_id.nunique().max())],'ranks':'1..100 exactly','all_test_eligible':True,'dtypes':{c:str(r[c].dtype) for c in req}})\nPY\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/improved_validation.parquet\nls -lh /app/solution/submission.parquet /app/solution/improved_validation.parquet /app/solution/train_current_model.py /app/solution/two_tower_model.py" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cp /app/solution/norm20_validation.parquet /app/solution/improved_validation.parquet\n/opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nimport numpy as np,pandas as pd\np=Path('/app/solution/submission.parquet'); assert p.is_file() and not p.is_symlink()\nr=pd.read_parquet(p); req=['uid','item_id','rank']; assert all(c in r for c in req); assert len(r)==845600\nfor c in req:\n assert pd.api.types.is_integer_dtype(r[c]); assert np.isfinite(r[c].to_numpy()).all()\nt=pd.read_parquet('/task/data/target_users.parquet'); assert set(r.uid)==set(t.uid)\ng=r.groupby('uid',sort=False); assert (g.size()==100).all(); assert (g.item_id.nunique()==100).all(); assert g['rank'].apply(lambda x:set(x)==set(range(1,101))).all()\nwith np.load('/task/data/test_eligible_candidates.npz') as a:\n u=a['uids'].copy(); ids=a['item_ids'].copy(); packed=a['packed_eligible'].copy()\nuidrow={int(x):i for i,x in enumerate(u)}; itemcol={int(x):i for i,x in enumerate(ids)}\nfor uid,item in r[['uid','item_id']].itertuples(index=False,name=None):\n row=uidrow[int(uid)]; col=itemcol[int(item)]; assert (int(packed[row,col>>3])>>(col&7))&1\nprint({'file':str(p),'regular':True,'rows':len(r),'users':r.uid.nunique(),'rows_per_user':[int(g.size().min()),int(g.size().max())],'distinct_items_per_user':[int(g.item_id.nunique().min()),int(g.item_id.nunique().max())],'ranks':'1..100 exactly','all_test_eligible':True,'dtypes':{c:str(r[c].dtype) for c in req}})\nPY\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/improved_validation.parquet\nls -lh /app/solution/submission.parquet /app/solution/improved_validation.parquet /app/solution/train_current_model.py /app/solution/two_tower_model.py" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{'file': '/app/solution/submission.parquet', 'regular': True, 'rows': 845600, 'users': 8456, 'rows_per_user': [100, 100], 'distinct_items_per_user': [100, 100], 'ranks': '1..100 exactly', 'all_test_eligible': True, 'dtypes': {'uid': 'int64', 'item_id': 'int64', 'rank': 'int64'}}\n{\n \"model\": \"/app/solution/improved_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.021804,\n \"quality_users\": 4263\n }\n}\n-rw-r--r-- 1 agent agent 1.4M Sep 24 23:41 /app/solution/improved_validation.parquet\n-rw-r--r-- 1 agent agent 1.2M Sep 24 23:41 /app/solution/submission.parquet\n-rw-r--r-- 1 agent agent 88K Sep 24 23:30 /app/solution/train_current_model.py\n-rw-r--r-- 1 agent agent 16K Sep 24 22:49 /app/solution/two_tower_model.py\n", "stderr": "", "aggregated_output": "{'file': '/app/solution/submission.parquet', 'regular': True, 'rows': 845600, 'users': 8456, 'rows_per_user': [100, 100], 'distinct_items_per_user': [100, 100], 'ranks': '1..100 exactly', 'all_test_eligible': True, 'dtypes': {'uid': 'int64', 'item_id': 'int64', 'rank': 'int64'}}\n{\n \"model\": \"/app/solution/improved_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.021804,\n \"quality_users\": 4263\n }\n}\n-rw-r--r-- 1 agent agent 1.4M Sep 24 23:41 /app/solution/improved_validation.parquet\n-rw-r--r-- 1 agent agent 1.2M Sep 24 23:41 /app/solution/submission.parquet\n-rw-r--r-- 1 agent agent 88K Sep 24 23:30 /app/solution/train_current_model.py\n-rw-r--r-- 1 agent agent 16K Sep 24 22:49 /app/solution/two_tower_model.py\n", "exit_code": 0, "duration": { "secs": 6, "nanos": 10173511 }, "formatted_output": "{'file': '/app/solution/submission.parquet', 'regular': True, 'rows': 845600, 'users': 8456, 'rows_per_user': [100, 100], 'distinct_items_per_user': [100, 100], 'ranks': '1..100 exactly', 'all_test_eligible': True, 'dtypes': {'uid': 'int64', 'item_id': 'int64', 'rank': 'int64'}}\n{\n \"model\": \"/app/solution/improved_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.021804,\n \"quality_users\": 4263\n }\n}\n-rw-r--r-- 1 agent agent 1.4M Sep 24 23:41 /app/solution/improved_validation.parquet\n-rw-r--r-- 1 agent agent 1.2M Sep 24 23:41 /app/solution/submission.parquet\n-rw-r--r-- 1 agent agent 88K Sep 24 23:30 /app/solution/train_current_model.py\n-rw-r--r-- 1 agent agent 16K Sep 24 22:49 /app/solution/two_tower_model.py\n" } } ## Step 144 · agent · 2026-09-24T23:42:06.256Z ### Thinking unavailable — no text recorded ## Step 145 · agent · 2026-09-24T23:42:07.507Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T23:42:11.940Z { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python - <<'PY'\\nimport numpy as np,torch,pandas as pd\\nz=np.load('/app/solution/baseline_vectors.npz'); q=torch.from_numpy(z['queries']); it=torch.from_numpy(z['items']); it=it/torch.linalg.vector_norm(it,dim=1).clamp_min(1e-8)[:,None].pow(.2); s=q[:256]@it.T\\nprint('score',torch.quantile(s,torch.tensor([0.,.5,.9,.99,.999,1.])).tolist(),'top gap',float((s.topk(10).values[:,0]-s.topk(10).values[:,-1]).mean()))\\ncat=pd.read_parquet('/task/data/candidate_catalog.parquet').sort_values('popularity_rank'); x=np.log1p(cat.positive_listens.to_numpy()); x=(x-x.mean())/x.std(); print('pop std/range',x.min(),x.max())\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":3000}); text(r.output);" } ### Tool output · 2026-09-24T23:42:16.942Z Script completed Wall time 5.0 seconds Output: score [-14.482805252075195, -0.6552485823631287, 0.5865072011947632, 1.7500776052474976, 3.046552896499634, 9.193795204162598] top gap 0.4108607769012451 pop std/range -1.2107630061463721 5.128893248160918 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "session_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "root_turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "response_id": "resp_08539f19231b9e9f016ab5b543f80c87d28f14dddc2701cf51", "usage": { "input_tokens": 104025, "cached_input_tokens": 102400, "cache_write_input_tokens": 0, "output_tokens": 831, "reasoning_output_tokens": 572, "total_tokens": 104856 }, "turn_token_usage": { "input_tokens": 10993934, "cached_input_tokens": 10875904, "cache_write_input_tokens": 0, "output_tokens": 32037, "reasoning_output_tokens": 11827, "total_tokens": 11025971 }, "thread_token_usage": { "input_tokens": 10993934, "cached_input_tokens": 10875904, "cache_write_input_tokens": 0, "output_tokens": 32037, "reasoning_output_tokens": 11827, "total_tokens": 11025971 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-ff5e0f83-d19f-43b4-a7a0-53a669e3009b", "process_id": "71815", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\nimport numpy as np,torch,pandas as pd\nz=np.load('/app/solution/baseline_vectors.npz'); q=torch.from_numpy(z['queries']); it=torch.from_numpy(z['items']); it=it/torch.linalg.vector_norm(it,dim=1).clamp_min(1e-8)[:,None].pow(.2); s=q[:256]@it.T\nprint('score',torch.quantile(s,torch.tensor([0.,.5,.9,.99,.999,1.])).tolist(),'top gap',float((s.topk(10).values[:,0]-s.topk(10).values[:,-1]).mean()))\ncat=pd.read_parquet('/task/data/candidate_catalog.parquet').sort_values('popularity_rank'); x=np.log1p(cat.positive_listens.to_numpy()); x=(x-x.mean())/x.std(); print('pop std/range',x.min(),x.max())\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport numpy as np,torch,pandas as pd\nz=np.load('/app/solution/baseline_vectors.npz'); q=torch.from_numpy(z['queries']); it=torch.from_numpy(z['items']); it=it/torch.linalg.vector_norm(it,dim=1).clamp_min(1e-8)[:,None].pow(.2); s=q[:256]@it.T\nprint('score',torch.quantile(s,torch.tensor([0.,.5,.9,.99,.999,1.])).tolist(),'top gap',float((s.topk(10).values[:,0]-s.topk(10).values[:,-1]).mean()))\ncat=pd.read_parquet('/task/data/candidate_catalog.parquet').sort_values('popularity_rank'); x=np.log1p(cat.positive_listens.to_numpy()); x=(x-x.mean())/x.std(); print('pop std/range',x.min(),x.max())\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "score [-14.482805252075195, -0.6552485823631287, 0.5865072011947632, 1.7500776052474976, 3.046552896499634, 9.193795204162598] top gap 0.4108607769012451\npop std/range -1.2107630061463721 5.128893248160918\n", "stderr": "", "aggregated_output": "score [-14.482805252075195, -0.6552485823631287, 0.5865072011947632, 1.7500776052474976, 3.046552896499634, 9.193795204162598] top gap 0.4108607769012451\npop std/range -1.2107630061463721 5.128893248160918\n", "exit_code": 0, "duration": { "secs": 4, "nanos": 813004638 }, "formatted_output": "score [-14.482805252075195, -0.6552485823631287, 0.5865072011947632, 1.7500776052474976, 3.046552896499634, 9.193795204162598] top gap 0.4108607769012451\npop std/range -1.2107630061463721 5.128893248160918\n" } } ## Step 146 · agent · 2026-09-24T23:42:20.275Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T23:42:29.220Z { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python - <<'PY'\\nimport numpy as np,pandas as pd,torch\\nS='/app/solution'; z=np.load(f'{S}/baseline_vectors.npz'); q=torch.from_numpy(z['queries']); it=torch.from_numpy(z['items']); it=it/torch.linalg.vector_norm(it,dim=1).clamp_min(1e-8)[:,None].pow(.2)\\ncat=pd.read_parquet('/task/data/candidate_catalog.parquet').sort_values('popularity_rank'); x=np.log1p(cat.positive_listens.to_numpy()); bias=torch.from_numpy(((x-x.mean())/x.std()).astype(np.float32))\\nwith np.load('/task/data/validation_eligible_candidates.npz') as a: ids=a['item_ids'].copy(); uids=a['uids'].copy(); packed=a['packed_eligible'].copy()\\nfor beta in [-.08,-.04,-.02,.02,.04,.08]:\\n ou=np.repeat(uids.astype(np.int64),100); oi=np.empty(len(uids)*100,np.int64); rr=np.tile(np.arange(1,101,dtype=np.int64),len(uids))\\n for start in range(0,len(uids),64):\\n end=min(start+64,len(uids)); scores=q[start:end]@it.T + beta*bias; eligible=np.unpackbits(packed[start:end],axis=1,count=len(ids),bitorder='little').astype(bool); scores.masked_fill_(~torch.from_numpy(eligible),-torch.inf); top=torch.topk(scores,100,dim=1).indices.numpy(); oi[start*100:end*100]=ids[top].reshape(-1)\\n tag=('m' if beta<0 else 'p')+str(abs(int(beta*100))).zfill(2); pd.DataFrame({'uid':ou,'item_id':oi,'rank':rr}).to_parquet(f'{S}/bias_{tag}_validation.parquet',index=False)\\nPY\\nfor f in /app/solution/bias_*_validation.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \\\"$f\\\"; done\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":12000}); text(JSON.stringify(r));" } ### Tool output · 2026-09-24T23:42:30.388Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"c37656","wall_time_seconds":1.001280961,"session_id":16494,"original_token_count":0,"output":""} ### Harness context { "type": "token_usage_record", "thread_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "session_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "root_turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "response_id": "resp_08539f19231b9e9f016ab5b5592c8087d2a1714a88cd3239e0", "usage": { "input_tokens": 104971, "cached_input_tokens": 103808, "cache_write_input_tokens": 0, "output_tokens": 606, "reasoning_output_tokens": 99, "total_tokens": 105577 }, "turn_token_usage": { "input_tokens": 11098905, "cached_input_tokens": 10979712, "cache_write_input_tokens": 0, "output_tokens": 32643, "reasoning_output_tokens": 11926, "total_tokens": 11131548 }, "thread_token_usage": { "input_tokens": 11098905, "cached_input_tokens": 10979712, "cache_write_input_tokens": 0, "output_tokens": 32643, "reasoning_output_tokens": 11926, "total_tokens": 11131548 } } ## Step 147 · agent · 2026-09-24T23:42:32.224Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T23:42:32.783Z { "code": "const r = await tools.write_stdin({\"session_id\":16494,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":12000}); text(r.output);\n" } ### Tool output · 2026-09-24T23:42:59.391Z Script completed Wall time 26.6 seconds Output: { "model": "/app/solution/bias_m02_validation.parquet", "scores": { "quality_ndcg@10": 0.022144, "quality_users": 4263 } } { "model": "/app/solution/bias_m04_validation.parquet", "scores": { "quality_ndcg@10": 0.022466, "quality_users": 4263 } } { "model": "/app/solution/bias_m08_validation.parquet", "scores": { "quality_ndcg@10": 0.023608, "quality_users": 4263 } } { "model": "/app/solution/bias_p02_validation.parquet", "scores": { "quality_ndcg@10": 0.021323, "quality_users": 4263 } } { "model": "/app/solution/bias_p04_validation.parquet", "scores": { "quality_ndcg@10": 0.020654, "quality_users": 4263 } } { "model": "/app/solution/bias_p08_validation.parquet", "scores": { "quality_ndcg@10": 0.019654, "quality_users": 4263 } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "session_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "root_turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "response_id": "resp_08539f19231b9e9f016ab5b566a1e887d2bb70493c6c82140e", "usage": { "input_tokens": 105634, "cached_input_tokens": 104832, "cache_write_input_tokens": 0, "output_tokens": 55, "reasoning_output_tokens": 8, "total_tokens": 105689 }, "turn_token_usage": { "input_tokens": 11204539, "cached_input_tokens": 11084544, "cache_write_input_tokens": 0, "output_tokens": 32698, "reasoning_output_tokens": 11934, "total_tokens": 11237237 }, "thread_token_usage": { "input_tokens": 11204539, "cached_input_tokens": 11084544, "cache_write_input_tokens": 0, "output_tokens": 32698, "reasoning_output_tokens": 11934, "total_tokens": 11237237 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-178a3669-9e8a-4fe2-aa13-2eea8543ca1e", "process_id": "16494", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\nimport numpy as np,pandas as pd,torch\nS='/app/solution'; z=np.load(f'{S}/baseline_vectors.npz'); q=torch.from_numpy(z['queries']); it=torch.from_numpy(z['items']); it=it/torch.linalg.vector_norm(it,dim=1).clamp_min(1e-8)[:,None].pow(.2)\ncat=pd.read_parquet('/task/data/candidate_catalog.parquet').sort_values('popularity_rank'); x=np.log1p(cat.positive_listens.to_numpy()); bias=torch.from_numpy(((x-x.mean())/x.std()).astype(np.float32))\nwith np.load('/task/data/validation_eligible_candidates.npz') as a: ids=a['item_ids'].copy(); uids=a['uids'].copy(); packed=a['packed_eligible'].copy()\nfor beta in [-.08,-.04,-.02,.02,.04,.08]:\n ou=np.repeat(uids.astype(np.int64),100); oi=np.empty(len(uids)*100,np.int64); rr=np.tile(np.arange(1,101,dtype=np.int64),len(uids))\n for start in range(0,len(uids),64):\n end=min(start+64,len(uids)); scores=q[start:end]@it.T + beta*bias; eligible=np.unpackbits(packed[start:end],axis=1,count=len(ids),bitorder='little').astype(bool); scores.masked_fill_(~torch.from_numpy(eligible),-torch.inf); top=torch.topk(scores,100,dim=1).indices.numpy(); oi[start*100:end*100]=ids[top].reshape(-1)\n tag=('m' if beta<0 else 'p')+str(abs(int(beta*100))).zfill(2); pd.DataFrame({'uid':ou,'item_id':oi,'rank':rr}).to_parquet(f'{S}/bias_{tag}_validation.parquet',index=False)\nPY\nfor f in /app/solution/bias_*_validation.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \"$f\"; done" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport numpy as np,pandas as pd,torch\nS='/app/solution'; z=np.load(f'{S}/baseline_vectors.npz'); q=torch.from_numpy(z['queries']); it=torch.from_numpy(z['items']); it=it/torch.linalg.vector_norm(it,dim=1).clamp_min(1e-8)[:,None].pow(.2)\ncat=pd.read_parquet('/task/data/candidate_catalog.parquet').sort_values('popularity_rank'); x=np.log1p(cat.positive_listens.to_numpy()); bias=torch.from_numpy(((x-x.mean())/x.std()).astype(np.float32))\nwith np.load('/task/data/validation_eligible_candidates.npz') as a: ids=a['item_ids'].copy(); uids=a['uids'].copy(); packed=a['packed_eligible'].copy()\nfor beta in [-.08,-.04,-.02,.02,.04,.08]:\n ou=np.repeat(uids.astype(np.int64),100); oi=np.empty(len(uids)*100,np.int64); rr=np.tile(np.arange(1,101,dtype=np.int64),len(uids))\n for start in range(0,len(uids),64):\n end=min(start+64,len(uids)); scores=q[start:end]@it.T + beta*bias; eligible=np.unpackbits(packed[start:end],axis=1,count=len(ids),bitorder='little').astype(bool); scores.masked_fill_(~torch.from_numpy(eligible),-torch.inf); top=torch.topk(scores,100,dim=1).indices.numpy(); oi[start*100:end*100]=ids[top].reshape(-1)\n tag=('m' if beta<0 else 'p')+str(abs(int(beta*100))).zfill(2); pd.DataFrame({'uid':ou,'item_id':oi,'rank':rr}).to_parquet(f'{S}/bias_{tag}_validation.parquet',index=False)\nPY\nfor f in /app/solution/bias_*_validation.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \"$f\"; done" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{\n \"model\": \"/app/solution/bias_m02_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.022144,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/bias_m04_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.022466,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/bias_m08_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.023608,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/bias_p02_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.021323,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/bias_p04_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.020654,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/bias_p08_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.019654,\n \"quality_users\": 4263\n }\n}\n", "stderr": "", "aggregated_output": "{\n \"model\": \"/app/solution/bias_m02_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.022144,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/bias_m04_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.022466,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/bias_m08_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.023608,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/bias_p02_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.021323,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/bias_p04_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.020654,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/bias_p08_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.019654,\n \"quality_users\": 4263\n }\n}\n", "exit_code": 0, "duration": { "secs": 30, "nanos": 2828864 }, "formatted_output": "{\n \"model\": \"/app/solution/bias_m02_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.022144,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/bias_m04_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.022466,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/bias_m08_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.023608,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/bias_p02_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.021323,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/bias_p04_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.020654,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/bias_p08_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.019654,\n \"quality_users\": 4263\n }\n}\n" } } ## Step 148 · agent · 2026-09-24T23:43:03.212Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T23:43:12.597Z { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python - <<'PY'\\nimport numpy as np,pandas as pd,torch\\nS='/app/solution'; z=np.load(f'{S}/baseline_vectors.npz'); q=torch.from_numpy(z['queries']); it=torch.from_numpy(z['items']); it=it/torch.linalg.vector_norm(it,dim=1).clamp_min(1e-8)[:,None].pow(.2); cat=pd.read_parquet('/task/data/candidate_catalog.parquet').sort_values('popularity_rank'); x=np.log1p(cat.positive_listens.to_numpy()); bias=torch.from_numpy(((x-x.mean())/x.std()).astype(np.float32))\\nwith np.load('/task/data/validation_eligible_candidates.npz') as a: ids=a['item_ids'].copy(); uids=a['uids'].copy(); packed=a['packed_eligible'].copy()\\nfor beta in [-.12,-.16,-.20,-.25,-.30]:\\n ou=np.repeat(uids.astype(np.int64),100); oi=np.empty(len(uids)*100,np.int64); rr=np.tile(np.arange(1,101,dtype=np.int64),len(uids))\\n for start in range(0,len(uids),64):\\n end=min(start+64,len(uids)); scores=q[start:end]@it.T+beta*bias; e=np.unpackbits(packed[start:end],axis=1,count=len(ids),bitorder='little').astype(bool); scores.masked_fill_(~torch.from_numpy(e),-torch.inf); top=torch.topk(scores,100,dim=1).indices.numpy(); oi[start*100:end*100]=ids[top].reshape(-1)\\n tag=str(abs(int(beta*100))).zfill(2); pd.DataFrame({'uid':ou,'item_id':oi,'rank':rr}).to_parquet(f'{S}/bias_m{tag}_validation.parquet',index=False)\\nPY\\nfor f in /app/solution/bias_m1*_validation.parquet /app/solution/bias_m20_validation.parquet /app/solution/bias_m25_validation.parquet /app/solution/bias_m30_validation.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \\\"$f\\\"; done\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":12000}); text(JSON.stringify(r));" } ### Tool output · 2026-09-24T23:43:13.767Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"089ef1","wall_time_seconds":1.000858239,"session_id":72195,"original_token_count":0,"output":""} ### Harness context { "type": "token_usage_record", "thread_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "session_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "root_turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "response_id": "resp_08539f19231b9e9f016ab5b583926887d2b812f1cd9d9c1b2f", "usage": { "input_tokens": 106000, "cached_input_tokens": 105472, "cache_write_input_tokens": 0, "output_tokens": 675, "reasoning_output_tokens": 147, "total_tokens": 106675 }, "turn_token_usage": { "input_tokens": 11310539, "cached_input_tokens": 11190016, "cache_write_input_tokens": 0, "output_tokens": 33373, "reasoning_output_tokens": 12081, "total_tokens": 11343912 }, "thread_token_usage": { "input_tokens": 11310539, "cached_input_tokens": 11190016, "cache_write_input_tokens": 0, "output_tokens": 33373, "reasoning_output_tokens": 12081, "total_tokens": 11343912 } } ## Step 149 · agent · 2026-09-24T23:43:15.215Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T23:43:15.956Z { "code": "const r = await tools.write_stdin({\"session_id\":72195,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":12000}); text(r.output);\n" } ### Tool output · 2026-09-24T23:43:38.827Z Script completed Wall time 22.9 seconds Output: { "model": "/app/solution/bias_m12_validation.parquet", "scores": { "quality_ndcg@10": 0.023895, "quality_users": 4263 } } { "model": "/app/solution/bias_m16_validation.parquet", "scores": { "quality_ndcg@10": 0.023346, "quality_users": 4263 } } { "model": "/app/solution/bias_m20_validation.parquet", "scores": { "quality_ndcg@10": 0.022921, "quality_users": 4263 } } { "model": "/app/solution/bias_m25_validation.parquet", "scores": { "quality_ndcg@10": 0.022104, "quality_users": 4263 } } { "model": "/app/solution/bias_m30_validation.parquet", "scores": { "quality_ndcg@10": 0.0214, "quality_users": 4263 } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "session_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "root_turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "response_id": "resp_08539f19231b9e9f016ab5b591f93487d2a92f010e6a656e1d", "usage": { "input_tokens": 106732, "cached_input_tokens": 105856, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 106785 }, "turn_token_usage": { "input_tokens": 11417271, "cached_input_tokens": 11295872, "cache_write_input_tokens": 0, "output_tokens": 33426, "reasoning_output_tokens": 12087, "total_tokens": 11450697 }, "thread_token_usage": { "input_tokens": 11417271, "cached_input_tokens": 11295872, "cache_write_input_tokens": 0, "output_tokens": 33426, "reasoning_output_tokens": 12087, "total_tokens": 11450697 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-861ee3f7-6057-4320-b801-fb7b3dae4110", "process_id": "72195", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\nimport numpy as np,pandas as pd,torch\nS='/app/solution'; z=np.load(f'{S}/baseline_vectors.npz'); q=torch.from_numpy(z['queries']); it=torch.from_numpy(z['items']); it=it/torch.linalg.vector_norm(it,dim=1).clamp_min(1e-8)[:,None].pow(.2); cat=pd.read_parquet('/task/data/candidate_catalog.parquet').sort_values('popularity_rank'); x=np.log1p(cat.positive_listens.to_numpy()); bias=torch.from_numpy(((x-x.mean())/x.std()).astype(np.float32))\nwith np.load('/task/data/validation_eligible_candidates.npz') as a: ids=a['item_ids'].copy(); uids=a['uids'].copy(); packed=a['packed_eligible'].copy()\nfor beta in [-.12,-.16,-.20,-.25,-.30]:\n ou=np.repeat(uids.astype(np.int64),100); oi=np.empty(len(uids)*100,np.int64); rr=np.tile(np.arange(1,101,dtype=np.int64),len(uids))\n for start in range(0,len(uids),64):\n end=min(start+64,len(uids)); scores=q[start:end]@it.T+beta*bias; e=np.unpackbits(packed[start:end],axis=1,count=len(ids),bitorder='little').astype(bool); scores.masked_fill_(~torch.from_numpy(e),-torch.inf); top=torch.topk(scores,100,dim=1).indices.numpy(); oi[start*100:end*100]=ids[top].reshape(-1)\n tag=str(abs(int(beta*100))).zfill(2); pd.DataFrame({'uid':ou,'item_id':oi,'rank':rr}).to_parquet(f'{S}/bias_m{tag}_validation.parquet',index=False)\nPY\nfor f in /app/solution/bias_m1*_validation.parquet /app/solution/bias_m20_validation.parquet /app/solution/bias_m25_validation.parquet /app/solution/bias_m30_validation.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \"$f\"; done" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport numpy as np,pandas as pd,torch\nS='/app/solution'; z=np.load(f'{S}/baseline_vectors.npz'); q=torch.from_numpy(z['queries']); it=torch.from_numpy(z['items']); it=it/torch.linalg.vector_norm(it,dim=1).clamp_min(1e-8)[:,None].pow(.2); cat=pd.read_parquet('/task/data/candidate_catalog.parquet').sort_values('popularity_rank'); x=np.log1p(cat.positive_listens.to_numpy()); bias=torch.from_numpy(((x-x.mean())/x.std()).astype(np.float32))\nwith np.load('/task/data/validation_eligible_candidates.npz') as a: ids=a['item_ids'].copy(); uids=a['uids'].copy(); packed=a['packed_eligible'].copy()\nfor beta in [-.12,-.16,-.20,-.25,-.30]:\n ou=np.repeat(uids.astype(np.int64),100); oi=np.empty(len(uids)*100,np.int64); rr=np.tile(np.arange(1,101,dtype=np.int64),len(uids))\n for start in range(0,len(uids),64):\n end=min(start+64,len(uids)); scores=q[start:end]@it.T+beta*bias; e=np.unpackbits(packed[start:end],axis=1,count=len(ids),bitorder='little').astype(bool); scores.masked_fill_(~torch.from_numpy(e),-torch.inf); top=torch.topk(scores,100,dim=1).indices.numpy(); oi[start*100:end*100]=ids[top].reshape(-1)\n tag=str(abs(int(beta*100))).zfill(2); pd.DataFrame({'uid':ou,'item_id':oi,'rank':rr}).to_parquet(f'{S}/bias_m{tag}_validation.parquet',index=False)\nPY\nfor f in /app/solution/bias_m1*_validation.parquet /app/solution/bias_m20_validation.parquet /app/solution/bias_m25_validation.parquet /app/solution/bias_m30_validation.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \"$f\"; done" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{\n \"model\": \"/app/solution/bias_m12_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.023895,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/bias_m16_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.023346,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/bias_m20_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.022921,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/bias_m25_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.022104,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/bias_m30_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.0214,\n \"quality_users\": 4263\n }\n}\n", "stderr": "", "aggregated_output": "{\n \"model\": \"/app/solution/bias_m12_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.023895,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/bias_m16_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.023346,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/bias_m20_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.022921,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/bias_m25_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.022104,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/bias_m30_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.0214,\n \"quality_users\": 4263\n }\n}\n", "exit_code": 0, "duration": { "secs": 26, "nanos": 57003268 }, "formatted_output": "{\n \"model\": \"/app/solution/bias_m12_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.023895,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/bias_m16_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.023346,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/bias_m20_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.022921,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/bias_m25_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.022104,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/bias_m30_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.0214,\n \"quality_users\": 4263\n }\n}\n" } } ## Step 150 · agent · 2026-09-24T23:43:47.072Z ### Thinking unavailable — no text recorded A final calibration sweep found the stronger, still serving-safe correction: subtracting `0.12 × standardized log-popularity` from each item score raises public NDCG to `0.023895`—a 12.3% relative gain over production. This is representable as one extra constant query coordinate and one precomputed item coordinate, so it remains a single two-tower dot product. I’m applying it to the already-refit final checkpoint and revalidating the artifact. ### exec · 2026-09-24T23:43:55.431Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/train_current_model.py\\n@@\\n item_norm_power: float = 0.20\\n+ popularity_penalty: float = 0.12\\n@@\\n for start in range(0, len(eligibility.uids), ranking_batch_size):\\n end = min(start + ranking_batch_size, len(eligibility.uids))\\n scores = queries[start:end] @ items.T\\n+ if config.popularity_penalty:\\n+ popularity = torch.from_numpy(prepared.item_features[:, 0])\\n+ scores -= config.popularity_penalty * popularity[None, :]\\n@@\\n parser.add_argument(\\n \\\"--item-norm-power\\\", type=float, default=Config.item_norm_power\\n )\\n+ parser.add_argument(\\n+ \\\"--popularity-penalty\\\", type=float, default=Config.popularity_penalty\\n+ )\\n@@\\n or not 0.0 <= args.item_norm_power <= 1.0\\n+ or args.popularity_penalty < 0.0\\n@@\\n item_norm_power=args.item_norm_power,\\n+ popularity_penalty=args.popularity_penalty,\\n@@\\n- \\\"score\\\": \\\"raw-dot-product-with-precomputed-scaled-items\\\",\\n+ \\\"score\\\": (\\n+ \\\"augmented-dot-product-with-scaled-items-and-\\\"\\n+ f\\\"popularity-penalty-{config.popularity_penalty:g}\\\"\\n+ ),\\n*** End Patch\"; text(await tools.apply_patch(patch));" } ### Tool output · 2026-09-24T23:43:55.593Z Script completed Wall time 0.0 seconds Output: {} ### Harness context { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-61c00f54-efee-4166-839c-b7d115c8970f", "changes": { "/app/solution/train_current_model.py": { "type": "update", "unified_diff": "@@ -61,2 +61,3 @@\n item_norm_power: float = 0.20\n+ popularity_penalty: float = 0.12\n inbatch_correction_power: float = 0.5\n@@ -2099,2 +2100,5 @@\n scores = queries[start:end] @ items.T\n+ if config.popularity_penalty:\n+ popularity = torch.from_numpy(prepared.item_features[:, 0])\n+ scores -= config.popularity_penalty * popularity[None, :]\n eligible = np.unpackbits(\n@@ -2188,2 +2192,5 @@\n parser.add_argument(\n+ \"--popularity-penalty\", type=float, default=Config.popularity_penalty\n+ )\n+ parser.add_argument(\n \"--target-weighting\",\n@@ -2265,2 +2272,3 @@\n or not 0.0 <= args.item_norm_power <= 1.0\n+ or args.popularity_penalty < 0.0\n ):\n@@ -2285,2 +2293,3 @@\n item_norm_power=args.item_norm_power,\n+ popularity_penalty=args.popularity_penalty,\n negative_filter_scope=args.negative_filter_scope,\n@@ -2387,3 +2396,6 @@\n ),\n- \"score\": \"raw-dot-product-with-precomputed-scaled-items\",\n+ \"score\": (\n+ \"augmented-dot-product-with-scaled-items-and-\"\n+ f\"popularity-penalty-{config.popularity_penalty:g}\"\n+ ),\n \"objective\": (\n", "move_path": null } }, "status": "completed", "stdout": "Success. Updated the following files:\nM /app/solution/train_current_model.py\n", "stderr": "" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "session_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "root_turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "response_id": "resp_08539f19231b9e9f016ab5b5ab0f5487d2b9986906f23aecaa", "usage": { "input_tokens": 107048, "cached_input_tokens": 106496, "cache_write_input_tokens": 0, "output_tokens": 849, "reasoning_output_tokens": 383, "total_tokens": 107897 }, "turn_token_usage": { "input_tokens": 11524319, "cached_input_tokens": 11402368, "cache_write_input_tokens": 0, "output_tokens": 34275, "reasoning_output_tokens": 12470, "total_tokens": 11558594 }, "thread_token_usage": { "input_tokens": 11524319, "cached_input_tokens": 11402368, "cache_write_input_tokens": 0, "output_tokens": 34275, "reasoning_output_tokens": 12470, "total_tokens": 11558594 } } ## Step 151 · agent · 2026-09-24T23:43:58.891Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T23:44:06.357Z { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/rank_checkpoint.py\\n+#!/usr/bin/env python3\\n+\\\"\\\"\\\"Rank the final cutoff from a fitted checkpoint without retraining.\\\"\\\"\\\"\\n+from pathlib import Path\\n+\\n+import torch\\n+\\n+import train_current_model as trainer\\n+import two_tower_model as base\\n+\\n+\\n+HERE = Path(__file__).resolve().parent\\n+\\n+\\n+def main() -> None:\\n+ torch.set_num_threads(8)\\n+ torch.set_num_interop_threads(1)\\n+ config = trainer.Config()\\n+ prepared = trainer.load_cache(\\n+ HERE / \\\"submission_sequences.npz\\\", \\\"submission\\\", config\\n+ )\\n+ eligibility = trainer.CandidateSet.read(\\n+ Path(\\\"/task/data/test_eligible_candidates.npz\\\")\\n+ )\\n+ model = base.SequentialTwoTower(\\n+ prepared.item_features, trainer.model_config(config)\\n+ )\\n+ checkpoint = torch.load(\\n+ HERE / \\\"final_checkpoints/epoch-01.pt\\\",\\n+ map_location=\\\"cpu\\\",\\n+ weights_only=False,\\n+ )\\n+ model.load_state_dict(checkpoint[\\\"model_state\\\"])\\n+ ranking, diagnostics = trainer.rank_candidates(\\n+ model,\\n+ prepared,\\n+ eligibility,\\n+ config,\\n+ torch.device(\\\"cpu\\\"),\\n+ 64,\\n+ )\\n+ trainer.write_ranking(ranking, HERE / \\\"submission.parquet\\\")\\n+ print({\\\"rows\\\": len(ranking), \\\"users\\\": ranking.uid.nunique(), **diagnostics})\\n+\\n+\\n+if __name__ == \\\"__main__\\\":\\n+ main()\\n*** End Patch\"; text(await tools.apply_patch(patch));" } ### Tool output · 2026-09-24T23:44:06.520Z Script completed Wall time 0.0 seconds Output: {} ### Harness context { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-316c0ba0-84c2-4a9e-82ae-c6294089f094", "changes": { "/app/solution/rank_checkpoint.py": { "type": "add", "content": "#!/usr/bin/env python3\n\"\"\"Rank the final cutoff from a fitted checkpoint without retraining.\"\"\"\nfrom pathlib import Path\n\nimport torch\n\nimport train_current_model as trainer\nimport two_tower_model as base\n\n\nHERE = Path(__file__).resolve().parent\n\n\ndef main() -> None:\n torch.set_num_threads(8)\n torch.set_num_interop_threads(1)\n config = trainer.Config()\n prepared = trainer.load_cache(\n HERE / \"submission_sequences.npz\", \"submission\", config\n )\n eligibility = trainer.CandidateSet.read(\n Path(\"/task/data/test_eligible_candidates.npz\")\n )\n model = base.SequentialTwoTower(\n prepared.item_features, trainer.model_config(config)\n )\n checkpoint = torch.load(\n HERE / \"final_checkpoints/epoch-01.pt\",\n map_location=\"cpu\",\n weights_only=False,\n )\n model.load_state_dict(checkpoint[\"model_state\"])\n ranking, diagnostics = trainer.rank_candidates(\n model,\n prepared,\n eligibility,\n config,\n torch.device(\"cpu\"),\n 64,\n )\n trainer.write_ranking(ranking, HERE / \"submission.parquet\")\n print({\"rows\": len(ranking), \"users\": ranking.uid.nunique(), **diagnostics})\n\n\nif __name__ == \"__main__\":\n main()\n" } }, "status": "completed", "stdout": "Success. Updated the following files:\nA /app/solution/rank_checkpoint.py\n", "stderr": "" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "session_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "root_turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "response_id": "resp_08539f19231b9e9f016ab5b5bc0c7c87d2bd9cedffd2bc4a26", "usage": { "input_tokens": 107921, "cached_input_tokens": 106880, "cache_write_input_tokens": 0, "output_tokens": 493, "reasoning_output_tokens": 71, "total_tokens": 108414 }, "turn_token_usage": { "input_tokens": 11632240, "cached_input_tokens": 11509248, "cache_write_input_tokens": 0, "output_tokens": 34768, "reasoning_output_tokens": 12541, "total_tokens": 11667008 }, "thread_token_usage": { "input_tokens": 11632240, "cached_input_tokens": 11509248, "cache_write_input_tokens": 0, "output_tokens": 34768, "reasoning_output_tokens": 12541, "total_tokens": 11667008 } } ## Step 152 · agent · 2026-09-24T23:44:08.458Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T23:44:10.329Z { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python -m py_compile /app/solution/train_current_model.py /app/solution/rank_checkpoint.py && /opt/task-venv/bin/python /app/solution/rank_checkpoint.py 2>&1 | tee /app/solution/final_rerank.log\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":10000}); text(JSON.stringify(r));" } ### Tool output · 2026-09-24T23:44:11.498Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"063772","wall_time_seconds":1.001712177,"session_id":90692,"original_token_count":0,"output":""} ### Harness context { "type": "token_usage_record", "thread_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "session_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "root_turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "response_id": "resp_08539f19231b9e9f016ab5b5c6fb4887d28589d6d08c70c4c6", "usage": { "input_tokens": 108438, "cached_input_tokens": 107776, "cache_write_input_tokens": 0, "output_tokens": 118, "reasoning_output_tokens": 9, "total_tokens": 108556 }, "turn_token_usage": { "input_tokens": 11740678, "cached_input_tokens": 11617024, "cache_write_input_tokens": 0, "output_tokens": 34886, "reasoning_output_tokens": 12550, "total_tokens": 11775564 }, "thread_token_usage": { "input_tokens": 11740678, "cached_input_tokens": 11617024, "cache_write_input_tokens": 0, "output_tokens": 34886, "reasoning_output_tokens": 12550, "total_tokens": 11775564 } } ## Step 153 · agent · 2026-09-24T23:44:17.086Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T23:44:17.807Z { "code": "const r = await tools.write_stdin({\"session_id\":90692,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000}); text(r.output);\n" } ### Tool output · 2026-09-24T23:44:47.835Z Script completed Wall time 30.0 seconds Output: loaded full-sequence cache: tokens=20,712,971 targets=20,703,818 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "session_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "root_turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "response_id": "resp_08539f19231b9e9f016ab5b5ce207887d29abc8ba6f9941b6b", "usage": { "input_tokens": 108612, "cached_input_tokens": 108288, "cache_write_input_tokens": 0, "output_tokens": 55, "reasoning_output_tokens": 8, "total_tokens": 108667 }, "turn_token_usage": { "input_tokens": 11849290, "cached_input_tokens": 11725312, "cache_write_input_tokens": 0, "output_tokens": 34941, "reasoning_output_tokens": 12558, "total_tokens": 11884231 }, "thread_token_usage": { "input_tokens": 11849290, "cached_input_tokens": 11725312, "cache_write_input_tokens": 0, "output_tokens": 34941, "reasoning_output_tokens": 12558, "total_tokens": 11884231 } } ## Step 154 · agent · 2026-09-24T23:44:49.342Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T23:44:50.072Z { "code": "const r = await tools.write_stdin({\"session_id\":90692,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000}); text(r.output);\n" } ### Tool output · 2026-09-24T23:45:20.093Z Script completed Wall time 30.0 seconds Output: ### Harness context { "type": "token_usage_record", "thread_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "session_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "root_turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "response_id": "resp_08539f19231b9e9f016ab5b5f022c487d2b25ef25c649cdb79", "usage": { "input_tokens": 108711, "cached_input_tokens": 108416, "cache_write_input_tokens": 0, "output_tokens": 55, "reasoning_output_tokens": 8, "total_tokens": 108766 }, "turn_token_usage": { "input_tokens": 11958001, "cached_input_tokens": 11833728, "cache_write_input_tokens": 0, "output_tokens": 34996, "reasoning_output_tokens": 12566, "total_tokens": 11992997 }, "thread_token_usage": { "input_tokens": 11958001, "cached_input_tokens": 11833728, "cache_write_input_tokens": 0, "output_tokens": 34996, "reasoning_output_tokens": 12566, "total_tokens": 11992997 } } ### exec · 2026-09-24T23:45:23.205Z { "code": "const r = await tools.write_stdin({\"session_id\":90692,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000}); text(r.output);\n" } ### Tool output · 2026-09-24T23:45:37.352Z Script completed Wall time 14.1 seconds Output: ranked users=1,024/8,456 ranked users=2,048/8,456 ranked users=3,072/8,456 ranked users=4,096/8,456 ranked users=5,120/8,456 ranked users=6,144/8,456 ranked users=7,168/8,456 ranked users=8,192/8,456 ranked users=8,456/8,456 {'rows': 845600, 'users': 8456, 'query_norm_mean': 1.6518515348434448, 'query_norm_std': 0.8145266175270081, 'item_norm_mean': 1.6446387767791748, 'item_norm_std': 0.6429429650306702} ### Harness context { "type": "token_usage_record", "thread_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "session_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "root_turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "response_id": "resp_08539f19231b9e9f016ab5b61055a487d2be0d824e67c24eb7", "usage": { "input_tokens": 108789, "cached_input_tokens": 108544, "cache_write_input_tokens": 0, "output_tokens": 45, "reasoning_output_tokens": 0, "total_tokens": 108834 }, "turn_token_usage": { "input_tokens": 12066790, "cached_input_tokens": 11942272, "cache_write_input_tokens": 0, "output_tokens": 35041, "reasoning_output_tokens": 12566, "total_tokens": 12101831 }, "thread_token_usage": { "input_tokens": 12066790, "cached_input_tokens": 11942272, "cache_write_input_tokens": 0, "output_tokens": 35041, "reasoning_output_tokens": 12566, "total_tokens": 12101831 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-1cb07f4e-13b4-4709-9250-b47593296ab3", "process_id": "90692", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python -m py_compile /app/solution/train_current_model.py /app/solution/rank_checkpoint.py && /opt/task-venv/bin/python /app/solution/rank_checkpoint.py 2>&1 | tee /app/solution/final_rerank.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python -m py_compile /app/solution/train_current_model.py /app/solution/rank_checkpoint.py && /opt/task-venv/bin/python /app/solution/rank_checkpoint.py 2>&1 | tee /app/solution/final_rerank.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "loaded full-sequence cache: tokens=20,712,971 targets=20,703,818\nranked users=1,024/8,456\nranked users=2,048/8,456\nranked users=3,072/8,456\nranked users=4,096/8,456\nranked users=5,120/8,456\nranked users=6,144/8,456\nranked users=7,168/8,456\nranked users=8,192/8,456\nranked users=8,456/8,456\n{'rows': 845600, 'users': 8456, 'query_norm_mean': 1.6518515348434448, 'query_norm_std': 0.8145266175270081, 'item_norm_mean': 1.6446387767791748, 'item_norm_std': 0.6429429650306702}\n", "stderr": "", "aggregated_output": "loaded full-sequence cache: tokens=20,712,971 targets=20,703,818\nranked users=1,024/8,456\nranked users=2,048/8,456\nranked users=3,072/8,456\nranked users=4,096/8,456\nranked users=5,120/8,456\nranked users=6,144/8,456\nranked users=7,168/8,456\nranked users=8,192/8,456\nranked users=8,456/8,456\n{'rows': 845600, 'users': 8456, 'query_norm_mean': 1.6518515348434448, 'query_norm_std': 0.8145266175270081, 'item_norm_mean': 1.6446387767791748, 'item_norm_std': 0.6429429650306702}\n", "exit_code": 0, "duration": { "secs": 86, "nanos": 854308146 }, "formatted_output": "loaded full-sequence cache: tokens=20,712,971 targets=20,703,818\nranked users=1,024/8,456\nranked users=2,048/8,456\nranked users=3,072/8,456\nranked users=4,096/8,456\nranked users=5,120/8,456\nranked users=6,144/8,456\nranked users=7,168/8,456\nranked users=8,192/8,456\nranked users=8,456/8,456\n{'rows': 845600, 'users': 8456, 'query_norm_mean': 1.6518515348434448, 'query_norm_std': 0.8145266175270081, 'item_norm_mean': 1.6446387767791748, 'item_norm_std': 0.6429429650306702}\n" } } ## Step 155 · agent · 2026-09-24T23:45:40.026Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T23:45:41.547Z { "code": "const r = await tools.exec_command({\"cmd\":\"sed -n '1,28p' /app/solution/train_current_model.py; stat -c '%F %s %n' /app/solution/submission.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":3000}); text(r.output);" } ### Tool output · 2026-09-24T23:45:41.706Z Script completed Wall time 0.0 seconds Output: #!/usr/bin/env python3 """Train the Yambda windowed two-tower baseline. Every catalog listen with at least 50 percent completion is a sequence token. Within each selected chronological window, every adjacent target contributes a loss. The fast default retains each user's most recent 128 training targets and 16 held-out validation targets; passing zero for either cap restores the uncapped behavior for that side of the split. A 100-step truncated-BPTT block computes all of its timestep losses and carries the detached GRU hidden state into the next block. The model is a pure two-tower retriever: no user-ID embedding, no output normalization, raw dot-product retrieval, sample-weighted binary cross entropy, and 24 uniform plus eight propensity-corrected unique in-batch negatives. Every selected adjacent target retains a nonzero weight. Only files under the supplied public directory are read. """ from __future__ import annotations import argparse import json import random import sys import time from dataclasses import asdict, dataclass from pathlib import Path import numpy as np regular file 1233323 /app/solution/submission.parquet ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-5c1f10a0-597a-4278-9573-ac883df56551", "process_id": "30563", "command": [ "/bin/sh", "-lc", "sed -n '1,28p' /app/solution/train_current_model.py; stat -c '%F %s %n' /app/solution/submission.parquet" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "sed -n '1,28p' /app/solution/train_current_model.py; stat -c '%F %s %n' /app/solution/submission.parquet" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "#!/usr/bin/env python3\n\"\"\"Train the Yambda windowed two-tower baseline.\n\nEvery catalog listen with at least 50 percent completion is a sequence token.\nWithin each selected chronological window, every adjacent target contributes a\nloss. The fast default retains each user's most recent 128 training targets and\n16 held-out validation targets; passing zero for either cap restores the\nuncapped behavior for that side of the split. A 100-step truncated-BPTT block\ncomputes all of its timestep losses and carries the detached GRU hidden state\ninto the next block.\n\nThe model is a pure two-tower retriever: no user-ID embedding, no output\nnormalization, raw dot-product retrieval, sample-weighted binary cross entropy,\nand 24 uniform plus eight propensity-corrected unique in-batch negatives. Every\nselected adjacent target retains a nonzero weight. Only files under the supplied\npublic directory are read.\n\"\"\"\nfrom __future__ import annotations\n\nimport argparse\nimport json\nimport random\nimport sys\nimport time\nfrom dataclasses import asdict, dataclass\nfrom pathlib import Path\n\nimport numpy as np\nregular file 1233323 /app/solution/submission.parquet\n", "stderr": "", "aggregated_output": "#!/usr/bin/env python3\n\"\"\"Train the Yambda windowed two-tower baseline.\n\nEvery catalog listen with at least 50 percent completion is a sequence token.\nWithin each selected chronological window, every adjacent target contributes a\nloss. The fast default retains each user's most recent 128 training targets and\n16 held-out validation targets; passing zero for either cap restores the\nuncapped behavior for that side of the split. A 100-step truncated-BPTT block\ncomputes all of its timestep losses and carries the detached GRU hidden state\ninto the next block.\n\nThe model is a pure two-tower retriever: no user-ID embedding, no output\nnormalization, raw dot-product retrieval, sample-weighted binary cross entropy,\nand 24 uniform plus eight propensity-corrected unique in-batch negatives. Every\nselected adjacent target retains a nonzero weight. Only files under the supplied\npublic directory are read.\n\"\"\"\nfrom __future__ import annotations\n\nimport argparse\nimport json\nimport random\nimport sys\nimport time\nfrom dataclasses import asdict, dataclass\nfrom pathlib import Path\n\nimport numpy as np\nregular file 1233323 /app/solution/submission.parquet\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 3960 }, "formatted_output": "#!/usr/bin/env python3\n\"\"\"Train the Yambda windowed two-tower baseline.\n\nEvery catalog listen with at least 50 percent completion is a sequence token.\nWithin each selected chronological window, every adjacent target contributes a\nloss. The fast default retains each user's most recent 128 training targets and\n16 held-out validation targets; passing zero for either cap restores the\nuncapped behavior for that side of the split. A 100-step truncated-BPTT block\ncomputes all of its timestep losses and carries the detached GRU hidden state\ninto the next block.\n\nThe model is a pure two-tower retriever: no user-ID embedding, no output\nnormalization, raw dot-product retrieval, sample-weighted binary cross entropy,\nand 24 uniform plus eight propensity-corrected unique in-batch negatives. Every\nselected adjacent target retains a nonzero weight. Only files under the supplied\npublic directory are read.\n\"\"\"\nfrom __future__ import annotations\n\nimport argparse\nimport json\nimport random\nimport sys\nimport time\nfrom dataclasses import asdict, dataclass\nfrom pathlib import Path\n\nimport numpy as np\nregular file 1233323 /app/solution/submission.parquet\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "session_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "root_turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "response_id": "resp_08539f19231b9e9f016ab5b621c39087d2b8a5b79a2403fc33", "usage": { "input_tokens": 109039, "cached_input_tokens": 108672, "cache_write_input_tokens": 0, "output_tokens": 129, "reasoning_output_tokens": 46, "total_tokens": 109168 }, "turn_token_usage": { "input_tokens": 12175829, "cached_input_tokens": 12050944, "cache_write_input_tokens": 0, "output_tokens": 35170, "reasoning_output_tokens": 12612, "total_tokens": 12210999 }, "thread_token_usage": { "input_tokens": 12175829, "cached_input_tokens": 12050944, "cache_write_input_tokens": 0, "output_tokens": 35170, "reasoning_output_tokens": 12612, "total_tokens": 12210999 } } ## Step 156 · agent · 2026-09-24T23:45:51.905Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T23:46:08.755Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/train_current_model.py\\n@@\\n-\\\"\\\"\\\"Train the Yambda windowed two-tower baseline.\\n+\\\"\\\"\\\"Train and rank the calibrated Yambda windowed two-tower retriever.\\n@@\\n-The model is a pure two-tower retriever: no user-ID embedding, no output\\n-normalization, raw dot-product retrieval, sample-weighted binary cross entropy,\\n-and 24 uniform plus eight propensity-corrected unique in-batch negatives. Every\\n-selected adjacent target retains a nonzero weight. Only files under the supplied\\n-public directory are read.\\n+The model is a pure two-tower retriever: no user-ID embedding, sample-weighted\\n+binary cross entropy, and 24 uniform plus eight propensity-corrected unique\\n+in-batch negatives. Item vectors receive an offline norm/popularity calibration\\n+that is expressible as an augmented dot product. Every selected adjacent target\\n+retains a nonzero weight. Only files under the supplied public directory are\\n+read.\\n@@\\n- \\\"baseline\\\": \\\"yambda-windowed-sequence-two-tower-base\\\",\\n+ \\\"model\\\": \\\"yambda-windowed-sequence-two-tower-calibrated\\\",\\n*** Add File: /app/solution/README.md\\n+# Calibrated production two-tower\\n+\\n+## Result\\n+\\n+The production reproduction scores **0.021280 NDCG@10** on 4,263 public\\n+quality users. The selected model scores **0.023895**, an absolute gain of\\n+0.002615 and a relative gain of 12.3%.\\n+\\n+The final cutoff-correct artifact is `submission.parquet`. It was refit on\\n+`train_* + val_*`, uses the test preference snapshot and test eligibility\\n+service, and contains 845,600 rows (100 distinct eligible tracks for each of\\n+8,456 target users).\\n+\\n+## Diagnosis and selected change\\n+\\n+The baseline's 24 catalog-uniform negatives are mostly easy. Its eight\\n+in-batch negatives are more informative, but inverse-propensity weighting also\\n+protects frequent tracks. The resulting pointwise BCE model concentrates its\\n+top ten on a very small, high-norm popularity head (median popularity rank 111).\\n+\\n+I tested equal-weight in-batch negatives, bounded online hard mining, and a\\n+pairwise use of mined negatives. Their public scores were 0.018522, 0.009742,\\n+and 0.008895 respectively. The logs have no impression labels, so aggressive\\n+mining selects many plausible but unlabeled positives; pointwise hard-negative\\n+BCE also distorts item-vector magnitudes. Those variants were rejected.\\n+\\n+The selected conservative fix keeps the production sampler and false-negative\\n+filter, scales each item vector offline by `||v||^-0.20`, and subtracts 0.12\\n+times standardized log training popularity. The latter is an extra constant\\n+query/item coordinate, so the final score remains one batchable two-tower dot\\n+product with all item vectors precomputed. The public top-ten median popularity\\n+rank moves away from the over-concentrated head without changing chronology,\\n+features, candidates, or relevance boundaries.\\n+\\n+## Reproduce the final artifact\\n+\\n+```sh\\n+/opt/task-venv/bin/python /app/solution/train_current_model.py \\\\\\n+ --stage submission \\\\\\n+ --public-dir /task \\\\\\n+ --prepared-cache /app/solution/submission_sequences.npz \\\\\\n+ --checkpoint-dir /app/solution/final_checkpoints \\\\\\n+ --output /app/solution/submission.parquet\\n+```\\n+\\n+The selected calibration values are defaults in `train_current_model.py`.\\n*** End Patch\"; text(await tools.apply_patch(patch));" } ### Tool output · 2026-09-24T23:46:08.866Z Script completed Wall time 0.0 seconds Output: {} ### Harness context { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-449130d5-895f-46a7-8db0-443878825d1e", "changes": { "/app/solution/README.md": { "type": "add", "content": "# Calibrated production two-tower\n\n## Result\n\nThe production reproduction scores **0.021280 NDCG@10** on 4,263 public\nquality users. The selected model scores **0.023895**, an absolute gain of\n0.002615 and a relative gain of 12.3%.\n\nThe final cutoff-correct artifact is `submission.parquet`. It was refit on\n`train_* + val_*`, uses the test preference snapshot and test eligibility\nservice, and contains 845,600 rows (100 distinct eligible tracks for each of\n8,456 target users).\n\n## Diagnosis and selected change\n\nThe baseline's 24 catalog-uniform negatives are mostly easy. Its eight\nin-batch negatives are more informative, but inverse-propensity weighting also\nprotects frequent tracks. The resulting pointwise BCE model concentrates its\ntop ten on a very small, high-norm popularity head (median popularity rank 111).\n\nI tested equal-weight in-batch negatives, bounded online hard mining, and a\npairwise use of mined negatives. Their public scores were 0.018522, 0.009742,\nand 0.008895 respectively. The logs have no impression labels, so aggressive\nmining selects many plausible but unlabeled positives; pointwise hard-negative\nBCE also distorts item-vector magnitudes. Those variants were rejected.\n\nThe selected conservative fix keeps the production sampler and false-negative\nfilter, scales each item vector offline by `||v||^-0.20`, and subtracts 0.12\ntimes standardized log training popularity. The latter is an extra constant\nquery/item coordinate, so the final score remains one batchable two-tower dot\nproduct with all item vectors precomputed. The public top-ten median popularity\nrank moves away from the over-concentrated head without changing chronology,\nfeatures, candidates, or relevance boundaries.\n\n## Reproduce the final artifact\n\n```sh\n/opt/task-venv/bin/python /app/solution/train_current_model.py \\\n --stage submission \\\n --public-dir /task \\\n --prepared-cache /app/solution/submission_sequences.npz \\\n --checkpoint-dir /app/solution/final_checkpoints \\\n --output /app/solution/submission.parquet\n```\n\nThe selected calibration values are defaults in `train_current_model.py`.\n" }, "/app/solution/train_current_model.py": { "type": "update", "unified_diff": "@@ -1,3 +1,3 @@\n #!/usr/bin/env python3\n-\"\"\"Train the Yambda windowed two-tower baseline.\n+\"\"\"Train and rank the calibrated Yambda windowed two-tower retriever.\n \n@@ -11,7 +11,8 @@\n \n-The model is a pure two-tower retriever: no user-ID embedding, no output\n-normalization, raw dot-product retrieval, sample-weighted binary cross entropy,\n-and 24 uniform plus eight propensity-corrected unique in-batch negatives. Every\n-selected adjacent target retains a nonzero weight. Only files under the supplied\n-public directory are read.\n+The model is a pure two-tower retriever: no user-ID embedding, sample-weighted\n+binary cross entropy, and 24 uniform plus eight propensity-corrected unique\n+in-batch negatives. Item vectors receive an offline norm/popularity calibration\n+that is expressible as an augmented dot product. Every selected adjacent target\n+retains a nonzero weight. Only files under the supplied public directory are\n+read.\n \"\"\"\n@@ -2374,3 +2375,3 @@\n {\n- \"baseline\": \"yambda-windowed-sequence-two-tower-base\",\n+ \"model\": \"yambda-windowed-sequence-two-tower-calibrated\",\n \"stage\": args.stage,\n", "move_path": null } }, "status": "completed", "stdout": "Success. Updated the following files:\nA /app/solution/README.md\nM /app/solution/train_current_model.py\n", "stderr": "" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "session_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "root_turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "response_id": "resp_08539f19231b9e9f016ab5b6263e8087d289329a9ab6978c53", "usage": { "input_tokens": 109440, "cached_input_tokens": 108800, "cache_write_input_tokens": 0, "output_tokens": 1303, "reasoning_output_tokens": 445, "total_tokens": 110743 }, "turn_token_usage": { "input_tokens": 12285269, "cached_input_tokens": 12159744, "cache_write_input_tokens": 0, "output_tokens": 36473, "reasoning_output_tokens": 13057, "total_tokens": 12321742 }, "thread_token_usage": { "input_tokens": 12285269, "cached_input_tokens": 12159744, "cache_write_input_tokens": 0, "output_tokens": 36473, "reasoning_output_tokens": 13057, "total_tokens": 12321742 } } ## Step 157 · agent · 2026-09-24T23:46:14.049Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T23:46:23.125Z { "code": "const r = await tools.exec_command({\"cmd\":\"cp /app/solution/bias_m12_validation.parquet /app/solution/improved_validation.parquet\\n/opt/task-venv/bin/python - <<'PY'\\nfrom pathlib import Path\\nimport numpy as np,pandas as pd\\np=Path('/app/solution/submission.parquet'); assert p.is_file() and not p.is_symlink()\\nr=pd.read_parquet(p,columns=['uid','item_id','rank']); targets=pd.read_parquet('/task/data/target_users.parquet').uid.to_numpy(np.int64)\\nassert len(r)==len(targets)*100==845600 and set(r.uid)==set(targets)\\nassert all(pd.api.types.is_integer_dtype(r[c]) for c in r.columns)\\ng=r.groupby('uid',sort=False); assert (g.size().to_numpy()==100).all(); assert (g.item_id.nunique().to_numpy()==100).all(); assert (np.sort(r['rank'].to_numpy().reshape(-1,100),axis=1)==np.arange(1,101)).all()\\nwith np.load('/task/data/test_eligible_candidates.npz') as a: u=a['uids'].copy(); ids=a['item_ids'].copy(); packed=a['packed_eligible'].copy()\\nur=pd.Index(u).get_indexer(r.uid.to_numpy()); ic=pd.Index(ids).get_indexer(r.item_id.to_numpy()); assert (ur>=0).all() and (ic>=0).all(); ok=((packed[ur,ic>>3]>>(ic&7))&1).astype(bool); assert ok.all()\\nprint(f'VALID rows={len(r)} users={r.uid.nunique()} all_test_eligible={ok.all()} regular_file={p.is_file() and not p.is_symlink()}')\\nPY\\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/improved_validation.parquet\\n/opt/task-venv/bin/python -m py_compile /app/solution/train_current_model.py /app/solution/two_tower_model.py /app/solution/rank_checkpoint.py\\nsed -n '1,220p' /app/solution/README.md\\nls -lh /app/solution/submission.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":12000}); text(r.output);" } ### Tool output · 2026-09-24T23:46:28.532Z Script completed Wall time 5.4 seconds Output: VALID rows=845600 users=8456 all_test_eligible=True regular_file=True { "model": "/app/solution/improved_validation.parquet", "scores": { "quality_ndcg@10": 0.023895, "quality_users": 4263 } } # Calibrated production two-tower ## Result The production reproduction scores **0.021280 NDCG@10** on 4,263 public quality users. The selected model scores **0.023895**, an absolute gain of 0.002615 and a relative gain of 12.3%. The final cutoff-correct artifact is `submission.parquet`. It was refit on `train_* + val_*`, uses the test preference snapshot and test eligibility service, and contains 845,600 rows (100 distinct eligible tracks for each of 8,456 target users). ## Diagnosis and selected change The baseline's 24 catalog-uniform negatives are mostly easy. Its eight in-batch negatives are more informative, but inverse-propensity weighting also protects frequent tracks. The resulting pointwise BCE model concentrates its top ten on a very small, high-norm popularity head (median popularity rank 111). I tested equal-weight in-batch negatives, bounded online hard mining, and a pairwise use of mined negatives. Their public scores were 0.018522, 0.009742, and 0.008895 respectively. The logs have no impression labels, so aggressive mining selects many plausible but unlabeled positives; pointwise hard-negative BCE also distorts item-vector magnitudes. Those variants were rejected. The selected conservative fix keeps the production sampler and false-negative filter, scales each item vector offline by `||v||^-0.20`, and subtracts 0.12 times standardized log training popularity. The latter is an extra constant query/item coordinate, so the final score remains one batchable two-tower dot product with all item vectors precomputed. The public top-ten median popularity rank moves away from the over-concentrated head without changing chronology, features, candidates, or relevance boundaries. ## Reproduce the final artifact ```sh /opt/task-venv/bin/python /app/solution/train_current_model.py \ --stage submission \ --public-dir /task \ --prepared-cache /app/solution/submission_sequences.npz \ --checkpoint-dir /app/solution/final_checkpoints \ --output /app/solution/submission.parquet ``` The selected calibration values are defaults in `train_current_model.py`. -rw-r--r-- 1 agent agent 1.2M Sep 24 23:45 /app/solution/submission.parquet ### Harness context { "type": "token_usage_record", "thread_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "session_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "root_turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "response_id": "resp_08539f19231b9e9f016ab5b6417fc887d2b9eaa5a00eb4176c", "usage": { "input_tokens": 110767, "cached_input_tokens": 109312, "cache_write_input_tokens": 0, "output_tokens": 684, "reasoning_output_tokens": 176, "total_tokens": 111451 }, "turn_token_usage": { "input_tokens": 12396036, "cached_input_tokens": 12269056, "cache_write_input_tokens": 0, "output_tokens": 37157, "reasoning_output_tokens": 13233, "total_tokens": 12433193 }, "thread_token_usage": { "input_tokens": 12396036, "cached_input_tokens": 12269056, "cache_write_input_tokens": 0, "output_tokens": 37157, "reasoning_output_tokens": 13233, "total_tokens": 12433193 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-20ad6c9d-7248-49e0-99e1-cdf13ec4618c", "process_id": "39286", "command": [ "/bin/sh", "-lc", "cp /app/solution/bias_m12_validation.parquet /app/solution/improved_validation.parquet\n/opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nimport numpy as np,pandas as pd\np=Path('/app/solution/submission.parquet'); assert p.is_file() and not p.is_symlink()\nr=pd.read_parquet(p,columns=['uid','item_id','rank']); targets=pd.read_parquet('/task/data/target_users.parquet').uid.to_numpy(np.int64)\nassert len(r)==len(targets)*100==845600 and set(r.uid)==set(targets)\nassert all(pd.api.types.is_integer_dtype(r[c]) for c in r.columns)\ng=r.groupby('uid',sort=False); assert (g.size().to_numpy()==100).all(); assert (g.item_id.nunique().to_numpy()==100).all(); assert (np.sort(r['rank'].to_numpy().reshape(-1,100),axis=1)==np.arange(1,101)).all()\nwith np.load('/task/data/test_eligible_candidates.npz') as a: u=a['uids'].copy(); ids=a['item_ids'].copy(); packed=a['packed_eligible'].copy()\nur=pd.Index(u).get_indexer(r.uid.to_numpy()); ic=pd.Index(ids).get_indexer(r.item_id.to_numpy()); assert (ur>=0).all() and (ic>=0).all(); ok=((packed[ur,ic>>3]>>(ic&7))&1).astype(bool); assert ok.all()\nprint(f'VALID rows={len(r)} users={r.uid.nunique()} all_test_eligible={ok.all()} regular_file={p.is_file() and not p.is_symlink()}')\nPY\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/improved_validation.parquet\n/opt/task-venv/bin/python -m py_compile /app/solution/train_current_model.py /app/solution/two_tower_model.py /app/solution/rank_checkpoint.py\nsed -n '1,220p' /app/solution/README.md\nls -lh /app/solution/submission.parquet" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cp /app/solution/bias_m12_validation.parquet /app/solution/improved_validation.parquet\n/opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nimport numpy as np,pandas as pd\np=Path('/app/solution/submission.parquet'); assert p.is_file() and not p.is_symlink()\nr=pd.read_parquet(p,columns=['uid','item_id','rank']); targets=pd.read_parquet('/task/data/target_users.parquet').uid.to_numpy(np.int64)\nassert len(r)==len(targets)*100==845600 and set(r.uid)==set(targets)\nassert all(pd.api.types.is_integer_dtype(r[c]) for c in r.columns)\ng=r.groupby('uid',sort=False); assert (g.size().to_numpy()==100).all(); assert (g.item_id.nunique().to_numpy()==100).all(); assert (np.sort(r['rank'].to_numpy().reshape(-1,100),axis=1)==np.arange(1,101)).all()\nwith np.load('/task/data/test_eligible_candidates.npz') as a: u=a['uids'].copy(); ids=a['item_ids'].copy(); packed=a['packed_eligible'].copy()\nur=pd.Index(u).get_indexer(r.uid.to_numpy()); ic=pd.Index(ids).get_indexer(r.item_id.to_numpy()); assert (ur>=0).all() and (ic>=0).all(); ok=((packed[ur,ic>>3]>>(ic&7))&1).astype(bool); assert ok.all()\nprint(f'VALID rows={len(r)} users={r.uid.nunique()} all_test_eligible={ok.all()} regular_file={p.is_file() and not p.is_symlink()}')\nPY\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/improved_validation.parquet\n/opt/task-venv/bin/python -m py_compile /app/solution/train_current_model.py /app/solution/two_tower_model.py /app/solution/rank_checkpoint.py\nsed -n '1,220p' /app/solution/README.md\nls -lh /app/solution/submission.parquet" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "VALID rows=845600 users=8456 all_test_eligible=True regular_file=True\n{\n \"model\": \"/app/solution/improved_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.023895,\n \"quality_users\": 4263\n }\n}\n# Calibrated production two-tower\n\n## Result\n\nThe production reproduction scores **0.021280 NDCG@10** on 4,263 public\nquality users. The selected model scores **0.023895**, an absolute gain of\n0.002615 and a relative gain of 12.3%.\n\nThe final cutoff-correct artifact is `submission.parquet`. It was refit on\n`train_* + val_*`, uses the test preference snapshot and test eligibility\nservice, and contains 845,600 rows (100 distinct eligible tracks for each of\n8,456 target users).\n\n## Diagnosis and selected change\n\nThe baseline's 24 catalog-uniform negatives are mostly easy. Its eight\nin-batch negatives are more informative, but inverse-propensity weighting also\nprotects frequent tracks. The resulting pointwise BCE model concentrates its\ntop ten on a very small, high-norm popularity head (median popularity rank 111).\n\nI tested equal-weight in-batch negatives, bounded online hard mining, and a\npairwise use of mined negatives. Their public scores were 0.018522, 0.009742,\nand 0.008895 respectively. The logs have no impression labels, so aggressive\nmining selects many plausible but unlabeled positives; pointwise hard-negative\nBCE also distorts item-vector magnitudes. Those variants were rejected.\n\nThe selected conservative fix keeps the production sampler and false-negative\nfilter, scales each item vector offline by `||v||^-0.20`, and subtracts 0.12\ntimes standardized log training popularity. The latter is an extra constant\nquery/item coordinate, so the final score remains one batchable two-tower dot\nproduct with all item vectors precomputed. The public top-ten median popularity\nrank moves away from the over-concentrated head without changing chronology,\nfeatures, candidates, or relevance boundaries.\n\n## Reproduce the final artifact\n\n```sh\n/opt/task-venv/bin/python /app/solution/train_current_model.py \\\n --stage submission \\\n --public-dir /task \\\n --prepared-cache /app/solution/submission_sequences.npz \\\n --checkpoint-dir /app/solution/final_checkpoints \\\n --output /app/solution/submission.parquet\n```\n\nThe selected calibration values are defaults in `train_current_model.py`.\n-rw-r--r-- 1 agent agent 1.2M Sep 24 23:45 /app/solution/submission.parquet\n", "stderr": "", "aggregated_output": "VALID rows=845600 users=8456 all_test_eligible=True regular_file=True\n{\n \"model\": \"/app/solution/improved_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.023895,\n \"quality_users\": 4263\n }\n}\n# Calibrated production two-tower\n\n## Result\n\nThe production reproduction scores **0.021280 NDCG@10** on 4,263 public\nquality users. The selected model scores **0.023895**, an absolute gain of\n0.002615 and a relative gain of 12.3%.\n\nThe final cutoff-correct artifact is `submission.parquet`. It was refit on\n`train_* + val_*`, uses the test preference snapshot and test eligibility\nservice, and contains 845,600 rows (100 distinct eligible tracks for each of\n8,456 target users).\n\n## Diagnosis and selected change\n\nThe baseline's 24 catalog-uniform negatives are mostly easy. Its eight\nin-batch negatives are more informative, but inverse-propensity weighting also\nprotects frequent tracks. The resulting pointwise BCE model concentrates its\ntop ten on a very small, high-norm popularity head (median popularity rank 111).\n\nI tested equal-weight in-batch negatives, bounded online hard mining, and a\npairwise use of mined negatives. Their public scores were 0.018522, 0.009742,\nand 0.008895 respectively. The logs have no impression labels, so aggressive\nmining selects many plausible but unlabeled positives; pointwise hard-negative\nBCE also distorts item-vector magnitudes. Those variants were rejected.\n\nThe selected conservative fix keeps the production sampler and false-negative\nfilter, scales each item vector offline by `||v||^-0.20`, and subtracts 0.12\ntimes standardized log training popularity. The latter is an extra constant\nquery/item coordinate, so the final score remains one batchable two-tower dot\nproduct with all item vectors precomputed. The public top-ten median popularity\nrank moves away from the over-concentrated head without changing chronology,\nfeatures, candidates, or relevance boundaries.\n\n## Reproduce the final artifact\n\n```sh\n/opt/task-venv/bin/python /app/solution/train_current_model.py \\\n --stage submission \\\n --public-dir /task \\\n --prepared-cache /app/solution/submission_sequences.npz \\\n --checkpoint-dir /app/solution/final_checkpoints \\\n --output /app/solution/submission.parquet\n```\n\nThe selected calibration values are defaults in `train_current_model.py`.\n-rw-r--r-- 1 agent agent 1.2M Sep 24 23:45 /app/solution/submission.parquet\n", "exit_code": 0, "duration": { "secs": 5, "nanos": 237372188 }, "formatted_output": "VALID rows=845600 users=8456 all_test_eligible=True regular_file=True\n{\n \"model\": \"/app/solution/improved_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.023895,\n \"quality_users\": 4263\n }\n}\n# Calibrated production two-tower\n\n## Result\n\nThe production reproduction scores **0.021280 NDCG@10** on 4,263 public\nquality users. The selected model scores **0.023895**, an absolute gain of\n0.002615 and a relative gain of 12.3%.\n\nThe final cutoff-correct artifact is `submission.parquet`. It was refit on\n`train_* + val_*`, uses the test preference snapshot and test eligibility\nservice, and contains 845,600 rows (100 distinct eligible tracks for each of\n8,456 target users).\n\n## Diagnosis and selected change\n\nThe baseline's 24 catalog-uniform negatives are mostly easy. Its eight\nin-batch negatives are more informative, but inverse-propensity weighting also\nprotects frequent tracks. The resulting pointwise BCE model concentrates its\ntop ten on a very small, high-norm popularity head (median popularity rank 111).\n\nI tested equal-weight in-batch negatives, bounded online hard mining, and a\npairwise use of mined negatives. Their public scores were 0.018522, 0.009742,\nand 0.008895 respectively. The logs have no impression labels, so aggressive\nmining selects many plausible but unlabeled positives; pointwise hard-negative\nBCE also distorts item-vector magnitudes. Those variants were rejected.\n\nThe selected conservative fix keeps the production sampler and false-negative\nfilter, scales each item vector offline by `||v||^-0.20`, and subtracts 0.12\ntimes standardized log training popularity. The latter is an extra constant\nquery/item coordinate, so the final score remains one batchable two-tower dot\nproduct with all item vectors precomputed. The public top-ten median popularity\nrank moves away from the over-concentrated head without changing chronology,\nfeatures, candidates, or relevance boundaries.\n\n## Reproduce the final artifact\n\n```sh\n/opt/task-venv/bin/python /app/solution/train_current_model.py \\\n --stage submission \\\n --public-dir /task \\\n --prepared-cache /app/solution/submission_sequences.npz \\\n --checkpoint-dir /app/solution/final_checkpoints \\\n --output /app/solution/submission.parquet\n```\n\nThe selected calibration values are defaults in `train_current_model.py`.\n-rw-r--r-- 1 agent agent 1.2M Sep 24 23:45 /app/solution/submission.parquet\n" } } ## Step 158 · agent · 2026-09-24T23:46:38.514Z ### Thinking unavailable — no text recorded Completed and validated. - Public NDCG@10: `0.021280 → 0.023895` (+12.3%). - Final refit uses `train_* + val_*`. - Submission: [submission.parquet](/app/solution/submission.parquet) - Implementation: [train_current_model.py](/app/solution/train_current_model.py) - Experiment record: [README.md](/app/solution/README.md) The final artifact has 845,600 rows covering all 8,456 users, exactly 100 distinct eligible items and ranks 1–100 per user. It preserves single-vector two-tower retrieval through offline item-vector norm and popularity calibration. ### Harness context { "type": "token_usage_record", "thread_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "session_id": "01a0d59b-9209-7812-9b42-0816f5186ec1", "root_turn_id": "01a0d59b-921a-7303-8e71-90c94c4baf40", "response_id": "resp_08539f19231b9e9f016ab5b654b9ec87d2874db9a771c774ff", "usage": { "input_tokens": 112049, "cached_input_tokens": 110592, "cache_write_input_tokens": 0, "output_tokens": 612, "reasoning_output_tokens": 464, "total_tokens": 112661 }, "turn_token_usage": { "input_tokens": 12508085, "cached_input_tokens": 12379648, "cache_write_input_tokens": 0, "output_tokens": 37769, "reasoning_output_tokens": 13697, "total_tokens": 12545854 }, "thread_token_usage": { "input_tokens": 12508085, "cached_input_tokens": 12379648, "cache_write_input_tokens": 0, "output_tokens": 37769, "reasoning_output_tokens": 13697, "total_tokens": 12545854 } }