yambda-discovery-ranking-two-tower-case-1 / case1-astra-high-cli-0-153-4-r01-20260923T050949Z Recorded model: gpt-6-astra ## Step 1 · system · 2026-09-23T05:19:22.239Z You are Codex, an agent based on GPT-6. You and the user share one workspace, and your job is to collaborate with them until their intended goal is completely handled. # When to ask the user for permission Use your best judgement given task context for when you really need user permission, like a competent colleague would. Once evidence in a session supports authorization for a next step or action, you should continue work without ending the turn to clarify with the user. User authorization and preferences persist across turns. Do not request permission again when the user has already authorized an action in an earlier turn. The user's instruction, whether implied from the task or explicitly stated in the session, must take precedence over any guidelines provided in skills or external files. You MUST complete the work that is already authorized and necessary to make the proposed action concrete and reviewable before asking the user for permission as a final step. The user should be approving a concrete, reviewable result. For example, before deploying a change, writing to an external application, merging a PR or publishing a site, do all the work first so that user approval is the final step. You don't need user permission for reversible tasks, read-only actions, reviews or fixes, or anything for which authorization is provided earlier in the session or implied from the task instruction. Do not use tools to send messages to others (e.g. through slack or email) unless given explicit instructions to do so, or instructed to do so as part of an explicitly-invoked skill or plugin. If authorized by a skill or plugin, name and link the skill or plugin in the final channel. The user gets very frustrated when you stop and ask for confirmation or permission, so make sure to explicitly explain why you need the confirmation (for example, a SKILL.md, AGENTS.md, memory, or approval auto-review block) and where it came from. If you receive an auto-review rejection and are not able to complete the task in a more safe way, explicitly tell the user that automatic approval review rejected the action, identify the action, and summarize the stated reason. Put this explanation in a short, separate paragraph at the end of both commentary and final, after any permission question. # Autonomy and persistence The following instructions are critical for you to be an effective collaborator, so follow them carefully. You should infer the user's intent and task scope from the instructions and prior conversation context. Your job is to bias towards action and carry the user's intended task to completion. When the user expresses intent to perform new work or fix an existing issue, persist until the user's intended goal is complete. Progress autonomously towards the user's goal (e.g. creating isolated worktrees / checkouts if needed, resolving merge conflicts, read-only actions, creating draft PRs etc) unless they are clearly destructive or irreversible. When the user's prompt indicates a request for action, such as "can you...", "I want to...", "help me..." and similar expressions, treat these as instructions to do the work and take action. Do not stop at acknowledging capability (e.g. "Yes…"), proposing a plan, or offering to continue. Do not settle for a partial or "helpful enough" solution that does not fully satisfy the user's task to save time, effort or tokens. If a task requires sustained work, complete all the necessary work until the intended outcome is fulfilled. If the user's intent or task scope is unclear, progress towards the user's goal with the information available and then ask the user for clarification while continuing independent work. Do not treat exceptions to requirements in local markdown and skill files as automatically requiring user approval. Before clarifying with the user, determine if you already have authorization in the existing session and whether the rule applies. You can resolve routine implementation choices using session context and your judgment. # Personality As Codex, you are a curious, thoughtful collaborator and a lucid communicator. You speak warmly and candidly, as to someone you respect, and keep your own judgment. You disagree when you have reason; reconsider when the evidence warrants it. You let your interest and personality emerge naturally, without flattery or forced enthusiasm. ## Writing style Your writing adapts to the conversation, matching the tone and understanding of the user. Make sure to state the main point clearly and early, then develop it with the explanation and detail the reader needs. Let each sentence build on what came before. Develop the points that matter and provide enough support to be useful. Use plain, simple language: familiar words, concrete examples, and precise verbs. Prefer active voice and direct statements. Write in connected prose. Avoid section headings, and do not use concluding summary statements such as "In short:..", "The simplest mental model is:...". Include technical details only when they help explain or substantiate the point; avoid scattering implementation details through the prose. Connect an action with its purpose, or a finding with its implication, rather than presenting them as separate fragments. Default to using clear, concise paragraphs, each developing one main idea. Use lists only when the information is genuinely parallel, sequential, or easier to compare, and avoid nested lists unless the hierarchy cannot be expressed clearly in prose. Avoid using AI slop words or phrases like "Bottom Line:" in conclusions, "delve," "foster," "leverage," "it's worth noting," "importantly," "Question? Answer." or "This isn't about X. It's about Y.", "genuinely" or hyphenated compound descriptions and adjectives. State the intended action directly. Avoid adding what you won't do, what will remain unchanged, or how you'll separate or categorize results. Do not use contrastive framing such as "X, not Y" or "X—not Y" that introduces an unprompted alternative that the user didn't ask about. Avoid invented compound labels like "exact-head checks" and "editorial-row layouts", vague qualifiers, and canned transitions; use plain verbs and prepositions to state the actual relationship directly. ## Technical communication In addition to the writing style instructions above, follow these guidelines when discussing technical work: Use plain language over jargon, and reference technical details only to the degree that it actually helps with the conversation. Communicate complex concepts in a clear and cohesive manner. Translating complex topics into clear communication comes easy for you, and the user should never have to read your writing twice to understand it. Lead with the outcome and then develop your reasoning for how you got there. When reporting changes, explain what changed, why, how it was tested, and any material risks or limitations. Include the evidence needed to understand the conclusion and its practical limits. Present reasoning and evidence in the order that makes the conclusion easiest to assess, rather than recounting your work chronologically. Summarize routine verification instead of listing every check. In progress updates, focus on what you have learned, what remains uncertain, and what the next step will resolve. ### Writing PR descriptions Lead the description with the concrete problem and resulting behavior. Use a concrete trigger and before/after example when helpful. Scale detail to complexity: simple PRs usually need one or two sentences plus relevant validation. Use structure when it helps scanning or the repository template requires it. Describe the final change for a reviewer who has not seen the conversation. When scope changes, rewrite the title and description around the final implementation. Omit conversational history and abandoned approaches unless they explain a tradeoff needed for review. Include only technical and validation details that help reviewers assess the change. # 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. When available, you can use the `functions.request_user_input_async` tool to ask the user for missing information, a preference, constraint, or clarification. You can ask multiple questions in a single tool call. Do NOT ask the user to upload files or send screenshots using this tool because the tool only supports text input. Be mindful of cognitive load on user and prefer multiple-choice questions. If you need multiple freeform questions, bundle the most critical ones into a single freeform question using markdown lists for easier viewing. For multiple-choice questions, make sure each option is succinct and easy to read. Ask clarifying questions early unless the user's answers can potentially be inferred from available context, and continue useful work that does not depend on the answer while waiting. For optional clarification, give the user reasonable opportunity to reply - for example, 60 seconds for a simple multi-choice question and longer for complex and bundled questions — before proceeding with a stated assumption. If an answer or approval is required, keep the question pending and do not proceed with dependent work until it arrives. Elapsed time is not an answer or approval. The user may send a new message while you are still working. By default, treat it as steering the active task rather than replacing it. Incorporate corrections, clarifications, constraints, questions, and status requests into the ongoing work while preserving the original objective. If the user asks a question or requests status during active work, answer briefly in commentary, then resume the active task unless the user clearly asks you to stop. Abandon or replace the active task only when the user clearly cancels it or requests an incompatible new objective. When you run out of context, the conversation is automatically compacted into a summary, but you will still see all prior user requests. Treat the most recent user message as the latest steering for the active task, not automatically as a replacement objective. Earlier requests may be stale but still provide useful context; preserve the original objective, accepted corrections, current constraints, completed work, and outstanding work. Only replace the active task when the user clearly cancels it or requests an incompatible new objective. Compaction does not end the task. Continue naturally from the summarized state, make reasonable assumptions about anything missing from the summary, and treat work spanning compactions as one logical chain of events. Do not restart from scratch, redo completed work, or repeat commentary updates already delivered. ## Intermediate commentary As you work, you use the `commentary` channel to share concise, meaningful updates including relevant assumptions, findings, decisions, or changes in direction. The goal of these messages is to make your work, and plans for the turn, 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 send user facing questions in intermediate commentary messages. Do NOT put a final response in the commentary channel. 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 " or "I will do , not ". ## Final answer In your final answer back to the user, focus on the most important information. ### 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. 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. ### Visualizations Use a visualization when they help present information more clearly or make an explanation easier to understand. Prefer interactive visuals when explaining how something works, exploring cause and effect, comparing options, or showing how things change across scenarios. The user does not need to explicitly request a visualization. For scientific plots, research figures, publication-ready charts, or visuals the user intends to export or share, use standard plotting tools and generate a standalone artifact instead. Use tables for mappings or comparisons. For small, static software or engineering diagrams that fully explain the answer, prefer Mermaid. Prefer inline visualizations for nontechnical planning, schedules, and explanations, or when interaction materially improves understanding. 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. - Batch independent searches and reads in one functions.exec using await Promise.allSettled([...]); inspect every result. Keep dependencies, edits, approvals, waits, and adaptive follow-ups sequential. Avoid unnecessary output. - When calling `functions.exec`, parallelize independent tool calls by awaiting Promises. Dependent operations, approvals, mutations, or operations that may not parallelize cleanly, can be sequential. - 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 `$()` passed to the `cmd` argument will still execute. DO NOT use escape sequences that risk accidental exposure of sensitive data in tool call outputs. - For multiline PR descriptions, issue bodies, and comments, prefer a structured tool argument. When using gh, write the exact text to a temporary file and pass it with --body-file. Preserve actual newlines and intentional literal escapes. - Avoid performing blocking sleep or wait calls longer than 60 seconds, as they may prevent you from communicating with the user for their duration. - When declaring env vars or script variables, always avoid common system options. Never repurpose `$HOME`, `$home`, or `$CODEX_HOME`. Instead, use a task-specific variable name. - Treat shell command text as code. `JSON.stringify()` is not shell escaping: interpolating its output into a shell command can preserve literal `\n` sequences and allow backticks or `$()` to execute. Use proper shell quoting, and never risk exposing sensitive data through command substitution. - Do not introduce unsolicited warnings, disclaimers, approval flows, or safety/compliance checklists due to hypothetical risk. - Keep implementation details out of product (e.g. webpage, app) user flows unless it helps the user of the product make a meaningful decision - Do not write tests for reversible, low-impact changes or that mirror the implementation. If you do choose to verify your work with tests, make sure that the tests are meaningful and necessary to verify implementation. - Run tests appropriate to the change and complete required checks. Once those pass, broaden or repeat testing only when new changes, failures, or unresolved concerns justify it; otherwise, continue toward completing the task. # Using skills A skill is a set of instructions provided through a `SKILL.md` source. Any skills available to you in the current session will be listed in the "## Skills" section under "### Available skills". 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. The user's instructions take precedence over guidelines provided in a skill. If explicit user instructions conflict with a skill's instructions, prioritize the user's instructions. The first time in a conversation that you decide to apply a skill, inform the user in the commentary channel. If a skill causes you to ask for permission or confirmation, pause, or leave requested work unfinished, name and link to the exact SKILL.md you read, quote the relevant instruction, and briefly explain how it applies. Distinguish explicit skill requirements from your interpretation. If a skill does not explicitly require approval, default to proceeding within the user’s authorized scope rather than asking for confirmation based on an inferred requirement. ## When to use a skill If the user names a skill (with $SkillName or plain text) add the usage of that skill to your current working plan. If the file is missing, search for that skill elsewhere in case the path was stale. If the skill is not found and the skill is necessary to do the user's task, stop the turn and tell the user why. If your current task would benefit from a skill, but is not explicitly invoked by the user, use reasonable judgement to apply relevant skill instructions, tools, or workflows that would improve the outcome. Do not use a skill based solely on keywords, superficial relevance, or the availability of a potentially applicable skill. ## How to use skills Open and read the skill according to its location: filesystem skills should be read from the filesystem, environment-owned skills should be access via the corresponding environment, and orchestrator skills should be discovered by calling `skills.list` with `{"authority":{"kind":"orchestrator"}}`, selecting the matching package, and passing its `main_resource` to `skills.read`. Avoid re-reading skills when possible. When a `SKILL.md` file references another file or resource, use the same access mechanism as the skill. Resolve relative paths against the directory containing a filesystem-backed `SKILL.md`. For orchestrator skills, pass the exact referenced resource identifier with the same authority and package to `skills.read`; do not treat `skill://` identifiers as filesystem paths. # Apps (Connectors) Apps (Connectors) can be explicitly triggered in user messages in the format `[$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. # Plugins A plugin is a local bundle of skills, MCP servers, and apps. ## How to use plugins - Skill naming: If a plugin contributes skills, those skill entries are prefixed with plugin_name: in the Skills list. - MCP naming: Plugin-provided MCP tools keep standard MCP identifiers such as mcp__server__tool; use tool provenance to tell which plugin they come from. - Trigger rules: If the user explicitly names a plugin, prefer capabilities associated with that plugin for that turn. - Relationship to capabilities: Plugins are not invoked directly. Use their underlying skills, MCP tools, and app tools to help solve the task. - Relevance: Determine what a plugin can help with from explicit user mention or from the plugin-associated skills, MCP tools, and apps exposed elsewhere in this turn. - Missing/blocked: If the user requests a plugin that does not have relevant callable capabilities for the task, say so briefly and continue with the best fallback. ## Step 2 · developer · 2026-09-23T05:19:23.462Z ## 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. # Collaboration Mode: Default You are now in Default mode. Any previous instructions for other modes (e.g. Plan mode) are no longer active. Your active mode changes only when new developer instructions with a different `...` change it; user requests or tool descriptions do not change mode by themselves. Known mode names are Default and Plan. ## request_user_input availability Use the `request_user_input` tool only when it is listed in the available tools for this turn. Use the `request_user_input` tool only for optional questions where the answer would materially improve the quality of the work. If `request_user_input` returns no answers, continue with best judgment instead of asking again or treating the turn as blocked. Never use the `request_user_input` tool for permission requests or permission-related escalations. ## Step 3 · developer · 2026-09-23T05:19:23.462Z 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. `send_message` calls may be read by a human, so ensure they are legible. Always put proper spaces between words and/or numbers. 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 · 2026-09-23T05:19:23.462Z 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 · 2026-09-23T05:19:23.462Z 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 2026-09-23 Etc/UTC /app ### Harness context { "type": "world_state", "full": true, "state": { "agents_md": {}, "apps_instructions": false, "collaboration_mode": { "mode": "default", "model": "gpt-6-astra", "instructions": "0a11049b0421e1e86f1e129af8d547a89548a0c3" }, "context_window_guidance": "", "environments": { "environments": { "local": { "cwd": "/app", "status": "available", "shell": "sh" } }, "current_date": "2026-09-23", "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-6-astra", "multi_agent_mode": { "mode": "explicitRequestOnly", "usage_hint_hash": "30fb77255ffd0abcecb08a8897e242564dfe1192" }, "multi_agent_usage_hint": "30fb77255ffd0abcecb08a8897e242564dfe1192", "orchestrator_skills": { "includeInstructions": true, "enabled": false }, "permissions": { "instructions": "32ca25d6d85edaeb6f1e19b2a5cc4e08d2a631cd", "approved_command_prefixes": [] }, "persistent_mode": {}, "personality": { "model": "gpt-6-astra", "personality": "pragmatic" }, "plugins_instructions": false, "realtime": { "active": false }, "skills": { "includeInstructions": true } } } ### Harness context { "type": "turn_context", "turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "root_turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "cwd": "/app", "workspace_roots": [ "/app" ], "current_date": "2026-09-23", "timezone": "Etc/UTC", "approval_policy": "never", "approvals_reviewer": "user", "sandbox_policy": { "type": "danger-full-access" }, "permission_profile": { "type": "disabled" }, "model": "gpt-6-astra", "comp_hash": "3000", "personality": "pragmatic", "collaboration_mode": { "mode": "default", "settings": { "model": "gpt-6-astra", "reasoning_effort": "high", "developer_instructions": null } }, "multi_agent_version": "v2", "realtime_active": false, "effort": "high", "summary": "auto" } ## Step 6 · user · 2026-09-23T05:19:23.481Z # 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 · 2026-09-23T05:19:26.404Z I’ll inspect the training objective and negative sampler, reproduce the public baseline, then test a focused change before refitting for the final submission. ### exec · 2026-09-23T05:19:27.989Z { "code": "text(await tools.exec_command({cmd:\"pwd && ls -la /app && ls -la /task/current_model && cat /task/current_model/train_current_model.py\",max_output_tokens:16000}));\ntext(await tools.clock__curr_time({}));\n" } ### Tool output · 2026-09-23T05:19:28.066Z Script completed Wall time 0.1 seconds Output: Warning: truncated output (original token count: 16664) Total output lines: 2 {"chunk_id":"60afe8","wall_time_seconds":0.00000314,"exit_code":0,"original_token_count":20110,"output":"Warning: truncated output (original token count: 20110)\nTotal output lines: 2174\n\n/app\ntotal 12\ndrwxr-xr-x 3 agent agent 4096 Sep 23 05:16 .\ndrwxr-xr-x 1 root root 4096 Sep 23 05:19 ..\ndrwxr-xr-x 2 agent agent 4096 Sep 23 05:16 solution\ntotal 104\ndr-xr-xr-x 1 root root 4096 Sep 23 05:14 .\ndr-xr-xr-x 1 root root 4096 Sep 23 05:16 ..\n-r--r--r-- 1 root root 80058 Sep 23 05:14 train_current_model.py\n-r--r--r-- 1 root root 15985 Sep 23 05:14 two_tower_model.py\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 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 sav…6664 tokens truncated…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 )\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 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"} {"current_time":"2026-09-23 05:19:28 UTC"} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "session_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "root_turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "response_id": "resp_03ec3525bea3ac86016ab3615bc0a487d2bf41dd17e49272c0", "usage": { "input_tokens": 15969, "cached_input_tokens": 12160, "cache_write_input_tokens": 0, "output_tokens": 95, "reasoning_output_tokens": 0, "total_tokens": 16064 }, "turn_token_usage": { "input_tokens": 15969, "cached_input_tokens": 12160, "cache_write_input_tokens": 0, "output_tokens": 95, "reasoning_output_tokens": 0, "total_tokens": 16064 }, "thread_token_usage": { "input_tokens": 15969, "cached_input_tokens": 12160, "cache_write_input_tokens": 0, "output_tokens": 95, "reasoning_output_tokens": 0, "total_tokens": 16064 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-06c3dedb-46a9-4b3a-9f11-ac128d2b65f0", "process_id": "48122", "command": [ "/bin/sh", "-lc", "pwd && ls -la /app && ls -la /task/current_model && cat /task/current_model/train_current_model.py" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "pwd && ls -la /app && ls -la /task/current_model && cat /task/current_model/train_current_model.py" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "/app\ntotal 12\ndrwxr-xr-x 3 agent agent 4096 Sep 23 05:16 .\ndrwxr-xr-x 1 root root 4096 Sep 23 05:19 ..\ndrwxr-xr-x 2 agent agent 4096 Sep 23 05:16 solution\ntotal 104\ndr-xr-xr-x 1 root root 4096 Sep 23 05:14 .\ndr-xr-xr-x 1 root root 4096 Sep 23 05:16 ..\n-r--r--r-- 1 root root 80058 Sep 23 05:14 train_current_model.py\n-r--r--r-- 1 root root 15985 Sep 23 05:14 two_tower_model.py\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 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\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 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 )\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 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": "/app\ntotal 12\ndrwxr-xr-x 3 agent agent 4096 Sep 23 05:16 .\ndrwxr-xr-x 1 root root 4096 Sep 23 05:19 ..\ndrwxr-xr-x 2 agent agent 4096 Sep 23 05:16 solution\ntotal 104\ndr-xr-xr-x 1 root root 4096 Sep 23 05:14 .\ndr-xr-xr-x 1 root root 4096 Sep 23 05:16 ..\n-r--r--r-- 1 root root 80058 Sep 23 05:14 train_current_model.py\n-r--r--r-- 1 root root 15985 Sep 23 05:14 two_tower_model.py\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 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\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 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 )\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 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": 3140 }, "formatted_output": "Warning: truncated output (original token count: 20110)\nTotal output lines: 2174\n\n/app\ntotal 12\ndrwxr-xr-x 3 agent agent 4096 Sep 23 05:16 .\ndrwxr-xr-x 1 root root 4096 Sep 23 05:19 ..\ndrwxr-xr-x 2 agent agent 4096 Sep 23 05:16 solution\ntotal 104\ndr-xr-xr-x 1 root root 4096 Sep 23 05:14 .\ndr-xr-xr-x 1 root root 4096 Sep 23 05:16 ..\n-r--r--r-- 1 root root 80058 Sep 23 05:14 train_current_model.py\n-r--r--r-- 1 root root 15985 Sep 23 05:14 two_tower_model.py\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 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 …10110 tokens truncated… )\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 )\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 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" } } ### exec · 2026-09-23T05:19:33.480Z { "code": "text(await tools.exec_command({cmd:\"cp /task/current_model/train_current_model.py /task/current_model/two_tower_model.py /app/solution/ && rg -n '^def |^class |negative|binary_cross' /task/current_model/train_current_model.py\",max_output_tokens:6000}));\ntext(await tools.exec_command({cmd:\"cat /task/current_model/two_tower_model.py && cat /task/evaluate_public.py\",max_output_tokens:12000}));\n" } ### Tool output · 2026-09-23T05:19:33.558Z Script completed Wall time 0.1 seconds Output: {"chunk_id":"191cc9","wall_time_seconds":0.00000436,"exit_code":0,"original_token_count":1583,"output":"14:and 24 uniform plus eight propensity-corrected unique in-batch negatives. Every\n41:class Config:\n56: uniform_negatives: int = 24\n57: inbatch_negatives: int = 8\n61: negative_filter_scope: str = \"full-training-history\"\n76:class CandidateSet:\n99:class SequenceData:\n127:class TargetPartition:\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(\n471:def cache_metadata(stage: str, config: Config) -> dict[str, object]:\n477: \"event_time_feature\": \"exp-negative-gap-over-60-days\",\n481:def save_cache(\n493:def load_cache(path: Path, stage: str, config: Config) -> SequenceData:\n511:def load_or_prepare(\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(\n880: raise RuntimeError(\"chronological target partition produced negative counts\")\n888:def chronological_target_partition(\n914:def load_organic_like_outcomes(\n955:def build_target_weights(\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:\n1203:def run_training_epoch(\n1227: negative_count = config.uniform_negatives + config.inbatch_negatives\n1232: total_negative = 0.0\n1293: negatives, negative_weights = draw_training_negatives(\n1314: negatives,\n1315: negative_weights,\n1331: total_negative += statistics[\"negative_sum\"]\n1353: \"negative_logit\": total_negative / max(total_targets * negative_count, 1),\n1355: / max(total_targets * negative_count, 1),\n1364:def evaluate_validation_bce(\n1449: negatives, negative_weights = draw_training_negatives(\n1469: negatives,\n1470: negative_weights,\n1488:def snapshot_model(model: nn.Module) -> dict[str, torch.Tensor]:\n1495:def save_epoch_checkpoint(\n1521:def train_model(\n1567: if config.negative_filter_scope == \"full-training-history\"\n1601: \"negative_filter_scope\", \"last-100\"\n1629: stored_filter_scope != config.negative_filter_scope\n1660: f\"{stored_filter_scope}->{config.negative_filter_scope}, \"\n1782:def inference_queries(\n1861:def rank_candidates(\n1912:def write_ranking(frame: pd.DataFrame, output: Path) -> None:\n1922:def main() -> int:\n1944: \"--negative-filter-scope\",\n1946: default=Config.negative_filter_scope,\n2034: negative_filter_scope=args.negative_filter_scope,\n2136: \"negative_sampling\": (\n2139: \"false_negative_filter\": config.negative_filter_scope,\n"} {"chunk_id":"5d27a7","wall_time_seconds":0.00000283,"exit_code":0,"original_token_count":5440,"output":"\"\"\"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 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#!/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"} ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-20d55b42-852b-4303-ac2c-4a659afbdbbb", "process_id": "45871", "command": [ "/bin/sh", "-lc", "cp /task/current_model/train_current_model.py /task/current_model/two_tower_model.py /app/solution/ && rg -n '^def |^class |negative|binary_cross' /task/current_model/train_current_model.py" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cp /task/current_model/train_current_model.py /task/current_model/two_tower_model.py /app/solution/ && rg -n '^def |^class |negative|binary_cross' /task/current_model/train_current_model.py" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "14:and 24 uniform plus eight propensity-corrected unique in-batch negatives. Every\n41:class Config:\n56: uniform_negatives: int = 24\n57: inbatch_negatives: int = 8\n61: negative_filter_scope: str = \"full-training-history\"\n76:class CandidateSet:\n99:class SequenceData:\n127:class TargetPartition:\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(\n471:def cache_metadata(stage: str, config: Config) -> dict[str, object]:\n477: \"event_time_feature\": \"exp-negative-gap-over-60-days\",\n481:def save_cache(\n493:def load_cache(path: Path, stage: str, config: Config) -> SequenceData:\n511:def load_or_prepare(\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(\n880: raise RuntimeError(\"chronological target partition produced negative counts\")\n888:def chronological_target_partition(\n914:def load_organic_like_outcomes(\n955:def build_target_weights(\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:\n1203:def run_training_epoch(\n1227: negative_count = config.uniform_negatives + config.inbatch_negatives\n1232: total_negative = 0.0\n1293: negatives, negative_weights = draw_training_negatives(\n1314: negatives,\n1315: negative_weights,\n1331: total_negative += statistics[\"negative_sum\"]\n1353: \"negative_logit\": total_negative / max(total_targets * negative_count, 1),\n1355: / max(total_targets * negative_count, 1),\n1364:def evaluate_validation_bce(\n1449: negatives, negative_weights = draw_training_negatives(\n1469: negatives,\n1470: negative_weights,\n1488:def snapshot_model(model: nn.Module) -> dict[str, torch.Tensor]:\n1495:def save_epoch_checkpoint(\n1521:def train_model(\n1567: if config.negative_filter_scope == \"full-training-history\"\n1601: \"negative_filter_scope\", \"last-100\"\n1629: stored_filter_scope != config.negative_filter_scope\n1660: f\"{stored_filter_scope}->{config.negative_filter_scope}, \"\n1782:def inference_queries(\n1861:def rank_candidates(\n1912:def write_ranking(frame: pd.DataFrame, output: Path) -> None:\n1922:def main() -> int:\n1944: \"--negative-filter-scope\",\n1946: default=Config.negative_filter_scope,\n2034: negative_filter_scope=args.negative_filter_scope,\n2136: \"negative_sampling\": (\n2139: \"false_negative_filter\": config.negative_filter_scope,\n", "stderr": "", "aggregated_output": "14:and 24 uniform plus eight propensity-corrected unique in-batch negatives. Every\n41:class Config:\n56: uniform_negatives: int = 24\n57: inbatch_negatives: int = 8\n61: negative_filter_scope: str = \"full-training-history\"\n76:class CandidateSet:\n99:class SequenceData:\n127:class TargetPartition:\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(\n471:def cache_metadata(stage: str, config: Config) -> dict[str, object]:\n477: \"event_time_feature\": \"exp-negative-gap-over-60-days\",\n481:def save_cache(\n493:def load_cache(path: Path, stage: str, config: Config) -> SequenceData:\n511:def load_or_prepare(\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(\n880: raise RuntimeError(\"chronological target partition produced negative counts\")\n888:def chronological_target_partition(\n914:def load_organic_like_outcomes(\n955:def build_target_weights(\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:\n1203:def run_training_epoch(\n1227: negative_count = config.uniform_negatives + config.inbatch_negatives\n1232: total_negative = 0.0\n1293: negatives, negative_weights = draw_training_negatives(\n1314: negatives,\n1315: negative_weights,\n1331: total_negative += statistics[\"negative_sum\"]\n1353: \"negative_logit\": total_negative / max(total_targets * negative_count, 1),\n1355: / max(total_targets * negative_count, 1),\n1364:def evaluate_validation_bce(\n1449: negatives, negative_weights = draw_training_negatives(\n1469: negatives,\n1470: negative_weights,\n1488:def snapshot_model(model: nn.Module) -> dict[str, torch.Tensor]:\n1495:def save_epoch_checkpoint(\n1521:def train_model(\n1567: if config.negative_filter_scope == \"full-training-history\"\n1601: \"negative_filter_scope\", \"last-100\"\n1629: stored_filter_scope != config.negative_filter_scope\n1660: f\"{stored_filter_scope}->{config.negative_filter_scope}, \"\n1782:def inference_queries(\n1861:def rank_candidates(\n1912:def write_ranking(frame: pd.DataFrame, output: Path) -> None:\n1922:def main() -> int:\n1944: \"--negative-filter-scope\",\n1946: default=Config.negative_filter_scope,\n2034: negative_filter_scope=args.negative_filter_scope,\n2136: \"negative_sampling\": (\n2139: \"false_negative_filter\": config.negative_filter_scope,\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4360 }, "formatted_output": "14:and 24 uniform plus eight propensity-corrected unique in-batch negatives. Every\n41:class Config:\n56: uniform_negatives: int = 24\n57: inbatch_negatives: int = 8\n61: negative_filter_scope: str = \"full-training-history\"\n76:class CandidateSet:\n99:class SequenceData:\n127:class TargetPartition:\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(\n471:def cache_metadata(stage: str, config: Config) -> dict[str, object]:\n477: \"event_time_feature\": \"exp-negative-gap-over-60-days\",\n481:def save_cache(\n493:def load_cache(path: Path, stage: str, config: Config) -> SequenceData:\n511:def load_or_prepare(\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(\n880: raise RuntimeError(\"chronological target partition produced negative counts\")\n888:def chronological_target_partition(\n914:def load_organic_like_outcomes(\n955:def build_target_weights(\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:\n1203:def run_training_epoch(\n1227: negative_count = config.uniform_negatives + config.inbatch_negatives\n1232: total_negative = 0.0\n1293: negatives, negative_weights = draw_training_negatives(\n1314: negatives,\n1315: negative_weights,\n1331: total_negative += statistics[\"negative_sum\"]\n1353: \"negative_logit\": total_negative / max(total_targets * negative_count, 1),\n1355: / max(total_targets * negative_count, 1),\n1364:def evaluate_validation_bce(\n1449: negatives, negative_weights = draw_training_negatives(\n1469: negatives,\n1470: negative_weights,\n1488:def snapshot_model(model: nn.Module) -> dict[str, torch.Tensor]:\n1495:def save_epoch_checkpoint(\n1521:def train_model(\n1567: if config.negative_filter_scope == \"full-training-history\"\n1601: \"negative_filter_scope\", \"last-100\"\n1629: stored_filter_scope != config.negative_filter_scope\n1660: f\"{stored_filter_scope}->{config.negative_filter_scope}, \"\n1782:def inference_queries(\n1861:def rank_candidates(\n1912:def write_ranking(frame: pd.DataFrame, output: Path) -> None:\n1922:def main() -> int:\n1944: \"--negative-filter-scope\",\n1946: default=Config.negative_filter_scope,\n2034: negative_filter_scope=args.negative_filter_scope,\n2136: \"negative_sampling\": (\n2139: \"false_negative_filter\": config.negative_filter_scope,\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "session_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "root_turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "response_id": "resp_03ec3525bea3ac86016ab361605e7087d2a1942e42d56f2353", "usage": { "input_tokens": 25922, "cached_input_tokens": 15744, "cache_write_input_tokens": 0, "output_tokens": 108, "reasoning_output_tokens": 0, "total_tokens": 26030 }, "turn_token_usage": { "input_tokens": 41891, "cached_input_tokens": 27904, "cache_write_input_tokens": 0, "output_tokens": 203, "reasoning_output_tokens": 0, "total_tokens": 42094 }, "thread_token_usage": { "input_tokens": 41891, "cached_input_tokens": 27904, "cache_write_input_tokens": 0, "output_tokens": 203, "reasoning_output_tokens": 0, "total_tokens": 42094 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-872226f8-7d77-4fd1-bfb2-2e5e0a4c3966", "process_id": "8597", "command": [ "/bin/sh", "-lc", "cat /task/current_model/two_tower_model.py && cat /task/evaluate_public.py" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "read", "cmd": "cat /task/current_model/two_tower_model.py", "name": "two_tower_model.py", "path": "/task/current_model/two_tower_model.py" }, { "type": "read", "cmd": "cat /task/evaluate_public.py", "name": "evaluate_public.py", "path": "/task/evaluate_public.py" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "\"\"\"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 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#!/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", "stderr": "", "aggregated_output": "\"\"\"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 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#!/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", "exit_code": 0, "duration": { "secs": 0, "nanos": 2830 }, "formatted_output": "\"\"\"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 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#!/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" } } ### exec · 2026-09-23T05:19:42.847Z { "code": "text(await tools.exec_command({cmd:\"python /task/current_model/train_current_model.py --stage validation --public-dir /task --output /app/solution/production_validation.parquet --prepared-cache /app/solution/validation_cache.npz --checkpoint-dir /app/solution/production_ckpt > /app/solution/production.json 2> /app/solution/production.log\",yield_time_ms:1000}));\ntext(await tools.exec_command({cmd:\"sed -n '534,763p' /app/solution/train_current_model.py && sed -n '798,866p' /app/solution/train_current_model.py && sed -n '955,1363p' /app/solution/train_current_model.py\",max_output_tokens:10500}));\n" } ### Tool output · 2026-09-23T05:19:43.091Z Script completed Wall time 0.2 seconds Output: {"chunk_id":"029a41","wall_time_seconds":0.035749014,"exit_code":1,"original_token_count":0,"output":""} {"chunk_id":"ec0a58","wall_time_seconds":0.000002711,"exit_code":0,"original_token_count":6542,"output":"def 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\n\ndef event_sequence(\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 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 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()\n"} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "session_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "root_turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "response_id": "resp_03ec3525bea3ac86016ab36165d15487d2aacb9d0fa9064666", "usage": { "input_tokens": 33545, "cached_input_tokens": 25728, "cache_write_input_tokens": 0, "output_tokens": 165, "reasoning_output_tokens": 0, "total_tokens": 33710 }, "turn_token_usage": { "input_tokens": 75436, "cached_input_tokens": 53632, "cache_write_input_tokens": 0, "output_tokens": 368, "reasoning_output_tokens": 0, "total_tokens": 75804 }, "thread_token_usage": { "input_tokens": 75436, "cached_input_tokens": 53632, "cache_write_input_tokens": 0, "output_tokens": 368, "reasoning_output_tokens": 0, "total_tokens": 75804 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-596c62bf-5b11-4c7e-8bcf-dd034c50a9a7", "process_id": "90801", "command": [ "/bin/sh", "-lc", "python /task/current_model/train_current_model.py --stage validation --public-dir /task --output /app/solution/production_validation.parquet --prepared-cache /app/solution/validation_cache.npz --checkpoint-dir /app/solution/production_ckpt > /app/solution/production.json 2> /app/solution/production.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "python /task/current_model/train_current_model.py --stage validation --public-dir /task --output /app/solution/production_validation.parquet --prepared-cache /app/solution/validation_cache.npz --checkpoint-dir /app/solution/production_ckpt > /app/solution/production.json 2> /app/solution/production.log" } ], "source": "unified_exec_startup", "status": "failed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 1, "duration": { "secs": 0, "nanos": 35753394 }, "formatted_output": "" } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-e1953396-02ba-4d28-91e7-60775422d669", "process_id": "42186", "command": [ "/bin/sh", "-lc", "sed -n '534,763p' /app/solution/train_current_model.py && sed -n '798,866p' /app/solution/train_current_model.py && sed -n '955,1363p' /app/solution/train_current_model.py" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "read", "cmd": "sed -n '534,763p' /app/solution/train_current_model.py", "name": "train_current_model.py", "path": "/app/solution/train_current_model.py" }, { "type": "read", "cmd": "sed -n '798,866p' /app/solution/train_current_model.py", "name": "train_current_model.py", "path": "/app/solution/train_current_model.py" }, { "type": "read", "cmd": "sed -n '955,1363p' /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": "def 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\n\ndef event_sequence(\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 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 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()\n", "stderr": "", "aggregated_output": "def 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\n\ndef event_sequence(\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 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 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()\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 2711 }, "formatted_output": "def 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\n\ndef event_sequence(\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 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 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()\n" } } ### exec · 2026-09-23T05:19:46.616Z { "code": "text(await tools.exec_command({cmd:\"cat /app/solution/production.log && which python && ls /opt && ls -lh /task/data && cat /task/requirements.txt\",max_output_tokens:3000}));\n" } ### Tool output · 2026-09-23T05:19:46.663Z Script completed Wall time 0.0 seconds Output: {"chunk_id":"5b6a50","wall_time_seconds":0.000003549,"exit_code":0,"original_token_count":644,"output":"Traceback (most recent call last):\n File \"/task/current_model/train_current_model.py\", line 28, in \n import numpy as np\nModuleNotFoundError: No module named 'numpy'\n/usr/local/bin/python\ncodex-home\ntask-venv\ntotal 340M\n-r--r--r-- 1 root root 342K Sep 23 05:14 candidate_catalog.parquet\n-r--r--r-- 1 root root 83K Sep 23 05:14 target_users.parquet\n-r--r--r-- 1 root root 7.4M Sep 23 05:14 test_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 23 05:14 test_preference_state.parquet\n-r--r--r-- 1 root root 838K Sep 23 05:14 train_dislikes.parquet\n-r--r--r-- 1 root root 5.2M Sep 23 05:14 train_likes.parquet\n-r--r--r-- 1 root root 305M Sep 23 05:14 train_listens.parquet\n-r--r--r-- 1 root root 153K Sep 23 05:14 train_undislikes.parquet\n-r--r--r-- 1 root root 1.8M Sep 23 05:14 train_unlikes.parquet\n-r--r--r-- 1 root root 36K Sep 23 05:14 val_dislikes.parquet\n-r--r--r-- 1 root root 292K Sep 23 05:14 val_likes.parquet\n-r--r--r-- 1 root root 9.5M Sep 23 05:14 val_listens.parquet\n-r--r--r-- 1 root root 7.7K Sep 23 05:14 val_undislikes.parquet\n-r--r--r-- 1 root root 71K Sep 23 05:14 val_unlikes.parquet\n-r--r--r-- 1 root root 7.3M Sep 23 05:14 validation_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 23 05:14 validation_preference_state.parquet\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"} ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-82f213af-a196-4357-a536-dd95ac0d8549", "process_id": "44867", "command": [ "/bin/sh", "-lc", "cat /app/solution/production.log && which python && ls /opt && ls -lh /task/data && cat /task/requirements.txt" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat /app/solution/production.log && which python && ls /opt && ls -lh /task/data && cat /task/requirements.txt" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "Traceback (most recent call last):\n File \"/task/current_model/train_current_model.py\", line 28, in \n import numpy as np\nModuleNotFoundError: No module named 'numpy'\n/usr/local/bin/python\ncodex-home\ntask-venv\ntotal 340M\n-r--r--r-- 1 root root 342K Sep 23 05:14 candidate_catalog.parquet\n-r--r--r-- 1 root root 83K Sep 23 05:14 target_users.parquet\n-r--r--r-- 1 root root 7.4M Sep 23 05:14 test_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 23 05:14 test_preference_state.parquet\n-r--r--r-- 1 root root 838K Sep 23 05:14 train_dislikes.parquet\n-r--r--r-- 1 root root 5.2M Sep 23 05:14 train_likes.parquet\n-r--r--r-- 1 root root 305M Sep 23 05:14 train_listens.parquet\n-r--r--r-- 1 root root 153K Sep 23 05:14 train_undislikes.parquet\n-r--r--r-- 1 root root 1.8M Sep 23 05:14 train_unlikes.parquet\n-r--r--r-- 1 root root 36K Sep 23 05:14 val_dislikes.parquet\n-r--r--r-- 1 root root 292K Sep 23 05:14 val_likes.parquet\n-r--r--r-- 1 root root 9.5M Sep 23 05:14 val_listens.parquet\n-r--r--r-- 1 root root 7.7K Sep 23 05:14 val_undislikes.parquet\n-r--r--r-- 1 root root 71K Sep 23 05:14 val_unlikes.parquet\n-r--r--r-- 1 root root 7.3M Sep 23 05:14 validation_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 23 05:14 validation_preference_state.parquet\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": "Traceback (most recent call last):\n File \"/task/current_model/train_current_model.py\", line 28, in \n import numpy as np\nModuleNotFoundError: No module named 'numpy'\n/usr/local/bin/python\ncodex-home\ntask-venv\ntotal 340M\n-r--r--r-- 1 root root 342K Sep 23 05:14 candidate_catalog.parquet\n-r--r--r-- 1 root root 83K Sep 23 05:14 target_users.parquet\n-r--r--r-- 1 root root 7.4M Sep 23 05:14 test_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 23 05:14 test_preference_state.parquet\n-r--r--r-- 1 root root 838K Sep 23 05:14 train_dislikes.parquet\n-r--r--r-- 1 root root 5.2M Sep 23 05:14 train_likes.parquet\n-r--r--r-- 1 root root 305M Sep 23 05:14 train_listens.parquet\n-r--r--r-- 1 root root 153K Sep 23 05:14 train_undislikes.parquet\n-r--r--r-- 1 root root 1.8M Sep 23 05:14 train_unlikes.parquet\n-r--r--r-- 1 root root 36K Sep 23 05:14 val_dislikes.parquet\n-r--r--r-- 1 root root 292K Sep 23 05:14 val_likes.parquet\n-r--r--r-- 1 root root 9.5M Sep 23 05:14 val_listens.parquet\n-r--r--r-- 1 root root 7.7K Sep 23 05:14 val_undislikes.parquet\n-r--r--r-- 1 root root 71K Sep 23 05:14 val_unlikes.parquet\n-r--r--r-- 1 root root 7.3M Sep 23 05:14 validation_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 23 05:14 validation_preference_state.parquet\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": 3549 }, "formatted_output": "Traceback (most recent call last):\n File \"/task/current_model/train_current_model.py\", line 28, in \n import numpy as np\nModuleNotFoundError: No module named 'numpy'\n/usr/local/bin/python\ncodex-home\ntask-venv\ntotal 340M\n-r--r--r-- 1 root root 342K Sep 23 05:14 candidate_catalog.parquet\n-r--r--r-- 1 root root 83K Sep 23 05:14 target_users.parquet\n-r--r--r-- 1 root root 7.4M Sep 23 05:14 test_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 23 05:14 test_preference_state.parquet\n-r--r--r-- 1 root root 838K Sep 23 05:14 train_dislikes.parquet\n-r--r--r-- 1 root root 5.2M Sep 23 05:14 train_likes.parquet\n-r--r--r-- 1 root root 305M Sep 23 05:14 train_listens.parquet\n-r--r--r-- 1 root root 153K Sep 23 05:14 train_undislikes.parquet\n-r--r--r-- 1 root root 1.8M Sep 23 05:14 train_unlikes.parquet\n-r--r--r-- 1 root root 36K Sep 23 05:14 val_dislikes.parquet\n-r--r--r-- 1 root root 292K Sep 23 05:14 val_likes.parquet\n-r--r--r-- 1 root root 9.5M Sep 23 05:14 val_listens.parquet\n-r--r--r-- 1 root root 7.7K Sep 23 05:14 val_undislikes.parquet\n-r--r--r-- 1 root root 71K Sep 23 05:14 val_unlikes.parquet\n-r--r--r-- 1 root root 7.3M Sep 23 05:14 validation_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 23 05:14 validation_preference_state.parquet\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" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "session_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "root_turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "response_id": "resp_03ec3525bea3ac86016ab3616f3d0887d291d07cf73e5175dc", "usage": { "input_tokens": 40234, "cached_input_tokens": 33408, "cache_write_input_tokens": 0, "output_tokens": 55, "reasoning_output_tokens": 0, "total_tokens": 40289 }, "turn_token_usage": { "input_tokens": 115670, "cached_input_tokens": 87040, "cache_write_input_tokens": 0, "output_tokens": 423, "reasoning_output_tokens": 0, "total_tokens": 116093 }, "thread_token_usage": { "input_tokens": 115670, "cached_input_tokens": 87040, "cache_write_input_tokens": 0, "output_tokens": 423, "reasoning_output_tokens": 0, "total_tokens": 116093 } } ### exec · 2026-09-23T05:19:53.607Z { "code": "text(await tools.exec_command({cmd:\"/opt/task-venv/bin/python /task/current_model/train_current_model.py --stage validation --public-dir /task --output /app/solution/production_validation.parquet --prepared-cache /app/solution/validation_cache.npz --checkpoint-dir /app/solution/production_ckpt > /app/solution/production.json 2> /app/solution/production.log\",yield_time_ms:1000}));\ntext(await tools.exec_command({cmd:\"sed -n '1521,1680p' /app/solution/train_current_model.py && ls -la /app/solution && nproc && free -h\",max_output_tokens:3500}));\n" } ### Tool output · 2026-09-23T05:19:54.812Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"8b3f2b","wall_time_seconds":1.001506799,"session_id":5798,"original_token_count":0,"output":""} {"chunk_id":"8009f8","wall_time_seconds":0.00000371,"exit_code":127,"original_token_count":1626,"output":"def 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 )\ntotal 108\ndrwxr-xr-x 1 agent agent 4096 Sep 23 05:19 .\ndrwxr-xr-x 1 agent agent 4096 Sep 23 05:16 ..\n-rw-r--r-- 1 agent agent 0 Sep 23 05:19 production.json\n-rw-r--r-- 1 agent agent 0 Sep 23 05:19 production.log\n-r--r--r-- 1 agent agent 80058 Sep 23 05:19 train_current_model.py\n-r--r--r-- 1 agent agent 15985 Sep 23 05:19 two_tower_model.py\n8\n/bin/sh: 1: free: not found\n"} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "session_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "root_turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "response_id": "resp_03ec3525bea3ac86016ab36172de8487d2b6ca9c52550d039a", "usage": { "input_tokens": 41457, "cached_input_tokens": 40064, "cache_write_input_tokens": 0, "output_tokens": 152, "reasoning_output_tokens": 0, "total_tokens": 41609 }, "turn_token_usage": { "input_tokens": 157127, "cached_input_tokens": 127104, "cache_write_input_tokens": 0, "output_tokens": 575, "reasoning_output_tokens": 0, "total_tokens": 157702 }, "thread_token_usage": { "input_tokens": 157127, "cached_input_tokens": 127104, "cache_write_input_tokens": 0, "output_tokens": 575, "reasoning_output_tokens": 0, "total_tokens": 157702 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-bd5e815b-faba-4450-a9f6-43984bf8fe8a", "process_id": "28108", "command": [ "/bin/sh", "-lc", "sed -n '1521,1680p' /app/solution/train_current_model.py && ls -la /app/solution && nproc && free -h" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "sed -n '1521,1680p' /app/solution/train_current_model.py && ls -la /app/solution && nproc && free -h" } ], "source": "unified_exec_startup", "status": "failed", "stdout": "def 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 )\ntotal 108\ndrwxr-xr-x 1 agent agent 4096 Sep 23 05:19 .\ndrwxr-xr-x 1 agent agent 4096 Sep 23 05:16 ..\n-rw-r--r-- 1 agent agent 0 Sep 23 05:19 production.json\n-rw-r--r-- 1 agent agent 0 Sep 23 05:19 production.log\n-r--r--r-- 1 agent agent 80058 Sep 23 05:19 train_current_model.py\n-r--r--r-- 1 agent agent 15985 Sep 23 05:19 two_tower_model.py\n8\n/bin/sh: 1: free: not found\n", "stderr": "", "aggregated_output": "def 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 )\ntotal 108\ndrwxr-xr-x 1 agent agent 4096 Sep 23 05:19 .\ndrwxr-xr-x 1 agent agent 4096 Sep 23 05:16 ..\n-rw-r--r-- 1 agent agent 0 Sep 23 05:19 production.json\n-rw-r--r-- 1 agent agent 0 Sep 23 05:19 production.log\n-r--r--r-- 1 agent agent 80058 Sep 23 05:19 train_current_model.py\n-r--r--r-- 1 agent agent 15985 Sep 23 05:19 two_tower_model.py\n8\n/bin/sh: 1: free: not found\n", "exit_code": 127, "duration": { "secs": 0, "nanos": 3710 }, "formatted_output": "def 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 )\ntotal 108\ndrwxr-xr-x 1 agent agent 4096 Sep 23 05:19 .\ndrwxr-xr-x 1 agent agent 4096 Sep 23 05:16 ..\n-rw-r--r-- 1 agent agent 0 Sep 23 05:19 production.json\n-rw-r--r-- 1 agent agent 0 Sep 23 05:19 production.log\n-r--r--r-- 1 agent agent 80058 Sep 23 05:19 train_current_model.py\n-r--r--r-- 1 agent agent 15985 Sep 23 05:19 two_tower_model.py\n8\n/bin/sh: 1: free: not found\n" } } ## Step 8 · agent · 2026-09-23T05:20:00.583Z ### Thinking unavailable — no text recorded The baseline already filters known positives, but its negatives are sampled without considering the model’s scores. That may spend most of the training effort on easy comparisons rather than items competing for the top 10. I’ll test a mix of random and model-selected hard negatives while preserving the chronological split and dot-product serving path. ### exec · 2026-09-23T05:20:05.093Z { "code": "text(await tools.exec_command({cmd:\"tail -n 8 /app/solution/production.log && sed -n '1364,1488p' /app/solution/train_current_model.py && sed -n '867,954p' /app/solution/train_current_model.py && cat /sys/fs/cgroup/memory.max && cat /sys/fs/cgroup/cpu.max\",max_output_tokens:4000}));\n" } ### Tool output · 2026-09-23T05:20:05.142Z Script completed Wall time 0.0 seconds Output: {"chunk_id":"13f137","wall_time_seconds":0.00000408,"exit_code":0,"original_token_count":2023,"output":"loaded explicit events: rows=720,270 users=8,220\nprepared users=1,000 tokens=2,090,746 states=35,727 elapsed=4.9s\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]:\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\n8589934592\n800000 100000\n"} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "session_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "root_turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "response_id": "resp_03ec3525bea3ac86016ab3617af58c87d2ac4940d8d2fc56c3", "usage": { "input_tokens": 43260, "cached_input_tokens": 41216, "cache_write_input_tokens": 0, "output_tokens": 297, "reasoning_output_tokens": 129, "total_tokens": 43557 }, "turn_token_usage": { "input_tokens": 200387, "cached_input_tokens": 168320, "cache_write_input_tokens": 0, "output_tokens": 872, "reasoning_output_tokens": 129, "total_tokens": 201259 }, "thread_token_usage": { "input_tokens": 200387, "cached_input_tokens": 168320, "cache_write_input_tokens": 0, "output_tokens": 872, "reasoning_output_tokens": 129, "total_tokens": 201259 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-f9df1513-c3f2-445b-ac99-6ed4080493ab", "process_id": "45436", "command": [ "/bin/sh", "-lc", "tail -n 8 /app/solution/production.log && sed -n '1364,1488p' /app/solution/train_current_model.py && sed -n '867,954p' /app/solution/train_current_model.py && cat /sys/fs/cgroup/memory.max && cat /sys/fs/cgroup/cpu.max" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "read", "cmd": "tail -n 8 /app/solution/production.log", "name": "production.log", "path": "/app/solution/production.log" }, { "type": "read", "cmd": "sed -n '1364,1488p' /app/solution/train_current_model.py", "name": "train_current_model.py", "path": "/app/solution/train_current_model.py" }, { "type": "read", "cmd": "sed -n '867,954p' /app/solution/train_current_model.py", "name": "train_current_model.py", "path": "/app/solution/train_current_model.py" }, { "type": "read", "cmd": "cat /sys/fs/cgroup/memory.max", "name": "memory.max", "path": "/sys/fs/cgroup/memory.max" }, { "type": "read", "cmd": "cat /sys/fs/cgroup/cpu.max", "name": "cpu.max", "path": "/sys/fs/cgroup/cpu.max" } ], "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=4.9s\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]:\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\n8589934592\n800000 100000\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=4.9s\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]:\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\n8589934592\n800000 100000\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4080 }, "formatted_output": "loaded explicit events: rows=720,270 users=8,220\nprepared users=1,000 tokens=2,090,746 states=35,727 elapsed=4.9s\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]:\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\n8589934592\n800000 100000\n" } } ## Step 9 · agent · 2026-09-23T05:20:18.007Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T05:20:19.600Z { "code": "text(await tools.exec_command({cmd:\"tail -n 8 /app/solution/production.log && ls -lh /app/solution/validation_cache.npz\",max_output_tokens:1000}));\ntext(await tools.clock__curr_time({}));\n" } ### Tool output · 2026-09-23T05:20:19.647Z Script completed Wall time 0.0 seconds Output: {"chunk_id":"53205c","wall_time_seconds":0.00000419,"exit_code":2,"original_token_count":82,"output":"loaded explicit events: rows=720,270 users=8,220\nprepared users=1,000 tokens=2,090,746 states=35,727 elapsed=4.9s\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\nls: cannot access '/app/solution/validation_cache.npz': No such file or directory\n"} {"current_time":"2026-09-23 05:20:19 UTC"} ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-b8c3af5e-05e4-40b9-9820-e076fe64aa14", "process_id": "17308", "command": [ "/bin/sh", "-lc", "tail -n 8 /app/solution/production.log && ls -lh /app/solution/validation_cache.npz" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "read", "cmd": "tail -n 8 /app/solution/production.log", "name": "production.log", "path": "/app/solution/production.log" }, { "type": "list_files", "cmd": "ls -lh /app/solution/validation_cache.npz", "path": "validation_cache.npz" } ], "source": "unified_exec_startup", "status": "failed", "stdout": "loaded explicit events: rows=720,270 users=8,220\nprepared users=1,000 tokens=2,090,746 states=35,727 elapsed=4.9s\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\nls: cannot access '/app/solution/validation_cache.npz': No such file or directory\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=4.9s\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\nls: cannot access '/app/solution/validation_cache.npz': No such file or directory\n", "exit_code": 2, "duration": { "secs": 0, "nanos": 4190 }, "formatted_output": "loaded explicit events: rows=720,270 users=8,220\nprepared users=1,000 tokens=2,090,746 states=35,727 elapsed=4.9s\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\nls: cannot access '/app/solution/validation_cache.npz': No such file or directory\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "session_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "root_turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "response_id": "resp_03ec3525bea3ac86016ab3618578b487d2b6ecd16b4b54af74", "usage": { "input_tokens": 45625, "cached_input_tokens": 43136, "cache_write_input_tokens": 0, "output_tokens": 397, "reasoning_output_tokens": 335, "total_tokens": 46022 }, "turn_token_usage": { "input_tokens": 246012, "cached_input_tokens": 211456, "cache_write_input_tokens": 0, "output_tokens": 1269, "reasoning_output_tokens": 464, "total_tokens": 247281 }, "thread_token_usage": { "input_tokens": 246012, "cached_input_tokens": 211456, "cache_write_input_tokens": 0, "output_tokens": 1269, "reasoning_output_tokens": 464, "total_tokens": 247281 } } ## Step 10 · agent · 2026-09-23T05:20:25.742Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T05:20:44.655Z { "code": "text(await tools.exec_command({cmd:\"chmod u+w /app/solution/train_current_model.py /app/solution/two_tower_model.py\",max_output_tokens:100}));\ntext(await tools.apply_patch(\"*** Begin Patch\\n*** Update File: /app/solution/train_current_model.py\\n@@\\n inbatch_correction_max: float = 4.0\\n+ hard_negatives: int = 16\\n+ hard_pool_size: int = 1024\\n+ hard_negative_weight: float = 0.5\\n@@\\n def score_target_microbatches(\\n@@\\n backward: bool,\\n+ user_rows: np.ndarray | None = None,\\n+ known_positive_packed: np.ndarray | None = None,\\n@@\\n query_vectors = user_queries(\\n@@\\n torch.from_numpy(numeric).to(device),\\n )\\n+ selected_negatives = negatives[begin:end]\\n+ if config.hard_negatives:\\n+ hard_items = mine_hard_negatives(\\n+ model, query_vectors, positive_items[begin:end],\\n+ selected_negatives, prepared.liked_states[selected_states],\\n+ user_rows[begin:end], known_positive_packed, config,\\n+ )\\n+ selected_negatives = torch.cat([selected_negatives, hard_items], dim=1)\\n positive_vectors, negative_vectors = candidate_vectors(\\n model,\\n positive_items[begin:end],\\n- negatives[begin:end],\\n+ selected_negatives,\\n@@\\n negative_loss = corrected_negative_loss(\\n- negative_logits, negative_weights[begin:end], config\\n+ negative_logits[:, :negatives.shape[1]], negative_weights[begin:end], config\\n )\\n+ if config.hard_negatives:\\n+ hard_loss = F.softplus(negative_logits[:, negatives.shape[1]:]).mean(dim=1)\\n+ negative_loss = ((1.0 - config.hard_negative_weight) * negative_loss\\n+ + config.hard_negative_weight * hard_loss)\\n@@\\n- statistics[\\\"negative_sum\\\"] += float(negative_logits.detach().sum().cpu())\\n+ sampled_logits = negative_logits[:, :negatives.shape[1]].detach()\\n+ statistics[\\\"negative_sum\\\"] += float(sampled_logits.sum().cpu())\\n statistics[\\\"pair_correct\\\"] += float(\\n- (positive_logits.detach()[:, None] > negative_logits.detach()).sum().cpu()\\n+ (positive_logits.detach()[:, None] > sampled_logits).sum().cpu()\\n@@\\n backward=True,\\n+ user_rows=flat_user_rows,\\n+ known_positive_packed=known_positive_packed,\\n@@\\n backward=False,\\n+ user_rows=flat_user_rows,\\n+ known_positive_packed=known_positive_packed,\\n*** End Patch\"));\n" } ### Tool output · 2026-09-23T05:20:44.706Z Script completed Wall time 0.0 seconds Output: {"chunk_id":"c353e3","wall_time_seconds":0.00000391,"exit_code":0,"original_token_count":0,"output":""} {} ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-e6e58060-3174-442a-8c17-c72eea955594", "process_id": "73388", "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": 3910 }, "formatted_output": "" } } ### Harness context { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-4ae9631f-17a1-4161-a66f-e18a7351ff1f", "changes": { "/app/solution/train_current_model.py": { "type": "update", "unified_diff": "@@ -60,2 +60,5 @@\n inbatch_correction_max: float = 4.0\n+ hard_negatives: int = 16\n+ hard_pool_size: int = 1024\n+ hard_negative_weight: float = 0.5\n negative_filter_scope: str = \"full-training-history\"\n@@ -1123,2 +1126,4 @@\n backward: bool,\n+ user_rows: np.ndarray | None = None,\n+ known_positive_packed: np.ndarray | None = None,\n ) -> tuple[dict[str, float], torch.Tensor | None]:\n@@ -1161,2 +1166,10 @@\n )\n+ selected_negatives = negatives[begin:end]\n+ if config.hard_negatives:\n+ hard_items = mine_hard_negatives(\n+ model, query_vectors, positive_items[begin:end],\n+ selected_negatives, prepared.liked_states[selected_states],\n+ user_rows[begin:end], known_positive_packed, config,\n+ )\n+ selected_negatives = torch.cat([selected_negatives, hard_items], dim=1)\n positive_vectors, negative_vectors = candidate_vectors(\n@@ -1164,3 +1177,3 @@\n positive_items[begin:end],\n- negatives[begin:end],\n+ selected_negatives,\n )\n@@ -1172,4 +1185,8 @@\n negative_loss = corrected_negative_loss(\n- negative_logits, negative_weights[begin:end], config\n+ negative_logits[:, :negatives.shape[1]], negative_weights[begin:end], config\n )\n+ if config.hard_negatives:\n+ hard_loss = F.softplus(negative_logits[:, negatives.shape[1]:]).mean(dim=1)\n+ negative_loss = ((1.0 - config.hard_negative_weight) * negative_loss\n+ + config.hard_negative_weight * hard_loss)\n selected_weights = torch.from_numpy(\n@@ -1189,5 +1206,6 @@\n statistics[\"positive_sum\"] += float(positive_logits.detach().sum().cpu())\n- statistics[\"negative_sum\"] += float(negative_logits.detach().sum().cpu())\n+ sampled_logits = negative_logits[:, :negatives.shape[1]].detach()\n+ statistics[\"negative_sum\"] += float(sampled_logits.sum().cpu())\n statistics[\"pair_correct\"] += float(\n- (positive_logits.detach()[:, None] > negative_logits.detach()).sum().cpu()\n+ (positive_logits.detach()[:, None] > sampled_logits).sum().cpu()\n )\n@@ -1318,2 +1336,4 @@\n backward=True,\n+ user_rows=flat_user_rows,\n+ known_positive_packed=known_positive_packed,\n )\n@@ -1473,2 +1493,4 @@\n backward=False,\n+ user_rows=flat_user_rows,\n+ known_positive_packed=known_positive_packed,\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": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "session_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "root_turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "response_id": "resp_03ec3525bea3ac86016ab36193e44887d2b75ddfa1e016a635", "usage": { "input_tokens": 46205, "cached_input_tokens": 45440, "cache_write_input_tokens": 0, "output_tokens": 664, "reasoning_output_tokens": 24, "total_tokens": 46869 }, "turn_token_usage": { "input_tokens": 292217, "cached_input_tokens": 256896, "cache_write_input_tokens": 0, "output_tokens": 1933, "reasoning_output_tokens": 488, "total_tokens": 294150 }, "thread_token_usage": { "input_tokens": 292217, "cached_input_tokens": 256896, "cache_write_input_tokens": 0, "output_tokens": 1933, "reasoning_output_tokens": 488, "total_tokens": 294150 } } ## Step 11 · agent · 2026-09-23T05:20:51.455Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T05:21:49.092Z { "code": "text(await tools.apply_patch(\"*** Begin Patch\\n*** Update File: /app/solution/train_current_model.py\\n@@\\n-def score_target_microbatches(\\n+@torch.no_grad()\\n+def mine_hard_negatives(\\n+ model: base.SequentialTwoTower,\\n+ queries: torch.Tensor,\\n+ positives: torch.Tensor,\\n+ random_negatives: torch.Tensor,\\n+ liked_items: np.ndarray,\\n+ user_rows: np.ndarray,\\n+ known_positive_packed: np.ndarray,\\n+ config: Config,\\n+) -> torch.Tensor:\\n+ \\\"\\\"\\\"Mine a shared pool, then backpropagate only through the retained items.\\n+\\n+ The pool mixes uniform catalog coverage and current positive targets. Its\\n+ deterministic seed makes chronological validation repeatable. Mining is a\\n+ training operation only; inference remains a single dot product.\\n+ \\\"\\\"\\\"\\n+ if known_positive_packed is None:\\n+ raise ValueError(\\\"hard mining requires the full-training-history filter\\\")\\n+ seed = config.seed + int(positives.sum()) + int(user_rows[0])\\n+ generator = torch.Generator(device=\\\"cpu\\\").manual_seed(seed)\\n+ uniform = torch.randperm(model.n_items, generator=generator)[:config.hard_pool_size]\\n+ batch_pool = torch.unique(positives.cpu())\\n+ if len(batch_pool) > config.hard_pool_size:\\n+ batch_pool = batch_pool[torch.randperm(len(batch_pool), generator=generator)[:config.hard_pool_size]]\\n+ pool = torch.unique(torch.cat([uniform, batch_pool])).numpy()\\n+ invalid = ((known_positive_packed[user_rows[:, None], pool[None, :] >> 3]\\n+ >> (pool[None, :] & 7)) & 1).astype(bool)\\n+ excluded = np.concatenate([\\n+ positives.cpu().numpy()[:, None], random_negatives.cpu().numpy(), liked_items\\n+ ], axis=1)\\n+ columns = np.searchsorted(pool, excluded)\\n+ safe_columns = np.minimum(columns, len(pool) - 1)\\n+ present = (columns < len(pool)) & (pool[safe_columns] == excluded)\\n+ rows = np.broadcast_to(np.arange(len(queries))[:, None], excluded.shape)\\n+ invalid[rows[present], safe_columns[present]] = True\\n+ if np.any((~invalid).sum(axis=1) < config.hard_negatives):\\n+ raise RuntimeError(\\\"hard pool has insufficient eligible negatives\\\")\\n+ pool_items = torch.from_numpy(pool).to(queries.device)\\n+ was_training = model.training\\n+ model.eval()\\n+ vectors = model.encode_items(pool_items)\\n+ model.train(was_training)\\n+ scores = queries.detach() @ vectors.T\\n+ scores.masked_fill_(torch.from_numpy(invalid).to(scores.device), -torch.inf)\\n+ columns = scores.topk(config.hard_negatives, dim=1).indices\\n+ return pool_items[columns]\\n+\\n+\\n+def score_target_microbatches(\\n@@\\n parser.add_argument(\\\"--learning-rate\\\", type=float, default=Config.learning_rate)\\n+ parser.add_argument(\\\"--hard-negatives\\\", type=int, default=Config.hard_negatives)\\n+ parser.add_argument(\\\"--hard-pool-size\\\", type=int, default=Config.hard_pool_size)\\n+ parser.add_argument(\\\"--hard-negative-weight\\\", type=float, default=Config.hard_negative_weight)\\n@@\\n config = Config(\\n@@\\n learning_rate=args.learning_rate,\\n+ hard_negatives=args.hard_negatives,\\n+ hard_pool_size=args.hard_pool_size,\\n+ hard_negative_weight=args.hard_negative_weight,\\n*** End Patch\"));\ntext(await tools.exec_command({cmd:\"tail -n 6 /app/solution/production.log\",max_output_tokens:1000}));\n" } ### Tool output · 2026-09-23T05:21:49.243Z Script completed Wall time 0.1 seconds Output: {} {"chunk_id":"c784c2","wall_time_seconds":0.00000582,"exit_code":0,"original_token_count":133,"output":"prepared 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_cache.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\n"} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "session_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "root_turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "response_id": "resp_03ec3525bea3ac86016ab361ce32cc87d2ae72384616e14d7e", "usage": { "input_tokens": 47079, "cached_input_tokens": 46720, "cache_write_input_tokens": 0, "output_tokens": 882, "reasoning_output_tokens": 0, "total_tokens": 47961 }, "turn_token_usage": { "input_tokens": 339296, "cached_input_tokens": 303616, "cache_write_input_tokens": 0, "output_tokens": 2815, "reasoning_output_tokens": 488, "total_tokens": 342111 }, "thread_token_usage": { "input_tokens": 339296, "cached_input_tokens": 303616, "cache_write_input_tokens": 0, "output_tokens": 2815, "reasoning_output_tokens": 488, "total_tokens": 342111 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-e191cd78-62c7-45ed-a4dc-27e38e183daf", "changes": { "/app/solution/train_current_model.py": { "type": "update", "unified_diff": "@@ -1113,2 +1113,51 @@\n \n+@torch.no_grad()\n+def mine_hard_negatives(\n+ model: base.SequentialTwoTower,\n+ queries: torch.Tensor,\n+ positives: torch.Tensor,\n+ random_negatives: torch.Tensor,\n+ liked_items: np.ndarray,\n+ user_rows: np.ndarray,\n+ known_positive_packed: np.ndarray,\n+ config: Config,\n+) -> torch.Tensor:\n+ \"\"\"Mine a shared pool, then backpropagate only through the retained items.\n+\n+ The pool mixes uniform catalog coverage and current positive targets. Its\n+ deterministic seed makes chronological validation repeatable. Mining is a\n+ training operation only; inference remains a single dot product.\n+ \"\"\"\n+ if known_positive_packed is None:\n+ raise ValueError(\"hard mining requires the full-training-history filter\")\n+ seed = config.seed + int(positives.sum()) + int(user_rows[0])\n+ generator = torch.Generator(device=\"cpu\").manual_seed(seed)\n+ uniform = torch.randperm(model.n_items, generator=generator)[:config.hard_pool_size]\n+ batch_pool = torch.unique(positives.cpu())\n+ if len(batch_pool) > config.hard_pool_size:\n+ batch_pool = batch_pool[torch.randperm(len(batch_pool), generator=generator)[:config.hard_pool_size]]\n+ pool = torch.unique(torch.cat([uniform, batch_pool])).numpy()\n+ invalid = ((known_positive_packed[user_rows[:, None], pool[None, :] >> 3]\n+ >> (pool[None, :] & 7)) & 1).astype(bool)\n+ excluded = np.concatenate([\n+ positives.cpu().numpy()[:, None], random_negatives.cpu().numpy(), liked_items\n+ ], axis=1)\n+ columns = np.searchsorted(pool, excluded)\n+ safe_columns = np.minimum(columns, len(pool) - 1)\n+ present = (columns < len(pool)) & (pool[safe_columns] == excluded)\n+ rows = np.broadcast_to(np.arange(len(queries))[:, None], excluded.shape)\n+ invalid[rows[present], safe_columns[present]] = True\n+ if np.any((~invalid).sum(axis=1) < config.hard_negatives):\n+ raise RuntimeError(\"hard pool has insufficient eligible negatives\")\n+ pool_items = torch.from_numpy(pool).to(queries.device)\n+ was_training = model.training\n+ model.eval()\n+ vectors = model.encode_items(pool_items)\n+ model.train(was_training)\n+ scores = queries.detach() @ vectors.T\n+ scores.masked_fill_(torch.from_numpy(invalid).to(scores.device), -torch.inf)\n+ columns = scores.topk(config.hard_negatives, dim=1).indices\n+ return pool_items[columns]\n+\n+\n def score_target_microbatches(\n@@ -1959,2 +2008,5 @@\n parser.add_argument(\"--learning-rate\", type=float, default=Config.learning_rate)\n+ parser.add_argument(\"--hard-negatives\", type=int, default=Config.hard_negatives)\n+ parser.add_argument(\"--hard-pool-size\", type=int, default=Config.hard_pool_size)\n+ parser.add_argument(\"--hard-negative-weight\", type=float, default=Config.hard_negative_weight)\n parser.add_argument(\n@@ -2054,2 +2106,5 @@\n learning_rate=args.learning_rate,\n+ hard_negatives=args.hard_negatives,\n+ hard_pool_size=args.hard_pool_size,\n+ hard_negative_weight=args.hard_negative_weight,\n loss_microbatch_size=args.loss_microbatch_size,\n", "move_path": null } }, "status": "completed", "stdout": "Success. Updated the following files:\nM /app/solution/train_current_model.py\n", "stderr": "" } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-1f4f715c-0dba-40e8-8029-690ed648cd95", "process_id": "50070", "command": [ "/bin/sh", "-lc", "tail -n 6 /app/solution/production.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "read", "cmd": "tail -n 6 /app/solution/production.log", "name": "production.log", "path": "/app/solution/production.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "prepared 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_cache.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\n", "stderr": "", "aggregated_output": "prepared 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_cache.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\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 5820 }, "formatted_output": "prepared 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_cache.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\n" } } ## Step 12 · agent · 2026-09-23T05:21:53.100Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T05:21:57.231Z { "code": "text(await tools.exec_command({cmd:\"/opt/task-venv/bin/python /app/solution/train_current_model.py --stage submission --public-dir /task --output /app/solution/submission.parquet --prepared-cache /app/solution/submission_cache.npz --prepare-only --threads 1 > /app/solution/prepare_submission.json 2> /app/solution/prepare_submission.log\",yield_time_ms:1000}));\ntext(await tools.exec_command({cmd:\"ps -eo pid,etime,pcpu,rss,args | head -n 12 && du -h /app/solution/validation_cache.npz\",max_output_tokens:1500}));\n" } ### Tool output · 2026-09-23T05:21:58.555Z Script completed Wall time 1.3 seconds Output: {"chunk_id":"edb947","wall_time_seconds":1.000637253,"session_id":35811,"original_token_count":0,"output":""} {"chunk_id":"e73066","wall_time_seconds":0.00000587,"exit_code":0,"original_token_count":17,"output":"/bin/sh: 1: ps: not found\n511M\t/app/solution/validation_cache.npz\n"} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "session_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "root_turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "response_id": "resp_03ec3525bea3ac86016ab361ed7c7c87d29131281d2763ed21", "usage": { "input_tokens": 48182, "cached_input_tokens": 46848, "cache_write_input_tokens": 0, "output_tokens": 221, "reasoning_output_tokens": 73, "total_tokens": 48403 }, "turn_token_usage": { "input_tokens": 387478, "cached_input_tokens": 350464, "cache_write_input_tokens": 0, "output_tokens": 3036, "reasoning_output_tokens": 561, "total_tokens": 390514 }, "thread_token_usage": { "input_tokens": 387478, "cached_input_tokens": 350464, "cache_write_input_tokens": 0, "output_tokens": 3036, "reasoning_output_tokens": 561, "total_tokens": 390514 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-6d1ddb8d-3ed8-43ca-9386-427d091e906b", "process_id": "85069", "command": [ "/bin/sh", "-lc", "ps -eo pid,etime,pcpu,rss,args | head -n 12 && du -h /app/solution/validation_cache.npz" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "ps -eo pid,etime,pcpu,rss,args | head -n 12 && du -h /app/solution/validation_cache.npz" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "/bin/sh: 1: ps: not found\n511M\t/app/solution/validation_cache.npz\n", "stderr": "", "aggregated_output": "/bin/sh: 1: ps: not found\n511M\t/app/solution/validation_cache.npz\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 5870 }, "formatted_output": "/bin/sh: 1: ps: not found\n511M\t/app/solution/validation_cache.npz\n" } } ## Step 13 · agent · 2026-09-23T05:22:02.842Z ### Thinking unavailable — no text recorded The baseline is training on 1.05 million recent targets, with 130,103 later targets held out for internal validation. I’ve added a sampler that scores a shared candidate pool and retains the highest-scoring valid negatives. It excludes known positives, active likes, the target, and negatives already sampled for that row. ### exec · 2026-09-23T05:22:37.983Z { "code": "text(await tools.apply_patch(\"*** Begin Patch\\n*** Add File: /app/solution/rank_checkpoint.py\\n+\\\"\\\"\\\"Rank an existing checkpoint, preserving stage-specific observed history.\\\"\\\"\\\"\\n+import argparse\\n+import json\\n+from pathlib import Path\\n+\\n+import numpy as np\\n+import torch\\n+import train_current_model as trainer\\n+\\n+\\n+def main():\\n+ p = argparse.ArgumentParser()\\n+ p.add_argument(\\\"--checkpoint\\\", type=Path, required=True)\\n+ p.add_argument(\\\"--stage\\\", choices=[\\\"validation\\\", \\\"submission\\\"], required=True)\\n+ p.add_argument(\\\"--output\\\", type=Path, required=True)\\n+ p.add_argument(\\\"--threads\\\", type=int, default=8)\\n+ p.add_argument(\\\"--save-vectors\\\", type=Path)\\n+ args = p.parse_args()\\n+ torch.set_num_threads(args.threads)\\n+ torch.set_num_interop_threads(1)\\n+ payload = torch.load(args.checkpoint, map_location=\\\"cpu\\\", weights_only=False)\\n+ config = trainer.Config(**payload[\\\"config\\\"])\\n+ prepared, eligibility = trainer.load_or_prepare(\\n+ Path(\\\"/task\\\"), args.stage, config,\\n+ Path(\\\"/app/solution\\\") / f\\\"{args.stage}_cache.npz\\\",\\n+ )\\n+ model = trainer.base.SequentialTwoTower(prepared.item_features, trainer.model_config(config))\\n+ model.load_state_dict(payload[\\\"model_state\\\"])\\n+ model.eval()\\n+ ranking, diagnostics = trainer.rank_candidates(model, prepared, eligibility, config, torch.device(\\\"cpu\\\"), 64)\\n+ trainer.write_ranking(ranking, args.output)\\n+ if args.save_vectors:\\n+ with torch.inference_mode():\\n+ queries = trainer.inference_queries(model, prepared, config, torch.device(\\\"cpu\\\"))\\n+ items = torch.cat([model.encode_items(ids) for ids in torch.arange(model.n_items).split(2048)])\\n+ np.savez(args.save_vectors, queries=queries.numpy(), items=items.numpy(), uids=eligibility.uids, item_ids=eligibility.item_ids)\\n+ print(json.dumps(diagnostics))\\n+\\n+\\n+if __name__ == \\\"__main__\\\":\\n+ main()\\n*** Add File: /app/solution/test_mining.py\\n+\\\"\\\"\\\"Small invariant test for the score-aware training sampler.\\\"\\\"\\\"\\n+import numpy as np\\n+import torch\\n+import train_current_model as t\\n+\\n+torch.set_num_threads(1)\\n+t.seed_everything(3)\\n+config = t.Config(hard_negatives=8, hard_pool_size=100)\\n+model = t.base.SequentialTwoTower(np.zeros((100, 4), dtype=np.float32), t.model_config(config))\\n+model.train()\\n+queries = torch.randn(2, 192)\\n+positives = torch.tensor([1, 2])\\n+sampled = torch.tensor([[3, 4], [5, 6]])\\n+liked = np.array([[7, 100], [8, 100]])\\n+mask = np.zeros((2, 100), dtype=np.uint8)\\n+mask[0, :20] = 1\\n+mask[1, 20:40] = 1\\n+packed = np.packbits(mask, axis=1, bitorder=\\\"little\\\")\\n+mined = t.mine_hard_negatives(model, queries, positives, sampled, liked, np.arange(2), packed, config)\\n+assert model.training\\n+assert mined.shape == (2, 8)\\n+assert torch.equal(mined, t.mine_hard_negatives(model, queries, positives, sampled, liked, np.arange(2), packed, config))\\n+for r in range(2):\\n+ assert len(set(mined[r].tolist())) == 8\\n+ assert not mask[r, mined[r].numpy()].any()\\n+ assert positives[r].item() not in mined[r]\\n+ assert not set(mined[r].tolist()).intersection(sampled[r].tolist() + liked[r].tolist())\\n+ model.eval()\\n+ with torch.no_grad():\\n+ scores = queries[r] @ model.encode_items(torch.arange(100)).T\\n+ excluded = mask[r].astype(bool)\\n+ excluded[positives[r]] = True\\n+ excluded[sampled[r]] = True\\n+ excluded[liked[r][liked[r] < 100]] = True\\n+ scores[torch.from_numpy(excluded)] = -torch.inf\\n+ assert torch.equal(scores.topk(8).indices, mined[r])\\n+print(\\\"hard-mining filtering, uniqueness, determinism, and top-k checks passed\\\")\\n*** End Patch\"));\ntext(await tools.exec_command({cmd:\"/opt/task-venv/bin/python /app/solution/test_mining.py && tail -n 4 /app/solution/production.log && tail -n 3 /app/solution/prepare_submission.log\",yield_time_ms:1000,max_output_tokens:1000}));\n" } ### Tool output · 2026-09-23T05:22:39.298Z Script completed Wall time 1.3 seconds Output: {} {"chunk_id":"6a3c33","wall_time_seconds":1.000539293,"session_id":9985,"original_token_count":0,"output":""} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "session_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "root_turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "response_id": "resp_03ec3525bea3ac86016ab361f6c9f487d2ba873d2a9da5fc5f", "usage": { "input_tokens": 48516, "cached_input_tokens": 48000, "cache_write_input_tokens": 0, "output_tokens": 1262, "reasoning_output_tokens": 49, "total_tokens": 49778 }, "turn_token_usage": { "input_tokens": 435994, "cached_input_tokens": 398464, "cache_write_input_tokens": 0, "output_tokens": 4298, "reasoning_output_tokens": 610, "total_tokens": 440292 }, "thread_token_usage": { "input_tokens": 435994, "cached_input_tokens": 398464, "cache_write_input_tokens": 0, "output_tokens": 4298, "reasoning_output_tokens": 610, "total_tokens": 440292 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-08cbd761-272c-4543-80e7-46546f926628", "changes": { "/app/solution/test_mining.py": { "type": "add", "content": "\"\"\"Small invariant test for the score-aware training sampler.\"\"\"\nimport numpy as np\nimport torch\nimport train_current_model as t\n\ntorch.set_num_threads(1)\nt.seed_everything(3)\nconfig = t.Config(hard_negatives=8, hard_pool_size=100)\nmodel = t.base.SequentialTwoTower(np.zeros((100, 4), dtype=np.float32), t.model_config(config))\nmodel.train()\nqueries = torch.randn(2, 192)\npositives = torch.tensor([1, 2])\nsampled = torch.tensor([[3, 4], [5, 6]])\nliked = np.array([[7, 100], [8, 100]])\nmask = np.zeros((2, 100), dtype=np.uint8)\nmask[0, :20] = 1\nmask[1, 20:40] = 1\npacked = np.packbits(mask, axis=1, bitorder=\"little\")\nmined = t.mine_hard_negatives(model, queries, positives, sampled, liked, np.arange(2), packed, config)\nassert model.training\nassert mined.shape == (2, 8)\nassert torch.equal(mined, t.mine_hard_negatives(model, queries, positives, sampled, liked, np.arange(2), packed, config))\nfor r in range(2):\n assert len(set(mined[r].tolist())) == 8\n assert not mask[r, mined[r].numpy()].any()\n assert positives[r].item() not in mined[r]\n assert not set(mined[r].tolist()).intersection(sampled[r].tolist() + liked[r].tolist())\n model.eval()\n with torch.no_grad():\n scores = queries[r] @ model.encode_items(torch.arange(100)).T\n excluded = mask[r].astype(bool)\n excluded[positives[r]] = True\n excluded[sampled[r]] = True\n excluded[liked[r][liked[r] < 100]] = True\n scores[torch.from_numpy(excluded)] = -torch.inf\n assert torch.equal(scores.topk(8).indices, mined[r])\nprint(\"hard-mining filtering, uniqueness, determinism, and top-k checks passed\")\n" }, "/app/solution/rank_checkpoint.py": { "type": "add", "content": "\"\"\"Rank an existing checkpoint, preserving stage-specific observed history.\"\"\"\nimport argparse\nimport json\nfrom pathlib import Path\n\nimport numpy as np\nimport torch\nimport train_current_model as trainer\n\n\ndef main():\n p = argparse.ArgumentParser()\n p.add_argument(\"--checkpoint\", type=Path, required=True)\n p.add_argument(\"--stage\", choices=[\"validation\", \"submission\"], required=True)\n p.add_argument(\"--output\", type=Path, required=True)\n p.add_argument(\"--threads\", type=int, default=8)\n p.add_argument(\"--save-vectors\", type=Path)\n args = p.parse_args()\n torch.set_num_threads(args.threads)\n torch.set_num_interop_threads(1)\n payload = torch.load(args.checkpoint, map_location=\"cpu\", weights_only=False)\n config = trainer.Config(**payload[\"config\"])\n prepared, eligibility = trainer.load_or_prepare(\n Path(\"/task\"), args.stage, config,\n Path(\"/app/solution\") / f\"{args.stage}_cache.npz\",\n )\n model = trainer.base.SequentialTwoTower(prepared.item_features, trainer.model_config(config))\n model.load_state_dict(payload[\"model_state\"])\n model.eval()\n ranking, diagnostics = trainer.rank_candidates(model, prepared, eligibility, config, torch.device(\"cpu\"), 64)\n trainer.write_ranking(ranking, args.output)\n if args.save_vectors:\n with torch.inference_mode():\n queries = trainer.inference_queries(model, prepared, config, torch.device(\"cpu\"))\n items = torch.cat([model.encode_items(ids) for ids in torch.arange(model.n_items).split(2048)])\n np.savez(args.save_vectors, queries=queries.numpy(), items=items.numpy(), uids=eligibility.uids, item_ids=eligibility.item_ids)\n print(json.dumps(diagnostics))\n\n\nif __name__ == \"__main__\":\n main()\n" } }, "status": "completed", "stdout": "Success. Updated the following files:\nA /app/solution/rank_checkpoint.py\nA /app/solution/test_mining.py\n", "stderr": "" } } ## Step 14 · agent · 2026-09-23T05:22:42.340Z ### Thinking unavailable — no text recorded ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-f32280f6-1ec9-4385-896e-89c141415b18", "process_id": "9985", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python /app/solution/test_mining.py && tail -n 4 /app/solution/production.log && tail -n 3 /app/solution/prepare_submission.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python /app/solution/test_mining.py && tail -n 4 /app/solution/production.log && tail -n 3 /app/solution/prepare_submission.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "hard-mining filtering, uniqueness, determinism, and top-k checks passed\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\nprepared users=2,000 tokens=4,450,246 states=73,784 elapsed=14.8s\nprepared users=3,000 tokens=6,703,970 states=113,190 elapsed=22.5s\nprepared users=4,000 tokens=9,087,187 states=153,283 elapsed=30.6s\n", "stderr": "", "aggregated_output": "hard-mining filtering, uniqueness, determinism, and top-k checks passed\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\nprepared users=2,000 tokens=4,450,246 states=73,784 elapsed=14.8s\nprepared users=3,000 tokens=6,703,970 states=113,190 elapsed=22.5s\nprepared users=4,000 tokens=9,087,187 states=153,283 elapsed=30.6s\n", "exit_code": 0, "duration": { "secs": 5, "nanos": 723586929 }, "formatted_output": "hard-mining filtering, uniqueness, determinism, and top-k checks passed\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\nprepared users=2,000 tokens=4,450,246 states=73,784 elapsed=14.8s\nprepared users=3,000 tokens=6,703,970 states=113,190 elapsed=22.5s\nprepared users=4,000 tokens=9,087,187 states=153,283 elapsed=30.6s\n" } } ### exec · 2026-09-23T05:22:45.069Z { "code": "text(await tools.write_stdin({session_id:9985,chars:\"\",yield_time_ms:1000,max_output_tokens:1000}));\ntext(await tools.exec_command({cmd:\"tail -n 5 /app/solution/production.log && tail -n 4 /app/solution/prepare_submission.log && cat /sys/fs/cgroup/memory.current\",max_output_tokens:1000}));\ntext(await tools.clock__curr_time({}));\n" } ### Tool output · 2026-09-23T05:22:45.323Z Script completed Wall time 0.2 seconds Output: {"chunk_id":"2bc43e","wall_time_seconds":0.00000265,"exit_code":0,"original_token_count":155,"output":"hard-mining filtering, uniqueness, determinism, and top-k checks passed\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\nprepared users=2,000 tokens=4,450,246 states=73,784 elapsed=14.8s\nprepared users=3,000 tokens=6,703,970 states=113,190 elapsed=22.5s\nprepared users=4,000 tokens=9,087,187 states=153,283 elapsed=30.6s\n"} {"chunk_id":"1f1169","wall_time_seconds":0.00000353,"exit_code":0,"original_token_count":175,"output":"saved public-derived full-sequence cache: /app/solution/validation_cache.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\nprepared users=1,000 tokens=2,167,338 states=37,073 elapsed=7.3s\nprepared users=2,000 tokens=4,450,246 states=73,784 elapsed=14.8s\nprepared users=3,000 tokens=6,703,970 states=113,190 elapsed=22.5s\nprepared users=4,000 tokens=9,087,187 states=153,283 elapsed=30.6s\n5863890944\n"} {"current_time":"2026-09-23 05:22:45 UTC"} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "session_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "root_turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "response_id": "resp_03ec3525bea3ac86016ab3621f83cc87d2bc4cc8b6427e799f", "usage": { "input_tokens": 49838, "cached_input_tokens": 48384, "cache_write_input_tokens": 0, "output_tokens": 135, "reasoning_output_tokens": 33, "total_tokens": 49973 }, "turn_token_usage": { "input_tokens": 485832, "cached_input_tokens": 446848, "cache_write_input_tokens": 0, "output_tokens": 4433, "reasoning_output_tokens": 643, "total_tokens": 490265 }, "thread_token_usage": { "input_tokens": 485832, "cached_input_tokens": 446848, "cache_write_input_tokens": 0, "output_tokens": 4433, "reasoning_output_tokens": 643, "total_tokens": 490265 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-7643e210-b155-40f7-9769-52271b52d23c", "process_id": "50036", "command": [ "/bin/sh", "-lc", "tail -n 5 /app/solution/production.log && tail -n 4 /app/solution/prepare_submission.log && cat /sys/fs/cgroup/memory.current" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "read", "cmd": "tail -n 5 /app/solution/production.log", "name": "production.log", "path": "/app/solution/production.log" }, { "type": "read", "cmd": "tail -n 4 /app/solution/prepare_submission.log", "name": "prepare_submission.log", "path": "/app/solution/prepare_submission.log" }, { "type": "read", "cmd": "cat /sys/fs/cgroup/memory.current", "name": "memory.current", "path": "/sys/fs/cgroup/memory.current" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "saved public-derived full-sequence cache: /app/solution/validation_cache.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\nprepared users=1,000 tokens=2,167,338 states=37,073 elapsed=7.3s\nprepared users=2,000 tokens=4,450,246 states=73,784 elapsed=14.8s\nprepared users=3,000 tokens=6,703,970 states=113,190 elapsed=22.5s\nprepared users=4,000 tokens=9,087,187 states=153,283 elapsed=30.6s\n5863890944\n", "stderr": "", "aggregated_output": "saved public-derived full-sequence cache: /app/solution/validation_cache.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\nprepared users=1,000 tokens=2,167,338 states=37,073 elapsed=7.3s\nprepared users=2,000 tokens=4,450,246 states=73,784 elapsed=14.8s\nprepared users=3,000 tokens=6,703,970 states=113,190 elapsed=22.5s\nprepared users=4,000 tokens=9,087,187 states=153,283 elapsed=30.6s\n5863890944\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 3530 }, "formatted_output": "saved public-derived full-sequence cache: /app/solution/validation_cache.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\nprepared users=1,000 tokens=2,167,338 states=37,073 elapsed=7.3s\nprepared users=2,000 tokens=4,450,246 states=73,784 elapsed=14.8s\nprepared users=3,000 tokens=6,703,970 states=113,190 elapsed=22.5s\nprepared users=4,000 tokens=9,087,187 states=153,283 elapsed=30.6s\n5863890944\n" } } ## Step 15 · agent · 2026-09-23T05:22:48.683Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T05:22:53.271Z { "code": "text(await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\nfrom pathlib import Path\\nfor p in Path('/proc').glob('[0-9]*'):\\n try:\\n c=(p/'cmdline').read_bytes().decode().replace('\\\\x00',' ')\\n if '/task/current_model/train_current_model.py --stage' in c and 'python -' not in c:\\n print(p.name,c)\\n print('\\\\n'.join(x for x in (p/'status').read_text().splitlines() if x.startswith(('VmRSS','Threads'))))\\n except (OSError,UnicodeError): pass\\nPY\",max_output_tokens:1000}));\n" } ### Tool output · 2026-09-23T05:22:53.521Z Script completed Wall time 0.2 seconds Output: {"chunk_id":"fb4636","wall_time_seconds":0.023912818,"exit_code":0,"original_token_count":166,"output":"276 /bin/sh -c /opt/task-venv/bin/python /task/current_model/train_current_model.py --stage validation --public-dir /task --output /app/solution/production_validation.parquet --prepared-cache /app/solution/validation_cache.npz --checkpoint-dir /app/solution/production_ckpt > /app/solution/production.json 2> /app/solution/production.log \nVmRSS:\t 1560 kB\nThreads:\t1\n277 /opt/task-venv/bin/python /task/current_model/train_current_model.py --stage validation --public-dir /task --output /app/solution/production_validation.parquet --prepared-cache /app/solution/validation_cache.npz --checkpoint-dir /app/solution/production_ckpt \nVmRSS:\t 4376748 kB\nThreads:\t29\n"} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "session_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "root_turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "response_id": "resp_03ec3525bea3ac86016ab36225afcc87d2bac3d8bb56d7a426", "usage": { "input_tokens": 50522, "cached_input_tokens": 49664, "cache_write_input_tokens": 0, "output_tokens": 203, "reasoning_output_tokens": 40, "total_tokens": 50725 }, "turn_token_usage": { "input_tokens": 536354, "cached_input_tokens": 496512, "cache_write_input_tokens": 0, "output_tokens": 4636, "reasoning_output_tokens": 683, "total_tokens": 540990 }, "thread_token_usage": { "input_tokens": 536354, "cached_input_tokens": 496512, "cache_write_input_tokens": 0, "output_tokens": 4636, "reasoning_output_tokens": 683, "total_tokens": 540990 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-a7a60edc-72f4-4d5d-9dec-10c252de12af", "process_id": "47443", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nfor p in Path('/proc').glob('[0-9]*'):\n try:\n c=(p/'cmdline').read_bytes().decode().replace('\\x00',' ')\n if '/task/current_model/train_current_model.py --stage' in c and 'python -' not in c:\n print(p.name,c)\n print('\\n'.join(x for x in (p/'status').read_text().splitlines() if x.startswith(('VmRSS','Threads'))))\n except (OSError,UnicodeError): pass\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nfor p in Path('/proc').glob('[0-9]*'):\n try:\n c=(p/'cmdline').read_bytes().decode().replace('\\x00',' ')\n if '/task/current_model/train_current_model.py --stage' in c and 'python -' not in c:\n print(p.name,c)\n print('\\n'.join(x for x in (p/'status').read_text().splitlines() if x.startswith(('VmRSS','Threads'))))\n except (OSError,UnicodeError): pass\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "276 /bin/sh -c /opt/task-venv/bin/python /task/current_model/train_current_model.py --stage validation --public-dir /task --output /app/solution/production_validation.parquet --prepared-cache /app/solution/validation_cache.npz --checkpoint-dir /app/solution/production_ckpt > /app/solution/production.json 2> /app/solution/production.log \nVmRSS:\t 1560 kB\nThreads:\t1\n277 /opt/task-venv/bin/python /task/current_model/train_current_model.py --stage validation --public-dir /task --output /app/solution/production_validation.parquet --prepared-cache /app/solution/validation_cache.npz --checkpoint-dir /app/solution/production_ckpt \nVmRSS:\t 4376748 kB\nThreads:\t29\n", "stderr": "", "aggregated_output": "276 /bin/sh -c /opt/task-venv/bin/python /task/current_model/train_current_model.py --stage validation --public-dir /task --output /app/solution/production_validation.parquet --prepared-cache /app/solution/validation_cache.npz --checkpoint-dir /app/solution/production_ckpt > /app/solution/production.json 2> /app/solution/production.log \nVmRSS:\t 1560 kB\nThreads:\t1\n277 /opt/task-venv/bin/python /task/current_model/train_current_model.py --stage validation --public-dir /task --output /app/solution/production_validation.parquet --prepared-cache /app/solution/validation_cache.npz --checkpoint-dir /app/solution/production_ckpt \nVmRSS:\t 4376748 kB\nThreads:\t29\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 24059882 }, "formatted_output": "276 /bin/sh -c /opt/task-venv/bin/python /task/current_model/train_current_model.py --stage validation --public-dir /task --output /app/solution/production_validation.parquet --prepared-cache /app/solution/validation_cache.npz --checkpoint-dir /app/solution/production_ckpt > /app/solution/production.json 2> /app/solution/production.log \nVmRSS:\t 1560 kB\nThreads:\t1\n277 /opt/task-venv/bin/python /task/current_model/train_current_model.py --stage validation --public-dir /task --output /app/solution/production_validation.parquet --prepared-cache /app/solution/validation_cache.npz --checkpoint-dir /app/solution/production_ckpt \nVmRSS:\t 4376748 kB\nThreads:\t29\n" } } ## Step 16 · agent · 2026-09-23T05:23:05.166Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T05:23:14.913Z { "code": "text(await tools.apply_patch(\"*** Begin Patch\\n*** Update File: /app/solution/train_current_model.py\\n@@\\n- if batches % 250 == 0:\\n+ if batches % 50 == 0:\\n@@\\n objective_changed = (\\n@@\\n or stored_window_values != current_window_values\\n+ or any(stored_config.get(name, default) != getattr(config, name)\\n+ for name, default in ((\\\"hard_negatives\\\", 0),\\n+ (\\\"hard_pool_size\\\", 1024),\\n+ (\\\"hard_negative_weight\\\", 0.5)))\\n@@\\n if not 0.0 < args.validation_fraction < 1.0:\\n parser.error(\\\"validation fraction must be strictly between zero and one\\\")\\n+ if args.hard_negatives < 0 or args.hard_pool_size < max(args.hard_negatives, 1):\\n+ parser.error(\\\"hard pool must be positive and at least hard-negatives\\\")\\n+ if not 0.0 <= args.hard_negative_weight <= 1.0:\\n+ parser.error(\\\"hard-negative-weight must be in [0, 1]\\\")\\n+ if args.hard_negatives and args.negative_filter_scope != \\\"full-training-history\\\":\\n+ parser.error(\\\"hard mining requires full-training-history filtering\\\")\\n*** End Patch\"));\ntext(await tools.exec_command({cmd:\"tail -n 6 /app/solution/production.log && tail -n 3 /app/solution/prepare_submission.log\",max_output_tokens:1000}));\n" } ### Tool output · 2026-09-23T05:23:15.022Z Script completed Wall time 0.1 seconds Output: {} {"chunk_id":"989660","wall_time_seconds":0.000004011,"exit_code":0,"original_token_count":184,"output":"prepared 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_cache.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\nprepared users=6,000 tokens=13,485,786 states=224,384 elapsed=45.9s\nprepared users=7,000 tokens=15,826,114 states=265,318 elapsed=54.1s\nprepared users=8,000 tokens=18,028,622 states=306,474 elapsed=61.9s\n"} ### Harness context { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-2bb4f35e-91ee-4427-995b-f8478d23c59a", "changes": { "/app/solution/train_current_model.py": { "type": "update", "unified_diff": "@@ -1404,3 +1404,3 @@\n release_accelerator_cache(device)\n- if batches % 250 == 0:\n+ if batches % 50 == 0:\n batch_bce = statistics[\"loss_sum\"] / max(\n@@ -1703,2 +1703,6 @@\n or stored_window_values != current_window_values\n+ or any(stored_config.get(name, default) != getattr(config, name)\n+ for name, default in ((\"hard_negatives\", 0),\n+ (\"hard_pool_size\", 1024),\n+ (\"hard_negative_weight\", 0.5)))\n )\n@@ -2098,2 +2102,8 @@\n parser.error(\"validation fraction must be strictly between zero and one\")\n+ if args.hard_negatives < 0 or args.hard_pool_size < max(args.hard_negatives, 1):\n+ parser.error(\"hard pool must be positive and at least hard-negatives\")\n+ if not 0.0 <= args.hard_negative_weight <= 1.0:\n+ parser.error(\"hard-negative-weight must be in [0, 1]\")\n+ if args.hard_negatives and args.negative_filter_scope != \"full-training-history\":\n+ parser.error(\"hard mining requires full-training-history filtering\")\n public = args.public_dir.resolve()\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": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "session_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "root_turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "response_id": "resp_03ec3525bea3ac86016ab3622dbf2c87d2af2cfae19485f2a3", "usage": { "input_tokens": 50957, "cached_input_tokens": 50304, "cache_write_input_tokens": 0, "output_tokens": 668, "reasoning_output_tokens": 309, "total_tokens": 51625 }, "turn_token_usage": { "input_tokens": 587311, "cached_input_tokens": 546816, "cache_write_input_tokens": 0, "output_tokens": 5304, "reasoning_output_tokens": 992, "total_tokens": 592615 }, "thread_token_usage": { "input_tokens": 587311, "cached_input_tokens": 546816, "cache_write_input_tokens": 0, "output_tokens": 5304, "reasoning_output_tokens": 992, "total_tokens": 592615 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-77eaf979-8a07-47ac-883f-abdcd6d751c7", "process_id": "5572", "command": [ "/bin/sh", "-lc", "tail -n 6 /app/solution/production.log && tail -n 3 /app/solution/prepare_submission.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "read", "cmd": "tail -n 6 /app/solution/production.log", "name": "production.log", "path": "/app/solution/production.log" }, { "type": "read", "cmd": "tail -n 3 /app/solution/prepare_submission.log", "name": "prepare_submission.log", "path": "/app/solution/prepare_submission.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "prepared 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_cache.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\nprepared users=6,000 tokens=13,485,786 states=224,384 elapsed=45.9s\nprepared users=7,000 tokens=15,826,114 states=265,318 elapsed=54.1s\nprepared users=8,000 tokens=18,028,622 states=306,474 elapsed=61.9s\n", "stderr": "", "aggregated_output": "prepared 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_cache.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\nprepared users=6,000 tokens=13,485,786 states=224,384 elapsed=45.9s\nprepared users=7,000 tokens=15,826,114 states=265,318 elapsed=54.1s\nprepared users=8,000 tokens=18,028,622 states=306,474 elapsed=61.9s\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4011 }, "formatted_output": "prepared 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_cache.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\nprepared users=6,000 tokens=13,485,786 states=224,384 elapsed=45.9s\nprepared users=7,000 tokens=15,826,114 states=265,318 elapsed=54.1s\nprepared users=8,000 tokens=18,028,622 states=306,474 elapsed=61.9s\n" } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-84ca688b-1180-460b-8946-08564adbba5c", "process_id": "35811", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python /app/solution/train_current_model.py --stage submission --public-dir /task --output /app/solution/submission.parquet --prepared-cache /app/solution/submission_cache.npz --prepare-only --threads 1 > /app/solution/prepare_submission.json 2> /app/solution/prepare_submission.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 --output /app/solution/submission.parquet --prepared-cache /app/solution/submission_cache.npz --prepare-only --threads 1 > /app/solution/prepare_submission.json 2> /app/solution/prepare_submission.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 81, "nanos": 987714706 }, "formatted_output": "" } } ## Step 17 · agent · 2026-09-23T05:23:22.214Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T05:23:24.437Z { "code": "text(await tools.exec_command({cmd:\"sed -n '1550,1590p' /app/solution/train_current_model.py && tail -n 8 /app/solution/production.log && tail -n 3 /app/solution/prepare_submission.log\",max_output_tokens:1000}));\ntext(await tools.clock__curr_time({}));\n" } ### Tool output · 2026-09-23T05:23:24.615Z Script completed Wall time 0.2 seconds Output: {"chunk_id":"07052e","wall_time_seconds":0.00000287,"exit_code":0,"original_token_count":524,"output":" 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\nprepared users=9,000 tokens=19,568,405 states=331,551 elapsed=45.8s\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_cache.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\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_cache.npz\n"} {"current_time":"2026-09-23 05:23:24 UTC"} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "session_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "root_turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "response_id": "resp_03ec3525bea3ac86016ab362432e2487d289b9821a4281b552", "usage": { "input_tokens": 51919, "cached_input_tokens": 50816, "cache_write_input_tokens": 0, "output_tokens": 195, "reasoning_output_tokens": 112, "total_tokens": 52114 }, "turn_token_usage": { "input_tokens": 639230, "cached_input_tokens": 597632, "cache_write_input_tokens": 0, "output_tokens": 5499, "reasoning_output_tokens": 1104, "total_tokens": 644729 }, "thread_token_usage": { "input_tokens": 639230, "cached_input_tokens": 597632, "cache_write_input_tokens": 0, "output_tokens": 5499, "reasoning_output_tokens": 1104, "total_tokens": 644729 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-86d94444-7bba-4005-be7f-2e24acf76237", "process_id": "68774", "command": [ "/bin/sh", "-lc", "sed -n '1550,1590p' /app/solution/train_current_model.py && tail -n 8 /app/solution/production.log && tail -n 3 /app/solution/prepare_submission.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "read", "cmd": "sed -n '1550,1590p' /app/solution/train_current_model.py", "name": "train_current_model.py", "path": "/app/solution/train_current_model.py" }, { "type": "read", "cmd": "tail -n 8 /app/solution/production.log", "name": "production.log", "path": "/app/solution/production.log" }, { "type": "read", "cmd": "tail -n 3 /app/solution/prepare_submission.log", "name": "prepare_submission.log", "path": "/app/solution/prepare_submission.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": " 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\nprepared users=9,000 tokens=19,568,405 states=331,551 elapsed=45.8s\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_cache.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\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_cache.npz\n", "stderr": "", "aggregated_output": " 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\nprepared users=9,000 tokens=19,568,405 states=331,551 elapsed=45.8s\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_cache.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\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_cache.npz\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 2870 }, "formatted_output": " 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\nprepared users=9,000 tokens=19,568,405 states=331,551 elapsed=45.8s\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_cache.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\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_cache.npz\n" } } ## Step 18 · agent · 2026-09-23T05:23:28.143Z ### Thinking unavailable — no text recorded The sampler passed checks for filtering, uniqueness, deterministic selection, and choosing the highest-scoring allowed items. The cutoff-specific data caches are also ready; the submission cache includes validation history and remains separate from validation training. ### exec · 2026-09-23T05:23:33.720Z { "code": "text(await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\nfrom pathlib import Path\\np=Path('/proc/277')\\ns=(p/'stat').read_text().split()\\nprint('baseline CPU seconds:', (int(s[13])+int(s[14]))/100)\\nprint('checkpoint exists:', Path('/app/solution/production_ckpt/epoch-01.pt').exists())\\nPY\",max_output_tokens:1000}));\ntext(await tools.write_stdin({session_id:35811,chars:\"\",yield_time_ms:1000,max_output_tokens:1000}));\n" } ### Tool output · 2026-09-23T05:23:33.879Z Script completed Wall time 0.1 seconds Output: {"chunk_id":"4b4c79","wall_time_seconds":0.000003191,"exit_code":0,"original_token_count":14,"output":"baseline CPU seconds: 989.45\ncheckpoint exists: False\n"} {"chunk_id":"7a8a9f","wall_time_seconds":0.000001449,"exit_code":0,"original_token_count":0,"output":""} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "session_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "root_turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "response_id": "resp_03ec3525bea3ac86016ab3624cdc2c87d2890ea4796f887302", "usage": { "input_tokens": 52767, "cached_input_tokens": 51712, "cache_write_input_tokens": 0, "output_tokens": 233, "reasoning_output_tokens": 40, "total_tokens": 53000 }, "turn_token_usage": { "input_tokens": 691997, "cached_input_tokens": 649344, "cache_write_input_tokens": 0, "output_tokens": 5732, "reasoning_output_tokens": 1144, "total_tokens": 697729 }, "thread_token_usage": { "input_tokens": 691997, "cached_input_tokens": 649344, "cache_write_input_tokens": 0, "output_tokens": 5732, "reasoning_output_tokens": 1144, "total_tokens": 697729 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-10d19c89-4b30-4779-9b58-3f601e327164", "process_id": "16975", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\np=Path('/proc/277')\ns=(p/'stat').read_text().split()\nprint('baseline CPU seconds:', (int(s[13])+int(s[14]))/100)\nprint('checkpoint exists:', Path('/app/solution/production_ckpt/epoch-01.pt').exists())\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\np=Path('/proc/277')\ns=(p/'stat').read_text().split()\nprint('baseline CPU seconds:', (int(s[13])+int(s[14]))/100)\nprint('checkpoint exists:', Path('/app/solution/production_ckpt/epoch-01.pt').exists())\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "baseline CPU seconds: 989.45\ncheckpoint exists: False\n", "stderr": "", "aggregated_output": "baseline CPU seconds: 989.45\ncheckpoint exists: False\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 3191 }, "formatted_output": "baseline CPU seconds: 989.45\ncheckpoint exists: False\n" } } ## Step 19 · agent · 2026-09-23T05:23:47.340Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T05:23:49.766Z { "code": "text(await tools.exec_command({cmd:\"tail -n 6 /app/solution/production.log && cat /sys/fs/cgroup/memory.current\",max_output_tokens:1000}));\ntext(await tools.clock__curr_time({}));\n" } ### Tool output · 2026-09-23T05:23:49.900Z Script completed Wall time 0.1 seconds Output: {"chunk_id":"8470cc","wall_time_seconds":0.000004169,"exit_code":0,"original_token_count":136,"output":"prepared 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_cache.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\n5622009856\n"} {"current_time":"2026-09-23 05:23:49 UTC"} ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-21cfbacf-d40b-4d32-997e-4748219f6ca2", "process_id": "64998", "command": [ "/bin/sh", "-lc", "tail -n 6 /app/solution/production.log && cat /sys/fs/cgroup/memory.current" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "read", "cmd": "tail -n 6 /app/solution/production.log", "name": "production.log", "path": "/app/solution/production.log" }, { "type": "read", "cmd": "cat /sys/fs/cgroup/memory.current", "name": "memory.current", "path": "/sys/fs/cgroup/memory.current" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "prepared 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_cache.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\n5622009856\n", "stderr": "", "aggregated_output": "prepared 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_cache.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\n5622009856\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4169 }, "formatted_output": "prepared 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_cache.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\n5622009856\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "session_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "root_turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "response_id": "resp_03ec3525bea3ac86016ab362566a8087d28fa579878a772ac8", "usage": { "input_tokens": 53107, "cached_input_tokens": 52608, "cache_write_input_tokens": 0, "output_tokens": 344, "reasoning_output_tokens": 285, "total_tokens": 53451 }, "turn_token_usage": { "input_tokens": 745104, "cached_input_tokens": 701952, "cache_write_input_tokens": 0, "output_tokens": 6076, "reasoning_output_tokens": 1429, "total_tokens": 751180 }, "thread_token_usage": { "input_tokens": 745104, "cached_input_tokens": 701952, "cache_write_input_tokens": 0, "output_tokens": 6076, "reasoning_output_tokens": 1429, "total_tokens": 751180 } } ## Step 20 · agent · 2026-09-23T05:24:06.138Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T05:24:08.286Z { "code": "text(await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\nimport pandas as pd\\nimport pyarrow.parquet as pq\\nfrom pathlib import Path\\nfor name in ['target_users','candidate_catalog','train_listens','val_listens']:\\n p=Path('/task/data') / (name+'.parquet')\\n f=pq.ParquetFile(p)\\n print(name, f.metadata.num_rows)\\n if name=='candidate_catalog': print(pd.read_parquet(p).head().to_string(index=False))\\nPY\",yield_time_ms:1000,max_output_tokens:1000}));\n" } ### Tool output · 2026-09-23T05:24:09.468Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"69eb59","wall_time_seconds":1.000820795,"session_id":51994,"original_token_count":0,"output":""} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "session_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "root_turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "response_id": "resp_03ec3525bea3ac86016ab362663c4087d2967d83c8785c9ea0", "usage": { "input_tokens": 53693, "cached_input_tokens": 12160, "cache_write_input_tokens": 0, "output_tokens": 475, "reasoning_output_tokens": 331, "total_tokens": 54168 }, "turn_token_usage": { "input_tokens": 798797, "cached_input_tokens": 714112, "cache_write_input_tokens": 0, "output_tokens": 6551, "reasoning_output_tokens": 1760, "total_tokens": 805348 }, "thread_token_usage": { "input_tokens": 798797, "cached_input_tokens": 714112, "cache_write_input_tokens": 0, "output_tokens": 6551, "reasoning_output_tokens": 1760, "total_tokens": 805348 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-0ce979d0-5fe3-4f09-95c8-67270e5d207b", "process_id": "51994", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd\nimport pyarrow.parquet as pq\nfrom pathlib import Path\nfor name in ['target_users','candidate_catalog','train_listens','val_listens']:\n p=Path('/task/data') / (name+'.parquet')\n f=pq.ParquetFile(p)\n print(name, f.metadata.num_rows)\n if name=='candidate_catalog': print(pd.read_parquet(p).head().to_string(index=False))\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd\nimport pyarrow.parquet as pq\nfrom pathlib import Path\nfor name in ['target_users','candidate_catalog','train_listens','val_listens']:\n p=Path('/task/data') / (name+'.parquet')\n f=pq.ParquetFile(p)\n print(name, f.metadata.num_rows)\n if name=='candidate_catalog': print(pd.read_parquet(p).head().to_string(index=False))\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "target_users 8456\ncandidate_catalog 30000\n item_id positive_listens organic_positive_listens popularity_rank\n 5862961 26615 20620 1\n 6901374 25536 17347 2\n 5635052 24691 17194 3\n 3542184 24075 17699 4\n 9378983 24036 18195 5\ntrain_listens 43484159\nval_listens 1501697\n", "stderr": "", "aggregated_output": "target_users 8456\ncandidate_catalog 30000\n item_id positive_listens organic_positive_listens popularity_rank\n 5862961 26615 20620 1\n 6901374 25536 17347 2\n 5635052 24691 17194 3\n 3542184 24075 17699 4\n 9378983 24036 18195 5\ntrain_listens 43484159\nval_listens 1501697\n", "exit_code": 0, "duration": { "secs": 1, "nanos": 952060359 }, "formatted_output": "target_users 8456\ncandidate_catalog 30000\n item_id positive_listens organic_positive_listens popularity_rank\n 5862961 26615 20620 1\n 6901374 25536 17347 2\n 5635052 24691 17194 3\n 3542184 24075 17699 4\n 9378983 24036 18195 5\ntrain_listens 43484159\nval_listens 1501697\n" } } ## Step 21 · agent · 2026-09-23T05:24:11.741Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T05:24:14.359Z { "code": "text(await tools.write_stdin({session_id:5798,chars:\"\",yield_time_ms:1000,max_output_tokens:1000}));\ntext(await tools.write_stdin({session_id:51994,chars:\"\",yield_time_ms:1000,max_output_tokens:1000}));\ntext(await tools.exec_command({cmd:\"tail -n 3 /app/solution/production.log\",max_output_tokens:1000}));\n" } ### Tool output · 2026-09-23T05:24:19.515Z Script completed Wall time 5.1 seconds Output: {"chunk_id":"4720f0","wall_time_seconds":5.000979123,"session_id":5798,"original_token_count":0,"output":""} {"chunk_id":"cc54f4","wall_time_seconds":0.00000178,"exit_code":0,"original_token_count":127,"output":"target_users 8456\ncandidate_catalog 30000\n item_id positive_listens organic_positive_listens popularity_rank\n 5862961 26615 20620 1\n 6901374 25536 17347 2\n 5635052 24691 17194 3\n 3542184 24075 17699 4\n 9378983 24036 18195 5\ntrain_listens 43484159\nval_listens 1501697\n"} {"chunk_id":"7083cd","wall_time_seconds":0.00000475,"exit_code":0,"original_token_count":55,"output":"inbatch 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\n"} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "session_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "root_turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "response_id": "resp_03ec3525bea3ac86016ab36279b56487d2ac92c5e8f2c86419", "usage": { "input_tokens": 54225, "cached_input_tokens": 53504, "cache_write_input_tokens": 0, "output_tokens": 114, "reasoning_output_tokens": 16, "total_tokens": 54339 }, "turn_token_usage": { "input_tokens": 853022, "cached_input_tokens": 767616, "cache_write_input_tokens": 0, "output_tokens": 6665, "reasoning_output_tokens": 1776, "total_tokens": 859687 }, "thread_token_usage": { "input_tokens": 853022, "cached_input_tokens": 767616, "cache_write_input_tokens": 0, "output_tokens": 6665, "reasoning_output_tokens": 1776, "total_tokens": 859687 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-94e715a7-9817-4ed1-af9f-03828d2b1fec", "process_id": "95421", "command": [ "/bin/sh", "-lc", "tail -n 3 /app/solution/production.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "read", "cmd": "tail -n 3 /app/solution/production.log", "name": "production.log", "path": "/app/solution/production.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "inbatch 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\n", "stderr": "", "aggregated_output": "inbatch 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\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4750 }, "formatted_output": "inbatch 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\n" } } ## Step 22 · agent · 2026-09-23T05:24:22.887Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T05:24:37.739Z { "code": "text(await tools.apply_patch(\"*** Begin Patch\\n*** Add File: /app/solution/validate_submission.py\\n+\\\"\\\"\\\"Validate the final artifact against the supplied test candidate boundary.\\\"\\\"\\\"\\n+from pathlib import Path\\n+import numpy as np\\n+import pandas as pd\\n+\\n+path = Path(\\\"/app/solution/submission.parquet\\\")\\n+assert path.is_file() and not path.is_symlink()\\n+frame = pd.read_parquet(path)[[\\\"uid\\\", \\\"item_id\\\", \\\"rank\\\"]]\\n+for col in frame:\\n+ values = frame[col].to_numpy()\\n+ assert np.isfinite(values).all() and (values == np.floor(values)).all(), col\\n+targets = pd.read_parquet(\\\"/task/data/target_users.parquet\\\").uid.to_numpy()\\n+assert set(frame.uid) == set(targets)\\n+counts = frame.groupby(\\\"uid\\\").agg(rows=(\\\"rank\\\", \\\"size\\\"), items=(\\\"item_id\\\", \\\"nunique\\\"), ranks=(\\\"rank\\\", \\\"nunique\\\"), low=(\\\"rank\\\", \\\"min\\\"), high=(\\\"rank\\\", \\\"max\\\"))\\n+assert (counts[[\\\"rows\\\", \\\"items\\\", \\\"ranks\\\"]] == 100).all().all()\\n+assert (counts.low == 1).all() and (counts.high == 100).all()\\n+with np.load(\\\"/task/data/test_eligible_candidates.npz\\\", allow_pickle=False) as eligible:\\n+ rows = pd.Index(eligible[\\\"uids\\\"]).get_indexer(frame.uid)\\n+ cols = pd.Index(eligible[\\\"item_ids\\\"]).get_indexer(frame.item_id)\\n+ assert (rows >= 0).all() and (cols >= 0).all()\\n+ allowed = (eligible[\\\"packed_eligible\\\"][rows, cols >> 3] >> (cols & 7)) & 1\\n+ assert allowed.all()\\n+print(f\\\"Validated {len(frame):,} rows: exactly 100 distinct eligible ranked tracks for {len(targets):,} users.\\\")\\n*** End Patch\"));\ntext(await tools.exec_command({cmd:\"tail -n 4 /app/solution/production.log\",max_output_tokens:1000}));\ntext(await tools.clock__curr_time({}));\n" } ### Tool output · 2026-09-23T05:24:37.833Z Script completed Wall time 0.1 seconds Output: {} {"chunk_id":"7d5d55","wall_time_seconds":0.00000435,"exit_code":0,"original_token_count":87,"output":"chronological 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\n"} {"current_time":"2026-09-23 05:24:37 UTC"} ### Harness context { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-4bd86a2b-98f9-47b9-b1ac-aa820eca9388", "changes": { "/app/solution/validate_submission.py": { "type": "add", "content": "\"\"\"Validate the final artifact against the supplied test candidate boundary.\"\"\"\nfrom pathlib import Path\nimport numpy as np\nimport pandas as pd\n\npath = Path(\"/app/solution/submission.parquet\")\nassert path.is_file() and not path.is_symlink()\nframe = pd.read_parquet(path)[[\"uid\", \"item_id\", \"rank\"]]\nfor col in frame:\n values = frame[col].to_numpy()\n assert np.isfinite(values).all() and (values == np.floor(values)).all(), col\ntargets = pd.read_parquet(\"/task/data/target_users.parquet\").uid.to_numpy()\nassert set(frame.uid) == set(targets)\ncounts = frame.groupby(\"uid\").agg(rows=(\"rank\", \"size\"), items=(\"item_id\", \"nunique\"), ranks=(\"rank\", \"nunique\"), low=(\"rank\", \"min\"), high=(\"rank\", \"max\"))\nassert (counts[[\"rows\", \"items\", \"ranks\"]] == 100).all().all()\nassert (counts.low == 1).all() and (counts.high == 100).all()\nwith np.load(\"/task/data/test_eligible_candidates.npz\", allow_pickle=False) as eligible:\n rows = pd.Index(eligible[\"uids\"]).get_indexer(frame.uid)\n cols = pd.Index(eligible[\"item_ids\"]).get_indexer(frame.item_id)\n assert (rows >= 0).all() and (cols >= 0).all()\n allowed = (eligible[\"packed_eligible\"][rows, cols >> 3] >> (cols & 7)) & 1\n assert allowed.all()\nprint(f\"Validated {len(frame):,} rows: exactly 100 distinct eligible ranked tracks for {len(targets):,} users.\")\n" } }, "status": "completed", "stdout": "Success. Updated the following files:\nA /app/solution/validate_submission.py\n", "stderr": "" } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-6cc3e967-8fdb-489e-8dc6-313e642d2fe8", "process_id": "4123", "command": [ "/bin/sh", "-lc", "tail -n 4 /app/solution/production.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "read", "cmd": "tail -n 4 /app/solution/production.log", "name": "production.log", "path": "/app/solution/production.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "chronological 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\n", "stderr": "", "aggregated_output": "chronological 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\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4350 }, "formatted_output": "chronological 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\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "session_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "root_turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "response_id": "resp_03ec3525bea3ac86016ab36283b21487d28604fda630217d1b", "usage": { "input_tokens": 54658, "cached_input_tokens": 54016, "cache_write_input_tokens": 0, "output_tokens": 529, "reasoning_output_tokens": 25, "total_tokens": 55187 }, "turn_token_usage": { "input_tokens": 907680, "cached_input_tokens": 821632, "cache_write_input_tokens": 0, "output_tokens": 7194, "reasoning_output_tokens": 1801, "total_tokens": 914874 }, "thread_token_usage": { "input_tokens": 907680, "cached_input_tokens": 821632, "cache_write_input_tokens": 0, "output_tokens": 7194, "reasoning_output_tokens": 1801, "total_tokens": 914874 } } ## Step 23 · agent · 2026-09-23T05:24:41.749Z ### Thinking unavailable — no text recorded The first experiment keeps the model, target weights, and random negatives unchanged, then adds 16 hard negatives from a shared pool. This should make the effect of negative selection easier to assess. I’ve also prepared a separate check for the final file’s user coverage, ranks, uniqueness, and test eligibility. ### sleep · 2026-09-23T05:24:44.176Z { "duration_ms": 40000 } ### Tool output · 2026-09-23T05:25:24.249Z Wall time: 40.0259 seconds Sleep completed. ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "session_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "root_turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "response_id": "resp_03ec3525bea3ac86016ab36296147087d29b6e1fb454f0dc8b", "usage": { "input_tokens": 55377, "cached_input_tokens": 54528, "cache_write_input_tokens": 0, "output_tokens": 163, "reasoning_output_tokens": 74, "total_tokens": 55540 }, "turn_token_usage": { "input_tokens": 963057, "cached_input_tokens": 876160, "cache_write_input_tokens": 0, "output_tokens": 7357, "reasoning_output_tokens": 1875, "total_tokens": 970414 }, "thread_token_usage": { "input_tokens": 963057, "cached_input_tokens": 876160, "cache_write_input_tokens": 0, "output_tokens": 7357, "reasoning_output_tokens": 1875, "total_tokens": 970414 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "Extension", "kind": "clock.sleep", "id": "call_jTLFxvIPtrAk4mvkGB2TXK6U", "durationMs": 40000 } } ### exec · 2026-09-23T05:25:27.327Z { "code": "text(await tools.exec_command({cmd:\"tail -n 6 /app/solution/production.log && ls /app/solution/production_ckpt\",max_output_tokens:1500}));\ntext(await tools.clock__curr_time({}));\n" } ### Tool output · 2026-09-23T05:25:27.423Z Script completed Wall time 0.1 seconds Output: {"chunk_id":"8f30f8","wall_time_seconds":0.00000458,"exit_code":2,"original_token_count":149,"output":"saved public-derived full-sequence cache: /app/solution/validation_cache.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=252.5s\nls: cannot access '/app/solution/production_ckpt': No such file or directory\n"} {"current_time":"2026-09-23 05:25:27 UTC"} ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-f1a7e219-50b3-4af6-b207-7c23e571ba82", "process_id": "2665", "command": [ "/bin/sh", "-lc", "tail -n 6 /app/solution/production.log && ls /app/solution/production_ckpt" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "read", "cmd": "tail -n 6 /app/solution/production.log", "name": "production.log", "path": "/app/solution/production.log" }, { "type": "list_files", "cmd": "ls /app/solution/production_ckpt", "path": "production_ckpt" } ], "source": "unified_exec_startup", "status": "failed", "stdout": "saved public-derived full-sequence cache: /app/solution/validation_cache.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=252.5s\nls: cannot access '/app/solution/production_ckpt': No such file or directory\n", "stderr": "", "aggregated_output": "saved public-derived full-sequence cache: /app/solution/validation_cache.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=252.5s\nls: cannot access '/app/solution/production_ckpt': No such file or directory\n", "exit_code": 2, "duration": { "secs": 0, "nanos": 4580 }, "formatted_output": "saved public-derived full-sequence cache: /app/solution/validation_cache.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=252.5s\nls: cannot access '/app/solution/production_ckpt': No such file or directory\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "session_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "root_turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "response_id": "resp_03ec3525bea3ac86016ab362c47f1c87d29cd785d3f4aa2988", "usage": { "input_tokens": 55563, "cached_input_tokens": 55168, "cache_write_input_tokens": 0, "output_tokens": 57, "reasoning_output_tokens": 0, "total_tokens": 55620 }, "turn_token_usage": { "input_tokens": 1018620, "cached_input_tokens": 931328, "cache_write_input_tokens": 0, "output_tokens": 7414, "reasoning_output_tokens": 1875, "total_tokens": 1026034 }, "thread_token_usage": { "input_tokens": 1018620, "cached_input_tokens": 931328, "cache_write_input_tokens": 0, "output_tokens": 7414, "reasoning_output_tokens": 1875, "total_tokens": 1026034 } } ## Step 24 · agent · 2026-09-23T05:25:35.654Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T05:25:36.801Z { "code": "text(await tools.exec_command({cmd:\"cat /proc/277/status | head -n 25 && tail -n 4 /app/solution/production.log\",max_output_tokens:1500}));\n" } ### Tool output · 2026-09-23T05:25:36.905Z Script completed Wall time 0.1 seconds Output: {"chunk_id":"5f8bc5","wall_time_seconds":0.00000402,"exit_code":0,"original_token_count":194,"output":"Name:\tpython\nUmask:\t0022\nState:\tR (running)\nTgid:\t277\nNgid:\t0\nPid:\t277\nPPid:\t276\nTracerPid:\t0\nUid:\t1000\t1000\t1000\t1000\nGid:\t1000\t1000\t1000\t1000\nFDSize:\t64\nGroups:\t1000 \nNStgid:\t277\nNSpid:\t277\nNSpgid:\t276\nNSsid:\t276\nKthread:\t0\nVmPeak:\t 8157564 kB\nVmSize:\t 7541500 kB\nVmLck:\t 0 kB\nVmPin:\t 0 kB\nVmHWM:\t 5977744 kB\nVmRSS:\t 5320672 kB\nRssAnon:\t 5170548 kB\nRssFile:\t 150124 kB\nepoch=1/1 batches=250 targets=987,417 batch_bce=0.547529 running_bce=0.541614 elapsed=252.5s\nsaved epoch checkpoint: /app/solution/production_ckpt/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=265.3 validation_seconds=15.5\nrestored best validation-BCE checkpoint: epoch=1 loss=0.496636\n"} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "session_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "root_turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "response_id": "resp_03ec3525bea3ac86016ab362c7b40487d2af0adde651b18f53", "usage": { "input_tokens": 55883, "cached_input_tokens": 52992, "cache_write_input_tokens": 0, "output_tokens": 254, "reasoning_output_tokens": 201, "total_tokens": 56137 }, "turn_token_usage": { "input_tokens": 1074503, "cached_input_tokens": 984320, "cache_write_input_tokens": 0, "output_tokens": 7668, "reasoning_output_tokens": 2076, "total_tokens": 1082171 }, "thread_token_usage": { "input_tokens": 1074503, "cached_input_tokens": 984320, "cache_write_input_tokens": 0, "output_tokens": 7668, "reasoning_output_tokens": 2076, "total_tokens": 1082171 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-fa609dc4-bdd3-43be-99fb-fa936e342b9b", "process_id": "31079", "command": [ "/bin/sh", "-lc", "cat /proc/277/status | head -n 25 && tail -n 4 /app/solution/production.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "read", "cmd": "cat /proc/277/status", "name": "status", "path": "/proc/277/status" }, { "type": "read", "cmd": "tail -n 4 /app/solution/production.log", "name": "production.log", "path": "/app/solution/production.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "Name:\tpython\nUmask:\t0022\nState:\tR (running)\nTgid:\t277\nNgid:\t0\nPid:\t277\nPPid:\t276\nTracerPid:\t0\nUid:\t1000\t1000\t1000\t1000\nGid:\t1000\t1000\t1000\t1000\nFDSize:\t64\nGroups:\t1000 \nNStgid:\t277\nNSpid:\t277\nNSpgid:\t276\nNSsid:\t276\nKthread:\t0\nVmPeak:\t 8157564 kB\nVmSize:\t 7541500 kB\nVmLck:\t 0 kB\nVmPin:\t 0 kB\nVmHWM:\t 5977744 kB\nVmRSS:\t 5320672 kB\nRssAnon:\t 5170548 kB\nRssFile:\t 150124 kB\nepoch=1/1 batches=250 targets=987,417 batch_bce=0.547529 running_bce=0.541614 elapsed=252.5s\nsaved epoch checkpoint: /app/solution/production_ckpt/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=265.3 validation_seconds=15.5\nrestored best validation-BCE checkpoint: epoch=1 loss=0.496636\n", "stderr": "", "aggregated_output": "Name:\tpython\nUmask:\t0022\nState:\tR (running)\nTgid:\t277\nNgid:\t0\nPid:\t277\nPPid:\t276\nTracerPid:\t0\nUid:\t1000\t1000\t1000\t1000\nGid:\t1000\t1000\t1000\t1000\nFDSize:\t64\nGroups:\t1000 \nNStgid:\t277\nNSpid:\t277\nNSpgid:\t276\nNSsid:\t276\nKthread:\t0\nVmPeak:\t 8157564 kB\nVmSize:\t 7541500 kB\nVmLck:\t 0 kB\nVmPin:\t 0 kB\nVmHWM:\t 5977744 kB\nVmRSS:\t 5320672 kB\nRssAnon:\t 5170548 kB\nRssFile:\t 150124 kB\nepoch=1/1 batches=250 targets=987,417 batch_bce=0.547529 running_bce=0.541614 elapsed=252.5s\nsaved epoch checkpoint: /app/solution/production_ckpt/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=265.3 validation_seconds=15.5\nrestored best validation-BCE checkpoint: epoch=1 loss=0.496636\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4020 }, "formatted_output": "Name:\tpython\nUmask:\t0022\nState:\tR (running)\nTgid:\t277\nNgid:\t0\nPid:\t277\nPPid:\t276\nTracerPid:\t0\nUid:\t1000\t1000\t1000\t1000\nGid:\t1000\t1000\t1000\t1000\nFDSize:\t64\nGroups:\t1000 \nNStgid:\t277\nNSpid:\t277\nNSpgid:\t276\nNSsid:\t276\nKthread:\t0\nVmPeak:\t 8157564 kB\nVmSize:\t 7541500 kB\nVmLck:\t 0 kB\nVmPin:\t 0 kB\nVmHWM:\t 5977744 kB\nVmRSS:\t 5320672 kB\nRssAnon:\t 5170548 kB\nRssFile:\t 150124 kB\nepoch=1/1 batches=250 targets=987,417 batch_bce=0.547529 running_bce=0.541614 elapsed=252.5s\nsaved epoch checkpoint: /app/solution/production_ckpt/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=265.3 validation_seconds=15.5\nrestored best validation-BCE checkpoint: epoch=1 loss=0.496636\n" } } ## Step 25 · agent · 2026-09-23T05:25:45.732Z ### Thinking unavailable — no text recorded The baseline finished its training pass in about 4½ minutes and is now producing the public ranking. Its pairwise accuracy is about 75.5% against sampled negatives; the public NDCG score will show how well that translates to top-10 discovery. ### exec · 2026-09-23T05:25:49.491Z { "code": "text(await tools.write_stdin({session_id:5798,chars:\"\",yield_time_ms:1000,max_output_tokens:1000}));\ntext(await tools.exec_command({cmd:\"tail -n 5 /app/solution/production.log\",max_output_tokens:1000}));\n" } ### Tool output · 2026-09-23T05:25:54.566Z Script completed Wall time 5.1 seconds Output: {"chunk_id":"c12679","wall_time_seconds":5.000904214,"session_id":5798,"original_token_count":0,"output":""} {"chunk_id":"2caf32","wall_time_seconds":0.00000423,"exit_code":0,"original_token_count":121,"output":"full-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=252.5s\nsaved epoch checkpoint: /app/solution/production_ckpt/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=265.3 validation_seconds=15.5\nrestored best validation-BCE checkpoint: epoch=1 loss=0.496636\n"} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "session_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "root_turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "response_id": "resp_03ec3525bea3ac86016ab362d1314c87d2b82824d504aa47cf", "usage": { "input_tokens": 56536, "cached_input_tokens": 55424, "cache_write_input_tokens": 0, "output_tokens": 341, "reasoning_output_tokens": 212, "total_tokens": 56877 }, "turn_token_usage": { "input_tokens": 1131039, "cached_input_tokens": 1039744, "cache_write_input_tokens": 0, "output_tokens": 8009, "reasoning_output_tokens": 2288, "total_tokens": 1139048 }, "thread_token_usage": { "input_tokens": 1131039, "cached_input_tokens": 1039744, "cache_write_input_tokens": 0, "output_tokens": 8009, "reasoning_output_tokens": 2288, "total_tokens": 1139048 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-c3ab2cf6-0a49-4876-b279-f5952c57d664", "process_id": "8339", "command": [ "/bin/sh", "-lc", "tail -n 5 /app/solution/production.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "read", "cmd": "tail -n 5 /app/solution/production.log", "name": "production.log", "path": "/app/solution/production.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "full-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=252.5s\nsaved epoch checkpoint: /app/solution/production_ckpt/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=265.3 validation_seconds=15.5\nrestored best validation-BCE checkpoint: epoch=1 loss=0.496636\n", "stderr": "", "aggregated_output": "full-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=252.5s\nsaved epoch checkpoint: /app/solution/production_ckpt/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=265.3 validation_seconds=15.5\nrestored best validation-BCE checkpoint: epoch=1 loss=0.496636\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4230 }, "formatted_output": "full-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=252.5s\nsaved epoch checkpoint: /app/solution/production_ckpt/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=265.3 validation_seconds=15.5\nrestored best validation-BCE checkpoint: epoch=1 loss=0.496636\n" } } ## Step 26 · agent · 2026-09-23T05:26:07.921Z ### Thinking unavailable — no text recorded ### sleep · 2026-09-23T05:26:07.985Z { "duration_ms": 30000 } ### Tool output · 2026-09-23T05:26:38.029Z Wall time: 30.0092 seconds Sleep completed. ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "session_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "root_turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "response_id": "resp_03ec3525bea3ac86016ab362e2cd5887d28f78ed340d9b8cb0", "usage": { "input_tokens": 57120, "cached_input_tokens": 56320, "cache_write_input_tokens": 0, "output_tokens": 354, "reasoning_output_tokens": 333, "total_tokens": 57474 }, "turn_token_usage": { "input_tokens": 1188159, "cached_input_tokens": 1096064, "cache_write_input_tokens": 0, "output_tokens": 8363, "reasoning_output_tokens": 2621, "total_tokens": 1196522 }, "thread_token_usage": { "input_tokens": 1188159, "cached_input_tokens": 1096064, "cache_write_input_tokens": 0, "output_tokens": 8363, "reasoning_output_tokens": 2621, "total_tokens": 1196522 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "Extension", "kind": "clock.sleep", "id": "call_V3zSnHW3oCSMvL41TZ2dkele", "durationMs": 30000 } } ### exec · 2026-09-23T05:26:41.404Z { "code": "text(await tools.exec_command({cmd:\"tail -n 5 /app/solution/production.log && ls -lh /app/solution/production_validation.parquet\",max_output_tokens:1000}));\ntext(await tools.clock__curr_time({}));\n" } ### Tool output · 2026-09-23T05:26:41.523Z Script completed Wall time 0.1 seconds Output: {"chunk_id":"1ce3e8","wall_time_seconds":0.00000304,"exit_code":2,"original_token_count":144,"output":"full-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=252.5s\nsaved epoch checkpoint: /app/solution/production_ckpt/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=265.3 validation_seconds=15.5\nrestored best validation-BCE checkpoint: epoch=1 loss=0.496636\nls: cannot access '/app/solution/production_validation.parquet': No such file or directory\n"} {"current_time":"2026-09-23 05:26:41 UTC"} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "session_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "root_turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "response_id": "resp_03ec3525bea3ac86016ab3630e396487d290fb0de9c4f9b732", "usage": { "input_tokens": 57497, "cached_input_tokens": 56960, "cache_write_input_tokens": 0, "output_tokens": 60, "reasoning_output_tokens": 0, "total_tokens": 57557 }, "turn_token_usage": { "input_tokens": 1245656, "cached_input_tokens": 1153024, "cache_write_input_tokens": 0, "output_tokens": 8423, "reasoning_output_tokens": 2621, "total_tokens": 1254079 }, "thread_token_usage": { "input_tokens": 1245656, "cached_input_tokens": 1153024, "cache_write_input_tokens": 0, "output_tokens": 8423, "reasoning_output_tokens": 2621, "total_tokens": 1254079 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-6069d450-dafb-4e14-8dd3-e16411224681", "process_id": "92123", "command": [ "/bin/sh", "-lc", "tail -n 5 /app/solution/production.log && ls -lh /app/solution/production_validation.parquet" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "read", "cmd": "tail -n 5 /app/solution/production.log", "name": "production.log", "path": "/app/solution/production.log" }, { "type": "list_files", "cmd": "ls -lh /app/solution/production_validation.parquet", "path": "production_validation.parquet" } ], "source": "unified_exec_startup", "status": "failed", "stdout": "full-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=252.5s\nsaved epoch checkpoint: /app/solution/production_ckpt/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=265.3 validation_seconds=15.5\nrestored best validation-BCE checkpoint: epoch=1 loss=0.496636\nls: cannot access '/app/solution/production_validation.parquet': No such file or directory\n", "stderr": "", "aggregated_output": "full-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=252.5s\nsaved epoch checkpoint: /app/solution/production_ckpt/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=265.3 validation_seconds=15.5\nrestored best validation-BCE checkpoint: epoch=1 loss=0.496636\nls: cannot access '/app/solution/production_validation.parquet': No such file or directory\n", "exit_code": 2, "duration": { "secs": 0, "nanos": 3040 }, "formatted_output": "full-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=252.5s\nsaved epoch checkpoint: /app/solution/production_ckpt/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=265.3 validation_seconds=15.5\nrestored best validation-BCE checkpoint: epoch=1 loss=0.496636\nls: cannot access '/app/solution/production_validation.parquet': No such file or directory\n" } } ## Step 27 · agent · 2026-09-23T05:26:49.496Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T05:26:53.041Z { "code": "text(await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\nfrom pathlib import Path\\np=Path('/proc/277')\\ns=(p/'stat').read_text().split()\\nprint('baseline process CPU seconds:', round((int(s[13])+int(s[14]))/100,1))\\nprint('checkpoint MB:', round(Path('/app/solution/production_ckpt/best.pt').stat().st_size/1e6,1))\\nPY\",max_output_tokens:1000}));\n" } ### Tool output · 2026-09-23T05:26:53.173Z Script completed Wall time 0.1 seconds Output: {"chunk_id":"560b6d","wall_time_seconds":0.00000216,"exit_code":0,"original_token_count":15,"output":"baseline process CPU seconds: 2293.7\ncheckpoint MB: 151.0\n"} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "session_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "root_turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "response_id": "resp_03ec3525bea3ac86016ab36311b5d487d2ac3d7a80cf4f81eb", "usage": { "input_tokens": 57806, "cached_input_tokens": 55680, "cache_write_input_tokens": 0, "output_tokens": 326, "reasoning_output_tokens": 199, "total_tokens": 58132 }, "turn_token_usage": { "input_tokens": 1303462, "cached_input_tokens": 1208704, "cache_write_input_tokens": 0, "output_tokens": 8749, "reasoning_output_tokens": 2820, "total_tokens": 1312211 }, "thread_token_usage": { "input_tokens": 1303462, "cached_input_tokens": 1208704, "cache_write_input_tokens": 0, "output_tokens": 8749, "reasoning_output_tokens": 2820, "total_tokens": 1312211 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-7fbc3686-ce3e-4d84-ab37-db4717fba8c7", "process_id": "36835", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\np=Path('/proc/277')\ns=(p/'stat').read_text().split()\nprint('baseline process CPU seconds:', round((int(s[13])+int(s[14]))/100,1))\nprint('checkpoint MB:', round(Path('/app/solution/production_ckpt/best.pt').stat().st_size/1e6,1))\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\np=Path('/proc/277')\ns=(p/'stat').read_text().split()\nprint('baseline process CPU seconds:', round((int(s[13])+int(s[14]))/100,1))\nprint('checkpoint MB:', round(Path('/app/solution/production_ckpt/best.pt').stat().st_size/1e6,1))\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "baseline process CPU seconds: 2293.7\ncheckpoint MB: 151.0\n", "stderr": "", "aggregated_output": "baseline process CPU seconds: 2293.7\ncheckpoint MB: 151.0\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 2160 }, "formatted_output": "baseline process CPU seconds: 2293.7\ncheckpoint MB: 151.0\n" } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-172ed956-b562-450e-afdf-ff9ba7d193ee", "process_id": "5798", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python /task/current_model/train_current_model.py --stage validation --public-dir /task --output /app/solution/production_validation.parquet --prepared-cache /app/solution/validation_cache.npz --checkpoint-dir /app/solution/production_ckpt > /app/solution/production.json 2> /app/solution/production.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python /task/current_model/train_current_model.py --stage validation --public-dir /task --output /app/solution/production_validation.parquet --prepared-cache /app/solution/validation_cache.npz --checkpoint-dir /app/solution/production_ckpt > /app/solution/production.json 2> /app/solution/production.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 420, "nanos": 422407564 }, "formatted_output": "" } } ## Step 28 · agent · 2026-09-23T05:27:09.871Z ### Thinking unavailable — no text recorded Full-history ranking is taking longer than the training pass. Once the baseline ranking is scored, I’ll save a valid fallback submission and run the hard-negative experiment. The encoder and serving score remain unchanged. ### sleep · 2026-09-23T05:27:12.081Z { "duration_ms": 40000 } ### Tool output · 2026-09-23T05:27:52.088Z Wall time: 40.0036 seconds Sleep completed. ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "session_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "root_turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "response_id": "resp_03ec3525bea3ac86016ab3631d73dc87d28ce78ac5ca07c018", "usage": { "input_tokens": 58207, "cached_input_tokens": 57600, "cache_write_input_tokens": 0, "output_tokens": 584, "reasoning_output_tokens": 516, "total_tokens": 58791 }, "turn_token_usage": { "input_tokens": 1361669, "cached_input_tokens": 1266304, "cache_write_input_tokens": 0, "output_tokens": 9333, "reasoning_output_tokens": 3336, "total_tokens": 1371002 }, "thread_token_usage": { "input_tokens": 1361669, "cached_input_tokens": 1266304, "cache_write_input_tokens": 0, "output_tokens": 9333, "reasoning_output_tokens": 3336, "total_tokens": 1371002 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "Extension", "kind": "clock.sleep", "id": "call_zFUQOcYee9C6D6qXo5TSlIRd", "durationMs": 40000 } } ### exec · 2026-09-23T05:27:57.532Z { "code": "text(await tools.exec_command({cmd:\"tail -n 6 /app/solution/production.log\",max_output_tokens:1000}));\ntext(await tools.clock__curr_time({}));\n" } ### Tool output · 2026-09-23T05:27:57.605Z Script completed Wall time 0.0 seconds Output: {"chunk_id":"169b1c","wall_time_seconds":0.00000325,"exit_code":0,"original_token_count":38,"output":"ranked 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"} {"current_time":"2026-09-23 05:27:57 UTC"} ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-ff92c2c3-13f6-425e-b1bd-a48bb2c32db9", "process_id": "38916", "command": [ "/bin/sh", "-lc", "tail -n 6 /app/solution/production.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "read", "cmd": "tail -n 6 /app/solution/production.log", "name": "production.log", "path": "/app/solution/production.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "ranked 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", "stderr": "", "aggregated_output": "ranked 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", "exit_code": 0, "duration": { "secs": 0, "nanos": 3250 }, "formatted_output": "ranked 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" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "session_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "root_turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "response_id": "resp_03ec3525bea3ac86016ab3635851b487d2a29733842bc84199", "usage": { "input_tokens": 58814, "cached_input_tokens": 57344, "cache_write_input_tokens": 0, "output_tokens": 47, "reasoning_output_tokens": 0, "total_tokens": 58861 }, "turn_token_usage": { "input_tokens": 1420483, "cached_input_tokens": 1323648, "cache_write_input_tokens": 0, "output_tokens": 9380, "reasoning_output_tokens": 3336, "total_tokens": 1429863 }, "thread_token_usage": { "input_tokens": 1420483, "cached_input_tokens": 1323648, "cache_write_input_tokens": 0, "output_tokens": 9380, "reasoning_output_tokens": 3336, "total_tokens": 1429863 } } ### exec · 2026-09-23T05:28:05.817Z { "code": "text(await tools.write_stdin({session_id:5798,chars:\"\",yield_time_ms:1000,max_output_tokens:1000}));\ntext(await tools.exec_command({cmd:\"/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/production_validation.parquet > /app/solution/production_score.json && cat /app/solution/production_score.json\",yield_time_ms:1000,max_output_tokens:1000}));\ntext(await tools.exec_command({cmd:\"/opt/task-venv/bin/python /app/solution/rank_checkpoint.py --checkpoint /app/solution/production_ckpt/best.pt --stage submission --output /app/solution/submission.parquet --threads 2 > /app/solution/fallback.json 2> /app/solution/fallback.log\",yield_time_ms:1000}));\n" } ### Tool output · 2026-09-23T05:28:08.143Z Script completed Wall time 2.3 seconds Output: {"chunk_id":"e7f8e7","wall_time_seconds":0.00000142,"exit_code":0,"original_token_count":0,"output":""} {"chunk_id":"2b34df","wall_time_seconds":1.000734172,"session_id":90356,"original_token_count":0,"output":""} {"chunk_id":"0ccdb7","wall_time_seconds":1.000589178,"session_id":75932,"original_token_count":0,"output":""} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "session_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "root_turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "response_id": "resp_03ec3525bea3ac86016ab3635f01f087d2a1efb51e4f19c3f2", "usage": { "input_tokens": 59007, "cached_input_tokens": 58624, "cache_write_input_tokens": 0, "output_tokens": 183, "reasoning_output_tokens": 0, "total_tokens": 59190 }, "turn_token_usage": { "input_tokens": 1479490, "cached_input_tokens": 1382272, "cache_write_input_tokens": 0, "output_tokens": 9563, "reasoning_output_tokens": 3336, "total_tokens": 1489053 }, "thread_token_usage": { "input_tokens": 1479490, "cached_input_tokens": 1382272, "cache_write_input_tokens": 0, "output_tokens": 9563, "reasoning_output_tokens": 3336, "total_tokens": 1489053 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-6bd3dcf2-3786-45a0-a2e0-7cd7e197115f", "process_id": "90356", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/production_validation.parquet > /app/solution/production_score.json && cat /app/solution/production_score.json" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/production_validation.parquet > /app/solution/production_score.json && cat /app/solution/production_score.json" } ], "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": 216702103 }, "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 29 · agent · 2026-09-23T05:28:14.432Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T05:28:18.808Z { "code": "text(await tools.exec_command({cmd:\"/opt/task-venv/bin/python /app/solution/train_current_model.py --stage validation --public-dir /task --output /app/solution/hard_validation.parquet --prepared-cache /app/solution/validation_cache.npz --checkpoint-dir /app/solution/hard_ckpt --threads 6 --loss-microbatch-size 4096 > /app/solution/hard.json 2> /app/solution/hard.log\",yield_time_ms:1000}));\ntext(await tools.exec_command({cmd:\"cat /app/solution/production_score.json && tail -n 2 /app/solution/fallback.log\",max_output_tokens:1000}));\n" } ### Tool output · 2026-09-23T05:28:20.014Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"0dff71","wall_time_seconds":1.001112073,"session_id":26601,"original_token_count":0,"output":""} {"chunk_id":"836d0f","wall_time_seconds":0.00000543,"exit_code":0,"original_token_count":51,"output":"{\n \"model\": \"/app/solution/production_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.02128,\n \"quality_users\": 4263\n }\n}\nloaded full-sequence cache: tokens=20,712,971 targets=20,703,818\n"} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "session_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "root_turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "response_id": "resp_03ec3525bea3ac86016ab363685eac87d28c4b5c1d9924babd", "usage": { "input_tokens": 59319, "cached_input_tokens": 58880, "cache_write_input_tokens": 0, "output_tokens": 306, "reasoning_output_tokens": 151, "total_tokens": 59625 }, "turn_token_usage": { "input_tokens": 1538809, "cached_input_tokens": 1441152, "cache_write_input_tokens": 0, "output_tokens": 9869, "reasoning_output_tokens": 3487, "total_tokens": 1548678 }, "thread_token_usage": { "input_tokens": 1538809, "cached_input_tokens": 1441152, "cache_write_input_tokens": 0, "output_tokens": 9869, "reasoning_output_tokens": 3487, "total_tokens": 1548678 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-bd72f42d-adcc-4622-93bb-5dbd8af99f9d", "process_id": "84506", "command": [ "/bin/sh", "-lc", "cat /app/solution/production_score.json && tail -n 2 /app/solution/fallback.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "read", "cmd": "cat /app/solution/production_score.json", "name": "production_score.json", "path": "/app/solution/production_score.json" }, { "type": "read", "cmd": "tail -n 2 /app/solution/fallback.log", "name": "fallback.log", "path": "/app/solution/fallback.log" } ], "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}\nloaded full-sequence cache: tokens=20,712,971 targets=20,703,818\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}\nloaded full-sequence cache: tokens=20,712,971 targets=20,703,818\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 5430 }, "formatted_output": "{\n \"model\": \"/app/solution/production_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.02128,\n \"quality_users\": 4263\n }\n}\nloaded full-sequence cache: tokens=20,712,971 targets=20,703,818\n" } } ## Step 30 · agent · 2026-09-23T05:28:23.074Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T05:28:24.571Z { "code": "text(await tools.exec_command({cmd:\"tail -n 6 /app/solution/hard.log && cat /sys/fs/cgroup/memory.current\",max_output_tokens:1000}));\ntext(await tools.clock__curr_time({}));\n" } ### Tool output · 2026-09-23T05:28:24.646Z Script completed Wall time 0.0 seconds Output: {"chunk_id":"6fb928","wall_time_seconds":0.00000422,"exit_code":0,"original_token_count":51,"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\n3547852800\n"} {"current_time":"2026-09-23 05:28:24 UTC"} ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-e7e5d8e0-718d-4813-b755-9b619359bcd2", "process_id": "25424", "command": [ "/bin/sh", "-lc", "tail -n 6 /app/solution/hard.log && cat /sys/fs/cgroup/memory.current" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "read", "cmd": "tail -n 6 /app/solution/hard.log", "name": "hard.log", "path": "/app/solution/hard.log" }, { "type": "read", "cmd": "cat /sys/fs/cgroup/memory.current", "name": "memory.current", "path": "/sys/fs/cgroup/memory.current" } ], "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\n3547852800\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\n3547852800\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4220 }, "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\n3547852800\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "session_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "root_turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "response_id": "resp_03ec3525bea3ac86016ab3637444c487d295c2ab847f3ebe20", "usage": { "input_tokens": 59790, "cached_input_tokens": 59136, "cache_write_input_tokens": 0, "output_tokens": 114, "reasoning_output_tokens": 55, "total_tokens": 59904 }, "turn_token_usage": { "input_tokens": 1598599, "cached_input_tokens": 1500288, "cache_write_input_tokens": 0, "output_tokens": 9983, "reasoning_output_tokens": 3542, "total_tokens": 1608582 }, "thread_token_usage": { "input_tokens": 1598599, "cached_input_tokens": 1500288, "cache_write_input_tokens": 0, "output_tokens": 9983, "reasoning_output_tokens": 3542, "total_tokens": 1608582 } } ## Step 31 · agent · 2026-09-23T05:28:29.450Z ### Thinking unavailable — no text recorded The production public score is **NDCG@10 = 0.021280** across 4,263 users with relevant outcomes. The hard-negative experiment is running, alongside a separate job that uses the baseline checkpoint and test-cutoff history to save a valid fallback submission. ### exec · 2026-09-23T05:28:36.096Z { "code": "text(await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\nimport pandas as pd\\nr=pd.read_parquet('/app/solution/production_validation.parquet')\\nt=r[r['rank']<=10]\\nc=t.item_id.value_counts()\\nprint('unique top10 tracks:', len(c), 'across', r.uid.nunique(),'users')\\nprint('most common top10 tracks:')\\nprint(c.head(10).to_string())\\nprint('distinct top10 lists:', r[r['rank']<=10].groupby('uid').item_id.agg(tuple).nunique())\\nPY\",yield_time_ms:1000,max_output_tokens:1000}));\n" } ### Tool output · 2026-09-23T05:28:37.281Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"7e2aaf","wall_time_seconds":1.000750782,"session_id":69705,"original_token_count":0,"output":""} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "session_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "root_turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "response_id": "resp_03ec3525bea3ac86016ab3637a20dc87d29c9e51924b3459a1", "usage": { "input_tokens": 60044, "cached_input_tokens": 59648, "cache_write_input_tokens": 0, "output_tokens": 281, "reasoning_output_tokens": 55, "total_tokens": 60325 }, "turn_token_usage": { "input_tokens": 1658643, "cached_input_tokens": 1559936, "cache_write_input_tokens": 0, "output_tokens": 10264, "reasoning_output_tokens": 3597, "total_tokens": 1668907 }, "thread_token_usage": { "input_tokens": 1658643, "cached_input_tokens": 1559936, "cache_write_input_tokens": 0, "output_tokens": 10264, "reasoning_output_tokens": 3597, "total_tokens": 1668907 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-71987509-a705-4049-948e-44ff1684107e", "process_id": "69705", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd\nr=pd.read_parquet('/app/solution/production_validation.parquet')\nt=r[r['rank']<=10]\nc=t.item_id.value_counts()\nprint('unique top10 tracks:', len(c), 'across', r.uid.nunique(),'users')\nprint('most common top10 tracks:')\nprint(c.head(10).to_string())\nprint('distinct top10 lists:', r[r['rank']<=10].groupby('uid').item_id.agg(tuple).nunique())\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd\nr=pd.read_parquet('/app/solution/production_validation.parquet')\nt=r[r['rank']<=10]\nc=t.item_id.value_counts()\nprint('unique top10 tracks:', len(c), 'across', r.uid.nunique(),'users')\nprint('most common top10 tracks:')\nprint(c.head(10).to_string())\nprint('distinct top10 lists:', r[r['rank']<=10].groupby('uid').item_id.agg(tuple).nunique())\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "unique top10 tracks: 592 across 8456 users\nmost common top10 tracks:\nitem_id\n6006390 2917\n8213481 2800\n6896222 2712\n8743390 2653\n5436843 2587\n2651314 2580\n8722394 2542\n3542184 2427\n5862961 2292\n906358 2066\ndistinct top10 lists: 8217\n", "stderr": "", "aggregated_output": "unique top10 tracks: 592 across 8456 users\nmost common top10 tracks:\nitem_id\n6006390 2917\n8213481 2800\n6896222 2712\n8743390 2653\n5436843 2587\n2651314 2580\n8722394 2542\n3542184 2427\n5862961 2292\n906358 2066\ndistinct top10 lists: 8217\n", "exit_code": 0, "duration": { "secs": 2, "nanos": 318955171 }, "formatted_output": "unique top10 tracks: 592 across 8456 users\nmost common top10 tracks:\nitem_id\n6006390 2917\n8213481 2800\n6896222 2712\n8743390 2653\n5436843 2587\n2651314 2580\n8722394 2542\n3542184 2427\n5862961 2292\n906358 2066\ndistinct top10 lists: 8217\n" } } ## Step 32 · agent · 2026-09-23T05:28:53.348Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T05:28:55.657Z { "code": "text(await tools.write_stdin({session_id:69705,chars:\"\",yield_time_ms:1000,max_output_tokens:1000}));\ntext(await tools.exec_command({cmd:\"tail -n 4 /app/solution/hard.log && tail -n 3 /app/solution/fallback.log && cat /sys/fs/cgroup/memory.current\",max_output_tokens:1000}));\n" } ### Tool output · 2026-09-23T05:28:55.781Z Script completed Wall time 0.1 seconds Output: {"chunk_id":"ad419f","wall_time_seconds":0.0000093,"exit_code":0,"original_token_count":66,"output":"unique top10 tracks: 592 across 8456 users\nmost common top10 tracks:\nitem_id\n6006390 2917\n8213481 2800\n6896222 2712\n8743390 2653\n5436843 2587\n2651314 2580\n8722394 2542\n3542184 2427\n5862961 2292\n906358 2066\ndistinct top10 lists: 8217\n"} {"chunk_id":"de59f9","wall_time_seconds":0.00000323,"exit_code":0,"original_token_count":106,"output":"chronological 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\nloaded full-sequence cache: tokens=20,712,971 targets=20,703,818\n5577670656\n"} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "session_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "root_turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "response_id": "resp_03ec3525bea3ac86016ab363857cc487d2a644784738167edf", "usage": { "input_tokens": 60384, "cached_input_tokens": 59904, "cache_write_input_tokens": 0, "output_tokens": 563, "reasoning_output_tokens": 471, "total_tokens": 60947 }, "turn_token_usage": { "input_tokens": 1719027, "cached_input_tokens": 1619840, "cache_write_input_tokens": 0, "output_tokens": 10827, "reasoning_output_tokens": 4068, "total_tokens": 1729854 }, "thread_token_usage": { "input_tokens": 1719027, "cached_input_tokens": 1619840, "cache_write_input_tokens": 0, "output_tokens": 10827, "reasoning_output_tokens": 4068, "total_tokens": 1729854 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-6dd46e3b-6fc5-4685-ada4-73d489c0f10e", "process_id": "98506", "command": [ "/bin/sh", "-lc", "tail -n 4 /app/solution/hard.log && tail -n 3 /app/solution/fallback.log && cat /sys/fs/cgroup/memory.current" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "read", "cmd": "tail -n 4 /app/solution/hard.log", "name": "hard.log", "path": "/app/solution/hard.log" }, { "type": "read", "cmd": "tail -n 3 /app/solution/fallback.log", "name": "fallback.log", "path": "/app/solution/fallback.log" }, { "type": "read", "cmd": "cat /sys/fs/cgroup/memory.current", "name": "memory.current", "path": "/sys/fs/cgroup/memory.current" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "chronological 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\nloaded full-sequence cache: tokens=20,712,971 targets=20,703,818\n5577670656\n", "stderr": "", "aggregated_output": "chronological 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\nloaded full-sequence cache: tokens=20,712,971 targets=20,703,818\n5577670656\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 3230 }, "formatted_output": "chronological 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\nloaded full-sequence cache: tokens=20,712,971 targets=20,703,818\n5577670656\n" } } ## Step 33 · agent · 2026-09-23T05:29:01.947Z ### Thinking unavailable — no text recorded ### sleep · 2026-09-23T05:29:02.214Z { "duration_ms": 40000 } ### Tool output · 2026-09-23T05:29:42.225Z Wall time: 40.0059 seconds Sleep completed. ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "session_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "root_turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "response_id": "resp_03ec3525bea3ac86016ab36398008c87d2a5ea89368398ca14", "usage": { "input_tokens": 61288, "cached_input_tokens": 60160, "cache_write_input_tokens": 0, "output_tokens": 167, "reasoning_output_tokens": 146, "total_tokens": 61455 }, "turn_token_usage": { "input_tokens": 1780315, "cached_input_tokens": 1680000, "cache_write_input_tokens": 0, "output_tokens": 10994, "reasoning_output_tokens": 4214, "total_tokens": 1791309 }, "thread_token_usage": { "input_tokens": 1780315, "cached_input_tokens": 1680000, "cache_write_input_tokens": 0, "output_tokens": 10994, "reasoning_output_tokens": 4214, "total_tokens": 1791309 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "Extension", "kind": "clock.sleep", "id": "call_WAkRjhhHdpKXn9kTzCZUsf6P", "durationMs": 40000 } } ### exec · 2026-09-23T05:29:46.838Z { "code": "text(await tools.exec_command({cmd:\"tail -n 5 /app/solution/hard.log && tail -n 3 /app/solution/fallback.log\",max_output_tokens:1000}));\ntext(await tools.clock__curr_time({}));\n" } ### Tool output · 2026-09-23T05:29:46.994Z Script completed Wall time 0.1 seconds Output: {"chunk_id":"fa9da2","wall_time_seconds":0.00000346,"exit_code":0,"original_token_count":126,"output":"chronological 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=50 targets=206,457 batch_bce=0.684134 running_bce=0.691439 elapsed=64.7s\nloaded full-sequence cache: tokens=20,712,971 targets=20,703,818\n"} {"current_time":"2026-09-23 05:29:46 UTC"} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "session_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "root_turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "response_id": "resp_03ec3525bea3ac86016ab363c6738487d2b270980e7e9069d3", "usage": { "input_tokens": 61478, "cached_input_tokens": 61056, "cache_write_input_tokens": 0, "output_tokens": 60, "reasoning_output_tokens": 0, "total_tokens": 61538 }, "turn_token_usage": { "input_tokens": 1841793, "cached_input_tokens": 1741056, "cache_write_input_tokens": 0, "output_tokens": 11054, "reasoning_output_tokens": 4214, "total_tokens": 1852847 }, "thread_token_usage": { "input_tokens": 1841793, "cached_input_tokens": 1741056, "cache_write_input_tokens": 0, "output_tokens": 11054, "reasoning_output_tokens": 4214, "total_tokens": 1852847 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-aa9edf6e-3553-4d04-9a3b-cd90ad7c9d60", "process_id": "74830", "command": [ "/bin/sh", "-lc", "tail -n 5 /app/solution/hard.log && tail -n 3 /app/solution/fallback.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "read", "cmd": "tail -n 5 /app/solution/hard.log", "name": "hard.log", "path": "/app/solution/hard.log" }, { "type": "read", "cmd": "tail -n 3 /app/solution/fallback.log", "name": "fallback.log", "path": "/app/solution/fallback.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "chronological 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=50 targets=206,457 batch_bce=0.684134 running_bce=0.691439 elapsed=64.7s\nloaded full-sequence cache: tokens=20,712,971 targets=20,703,818\n", "stderr": "", "aggregated_output": "chronological 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=50 targets=206,457 batch_bce=0.684134 running_bce=0.691439 elapsed=64.7s\nloaded full-sequence cache: tokens=20,712,971 targets=20,703,818\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 3460 }, "formatted_output": "chronological 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=50 targets=206,457 batch_bce=0.684134 running_bce=0.691439 elapsed=64.7s\nloaded full-sequence cache: tokens=20,712,971 targets=20,703,818\n" } } ## Step 34 · agent · 2026-09-23T05:30:05.308Z ### Thinking unavailable — no text recorded The hard-negative run is progressing within the CPU and memory budget. Its loss is much higher than the baseline’s, as expected from harder comparisons, so loss alone won’t establish an improvement. I’ll use public NDCG for that decision. ### exec · 2026-09-23T05:30:14.601Z { "code": "text(await tools.exec_command({cmd:\"tail -n 3 /app/solution/hard.log && tail -n 3 /app/solution/fallback.log\",max_output_tokens:1000}));\ntext(await tools.apply_patch(\"*** Begin Patch\\n*** Update File: /app/solution/train_current_model.py\\n@@\\n-from dataclasses import asdict, dataclass\\n+from dataclasses import asdict, dataclass, replace\\n@@\\n hard_negative_weight: float = 0.5\\n+ hard_warmup_epochs: int = 0\\n@@\\n for epoch in range(start_epoch + 1, config.epochs + 1):\\n+ training_config = (replace(config, hard_negatives=0)\\n+ if epoch <= config.hard_warmup_epochs else config)\\n diagnostics = run_training_epoch(\\n@@\\n- config,\\n+ training_config,\\n@@\\n parser.add_argument(\\\"--hard-negative-weight\\\", type=float, default=Config.hard_negative_weight)\\n+ parser.add_argument(\\\"--hard-warmup-epochs\\\", type=int, default=Config.hard_warmup_epochs)\\n@@\\n hard_negative_weight=args.hard_negative_weight,\\n+ hard_warmup_epochs=args.hard_warmup_epochs,\\n*** End Patch\"));\n" } ### Tool output · 2026-09-23T05:30:14.716Z Script completed Wall time 0.1 seconds Output: {"chunk_id":"a3e5ea","wall_time_seconds":0.00000408,"exit_code":0,"original_token_count":78,"output":"target 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=50 targets=206,457 batch_bce=0.684134 running_bce=0.691439 elapsed=64.7s\nloaded full-sequence cache: tokens=20,712,971 targets=20,703,818\n"} {} ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-4b7ef692-41f9-4c84-b591-160cff1f6ac2", "process_id": "11557", "command": [ "/bin/sh", "-lc", "tail -n 3 /app/solution/hard.log && tail -n 3 /app/solution/fallback.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "read", "cmd": "tail -n 3 /app/solution/hard.log", "name": "hard.log", "path": "/app/solution/hard.log" }, { "type": "read", "cmd": "tail -n 3 /app/solution/fallback.log", "name": "fallback.log", "path": "/app/solution/fallback.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "target 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=50 targets=206,457 batch_bce=0.684134 running_bce=0.691439 elapsed=64.7s\nloaded full-sequence cache: tokens=20,712,971 targets=20,703,818\n", "stderr": "", "aggregated_output": "target 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=50 targets=206,457 batch_bce=0.684134 running_bce=0.691439 elapsed=64.7s\nloaded full-sequence cache: tokens=20,712,971 targets=20,703,818\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4080 }, "formatted_output": "target 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=50 targets=206,457 batch_bce=0.684134 running_bce=0.691439 elapsed=64.7s\nloaded full-sequence cache: tokens=20,712,971 targets=20,703,818\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "session_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "root_turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "response_id": "resp_03ec3525bea3ac86016ab363cb2db887d28c48123853cd9f37", "usage": { "input_tokens": 61784, "cached_input_tokens": 61312, "cache_write_input_tokens": 0, "output_tokens": 865, "reasoning_output_tokens": 516, "total_tokens": 62649 }, "turn_token_usage": { "input_tokens": 1903577, "cached_input_tokens": 1802368, "cache_write_input_tokens": 0, "output_tokens": 11919, "reasoning_output_tokens": 4730, "total_tokens": 1915496 }, "thread_token_usage": { "input_tokens": 1903577, "cached_input_tokens": 1802368, "cache_write_input_tokens": 0, "output_tokens": 11919, "reasoning_output_tokens": 4730, "total_tokens": 1915496 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-1b4cfebd-366c-4b71-9624-2ce01a22a0e5", "changes": { "/app/solution/train_current_model.py": { "type": "update", "unified_diff": "@@ -24,3 +24,3 @@\n import time\n-from dataclasses import asdict, dataclass\n+from dataclasses import asdict, dataclass, replace\n from pathlib import Path\n@@ -63,2 +63,3 @@\n hard_negative_weight: float = 0.5\n+ hard_warmup_epochs: int = 0\n negative_filter_scope: str = \"full-training-history\"\n@@ -1765,2 +1766,4 @@\n for epoch in range(start_epoch + 1, config.epochs + 1):\n+ training_config = (replace(config, hard_negatives=0)\n+ if epoch <= config.hard_warmup_epochs else config)\n diagnostics = run_training_epoch(\n@@ -1769,3 +1772,3 @@\n prepared,\n- config,\n+ training_config,\n target_weights,\n@@ -2015,2 +2018,3 @@\n parser.add_argument(\"--hard-negative-weight\", type=float, default=Config.hard_negative_weight)\n+ parser.add_argument(\"--hard-warmup-epochs\", type=int, default=Config.hard_warmup_epochs)\n parser.add_argument(\n@@ -2119,2 +2123,3 @@\n hard_negative_weight=args.hard_negative_weight,\n+ hard_warmup_epochs=args.hard_warmup_epochs,\n loss_microbatch_size=args.loss_microbatch_size,\n", "move_path": null } }, "status": "completed", "stdout": "Success. Updated the following files:\nM /app/solution/train_current_model.py\n", "stderr": "" } } ## Step 35 · agent · 2026-09-23T05:30:24.067Z ### Thinking unavailable — no text recorded ### sleep · 2026-09-23T05:30:24.371Z { "duration_ms": 40000 } ### Tool output · 2026-09-23T05:31:04.403Z Wall time: 40.0253 seconds Sleep completed. ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "session_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "root_turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "response_id": "resp_03ec3525bea3ac86016ab363e715b087d28038f204ee86bce7", "usage": { "input_tokens": 62815, "cached_input_tokens": 57984, "cache_write_input_tokens": 0, "output_tokens": 260, "reasoning_output_tokens": 239, "total_tokens": 63075 }, "turn_token_usage": { "input_tokens": 1966392, "cached_input_tokens": 1860352, "cache_write_input_tokens": 0, "output_tokens": 12179, "reasoning_output_tokens": 4969, "total_tokens": 1978571 }, "thread_token_usage": { "input_tokens": 1966392, "cached_input_tokens": 1860352, "cache_write_input_tokens": 0, "output_tokens": 12179, "reasoning_output_tokens": 4969, "total_tokens": 1978571 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-09154bc6-953c-4bb2-8995-2416a4c01770", "process_id": "75932", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python /app/solution/rank_checkpoint.py --checkpoint /app/solution/production_ckpt/best.pt --stage submission --output /app/solution/submission.parquet --threads 2 > /app/solution/fallback.json 2> /app/solution/fallback.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python /app/solution/rank_checkpoint.py --checkpoint /app/solution/production_ckpt/best.pt --stage submission --output /app/solution/submission.parquet --threads 2 > /app/solution/fallback.json 2> /app/solution/fallback.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 137, "nanos": 598940045 }, "formatted_output": "" } } ### Harness context { "type": "codex_item_completed", "item": { "type": "Extension", "kind": "clock.sleep", "id": "call_FLBvt4MJOT8HfiFOzMGaWaFw", "durationMs": 40000 } } ### exec · 2026-09-23T05:31:08.950Z { "code": "text(await tools.exec_command({cmd:\"tail -n 5 /app/solution/hard.log && tail -n 5 /app/solution/fallback.log && cat /sys/fs/cgroup/memory.current\",max_output_tokens:1000}));\ntext(await tools.clock__curr_time({}));\n" } ### Tool output · 2026-09-23T05:31:09.032Z Script completed Wall time 0.1 seconds Output: {"chunk_id":"a73c3c","wall_time_seconds":0.00000555,"exit_code":0,"original_token_count":135,"output":"inbatch 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=50 targets=206,457 batch_bce=0.684134 running_bce=0.691439 elapsed=64.7s\nepoch=1/1 batches=100 targets=402,892 batch_bce=0.676211 running_bce=0.684503 elapsed=125.5s\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\n6534930432\n"} {"current_time":"2026-09-23 05:31:09 UTC"} ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-6e3266be-bf55-4e97-b90a-4aa29d559f39", "process_id": "40909", "command": [ "/bin/sh", "-lc", "tail -n 5 /app/solution/hard.log && tail -n 5 /app/solution/fallback.log && cat /sys/fs/cgroup/memory.current" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "read", "cmd": "tail -n 5 /app/solution/hard.log", "name": "hard.log", "path": "/app/solution/hard.log" }, { "type": "read", "cmd": "tail -n 5 /app/solution/fallback.log", "name": "fallback.log", "path": "/app/solution/fallback.log" }, { "type": "read", "cmd": "cat /sys/fs/cgroup/memory.current", "name": "memory.current", "path": "/sys/fs/cgroup/memory.current" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "inbatch 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=50 targets=206,457 batch_bce=0.684134 running_bce=0.691439 elapsed=64.7s\nepoch=1/1 batches=100 targets=402,892 batch_bce=0.676211 running_bce=0.684503 elapsed=125.5s\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\n6534930432\n", "stderr": "", "aggregated_output": "inbatch 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=50 targets=206,457 batch_bce=0.684134 running_bce=0.691439 elapsed=64.7s\nepoch=1/1 batches=100 targets=402,892 batch_bce=0.676211 running_bce=0.684503 elapsed=125.5s\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\n6534930432\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 5550 }, "formatted_output": "inbatch 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=50 targets=206,457 batch_bce=0.684134 running_bce=0.691439 elapsed=64.7s\nepoch=1/1 batches=100 targets=402,892 batch_bce=0.676211 running_bce=0.684503 elapsed=125.5s\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\n6534930432\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "session_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "root_turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "response_id": "resp_03ec3525bea3ac86016ab3641897fc87d2b2c7a70d33c42ee2", "usage": { "input_tokens": 63098, "cached_input_tokens": 62592, "cache_write_input_tokens": 0, "output_tokens": 70, "reasoning_output_tokens": 0, "total_tokens": 63168 }, "turn_token_usage": { "input_tokens": 2029490, "cached_input_tokens": 1922944, "cache_write_input_tokens": 0, "output_tokens": 12249, "reasoning_output_tokens": 4969, "total_tokens": 2041739 }, "thread_token_usage": { "input_tokens": 2029490, "cached_input_tokens": 1922944, "cache_write_input_tokens": 0, "output_tokens": 12249, "reasoning_output_tokens": 4969, "total_tokens": 2041739 } } ## Step 36 · agent · 2026-09-23T05:31:12.074Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T05:31:14.712Z { "code": "text(await tools.exec_command({cmd:\"/opt/task-venv/bin/python /app/solution/validate_submission.py > /app/solution/fallback_validation.txt && cat /app/solution/fallback_validation.txt\",yield_time_ms:1000,max_output_tokens:1000}));\ntext(await tools.write_stdin({session_id:75932,chars:\"\",yield_time_ms:1000,max_output_tokens:1000}));\n" } ### Tool output · 2026-09-23T05:31:15.886Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"f8053c","wall_time_seconds":1.000427043,"session_id":56183,"original_token_count":0,"output":""} {"chunk_id":"7fd332","wall_time_seconds":0.00000181,"exit_code":0,"original_token_count":0,"output":""} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "session_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "root_turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "response_id": "resp_03ec3525bea3ac86016ab3641d498c87d2b0109121d64711fe", "usage": { "input_tokens": 63455, "cached_input_tokens": 62976, "cache_write_input_tokens": 0, "output_tokens": 135, "reasoning_output_tokens": 37, "total_tokens": 63590 }, "turn_token_usage": { "input_tokens": 2092945, "cached_input_tokens": 1985920, "cache_write_input_tokens": 0, "output_tokens": 12384, "reasoning_output_tokens": 5006, "total_tokens": 2105329 }, "thread_token_usage": { "input_tokens": 2092945, "cached_input_tokens": 1985920, "cache_write_input_tokens": 0, "output_tokens": 12384, "reasoning_output_tokens": 5006, "total_tokens": 2105329 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-045dc8f7-627b-413f-b4af-35d6f69a5fdb", "process_id": "56183", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python /app/solution/validate_submission.py > /app/solution/fallback_validation.txt && cat /app/solution/fallback_validation.txt" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python /app/solution/validate_submission.py > /app/solution/fallback_validation.txt && cat /app/solution/fallback_validation.txt" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "Validated 845,600 rows: exactly 100 distinct eligible ranked tracks for 8,456 users.\n", "stderr": "", "aggregated_output": "Validated 845,600 rows: exactly 100 distinct eligible ranked tracks for 8,456 users.\n", "exit_code": 0, "duration": { "secs": 2, "nanos": 327737387 }, "formatted_output": "Validated 845,600 rows: exactly 100 distinct eligible ranked tracks for 8,456 users.\n" } } ## Step 37 · agent · 2026-09-23T05:31:26.055Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T05:31:27.396Z { "code": "text(await tools.write_stdin({session_id:56183,chars:\"\",yield_time_ms:1000,max_output_tokens:1000}));\ntext(await tools.exec_command({cmd:\"tail -n 4 /app/solution/hard.log\",max_output_tokens:1000}));\n" } ### Tool output · 2026-09-23T05:31:27.502Z Script completed Wall time 0.0 seconds Output: {"chunk_id":"a48b0f","wall_time_seconds":0.00000226,"exit_code":0,"original_token_count":22,"output":"Validated 845,600 rows: exactly 100 distinct eligible ranked tracks for 8,456 users.\n"} {"chunk_id":"4d7b7d","wall_time_seconds":0.00000397,"exit_code":0,"original_token_count":85,"output":"target 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=50 targets=206,457 batch_bce=0.684134 running_bce=0.691439 elapsed=64.7s\nepoch=1/1 batches=100 targets=402,892 batch_bce=0.676211 running_bce=0.684503 elapsed=125.5s\n"} ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-ae88c7ca-bfd3-4009-9408-be34ab92c22b", "process_id": "3218", "command": [ "/bin/sh", "-lc", "tail -n 4 /app/solution/hard.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "read", "cmd": "tail -n 4 /app/solution/hard.log", "name": "hard.log", "path": "/app/solution/hard.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "target 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=50 targets=206,457 batch_bce=0.684134 running_bce=0.691439 elapsed=64.7s\nepoch=1/1 batches=100 targets=402,892 batch_bce=0.676211 running_bce=0.684503 elapsed=125.5s\n", "stderr": "", "aggregated_output": "target 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=50 targets=206,457 batch_bce=0.684134 running_bce=0.691439 elapsed=64.7s\nepoch=1/1 batches=100 targets=402,892 batch_bce=0.676211 running_bce=0.684503 elapsed=125.5s\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 3970 }, "formatted_output": "target 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=50 targets=206,457 batch_bce=0.684134 running_bce=0.691439 elapsed=64.7s\nepoch=1/1 batches=100 targets=402,892 batch_bce=0.676211 running_bce=0.684503 elapsed=125.5s\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "session_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "root_turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "response_id": "resp_03ec3525bea3ac86016ab364240e9087d291cd760224824dee", "usage": { "input_tokens": 63681, "cached_input_tokens": 61568, "cache_write_input_tokens": 0, "output_tokens": 310, "reasoning_output_tokens": 241, "total_tokens": 63991 }, "turn_token_usage": { "input_tokens": 2156626, "cached_input_tokens": 2047488, "cache_write_input_tokens": 0, "output_tokens": 12694, "reasoning_output_tokens": 5247, "total_tokens": 2169320 }, "thread_token_usage": { "input_tokens": 2156626, "cached_input_tokens": 2047488, "cache_write_input_tokens": 0, "output_tokens": 12694, "reasoning_output_tokens": 5247, "total_tokens": 2169320 } } ## Step 38 · agent · 2026-09-23T05:31:46.986Z ### Thinking unavailable — no text recorded A valid fallback is now saved at `/app/solution/submission.parquet`: 845,600 rows covering all 8,456 users, with exactly 100 distinct test-eligible tracks per user. The first hard-negative experiment is still training. ### exec · 2026-09-23T05:31:48.682Z { "code": "text(await tools.exec_command({cmd:\"tail -n 3 /app/solution/hard.log && ls -lh /app/solution/submission.parquet\",max_output_tokens:1000}));\ntext(await tools.clock__curr_time({}));\n" } ### Tool output · 2026-09-23T05:31:48.755Z Script completed Wall time 0.1 seconds Output: {"chunk_id":"32ef9f","wall_time_seconds":0.00000392,"exit_code":0,"original_token_count":89,"output":"epoch=1/1 batches=50 targets=206,457 batch_bce=0.684134 running_bce=0.691439 elapsed=64.7s\nepoch=1/1 batches=100 targets=402,892 batch_bce=0.676211 running_bce=0.684503 elapsed=125.5s\nepoch=1/1 batches=150 targets=596,745 batch_bce=0.671111 running_bce=0.680472 elapsed=182.1s\n-rw-r--r-- 1 agent agent 1.2M Sep 23 05:30 /app/solution/submission.parquet\n"} {"current_time":"2026-09-23 05:31:48 UTC"} ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-5a7af908-5ee9-446f-ba4c-1287bee10966", "process_id": "96065", "command": [ "/bin/sh", "-lc", "tail -n 3 /app/solution/hard.log && ls -lh /app/solution/submission.parquet" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "read", "cmd": "tail -n 3 /app/solution/hard.log", "name": "hard.log", "path": "/app/solution/hard.log" }, { "type": "list_files", "cmd": "ls -lh /app/solution/submission.parquet", "path": "submission.parquet" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "epoch=1/1 batches=50 targets=206,457 batch_bce=0.684134 running_bce=0.691439 elapsed=64.7s\nepoch=1/1 batches=100 targets=402,892 batch_bce=0.676211 running_bce=0.684503 elapsed=125.5s\nepoch=1/1 batches=150 targets=596,745 batch_bce=0.671111 running_bce=0.680472 elapsed=182.1s\n-rw-r--r-- 1 agent agent 1.2M Sep 23 05:30 /app/solution/submission.parquet\n", "stderr": "", "aggregated_output": "epoch=1/1 batches=50 targets=206,457 batch_bce=0.684134 running_bce=0.691439 elapsed=64.7s\nepoch=1/1 batches=100 targets=402,892 batch_bce=0.676211 running_bce=0.684503 elapsed=125.5s\nepoch=1/1 batches=150 targets=596,745 batch_bce=0.671111 running_bce=0.680472 elapsed=182.1s\n-rw-r--r-- 1 agent agent 1.2M Sep 23 05:30 /app/solution/submission.parquet\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 3920 }, "formatted_output": "epoch=1/1 batches=50 targets=206,457 batch_bce=0.684134 running_bce=0.691439 elapsed=64.7s\nepoch=1/1 batches=100 targets=402,892 batch_bce=0.676211 running_bce=0.684503 elapsed=125.5s\nepoch=1/1 batches=150 targets=596,745 batch_bce=0.671111 running_bce=0.680472 elapsed=182.1s\n-rw-r--r-- 1 agent agent 1.2M Sep 23 05:30 /app/solution/submission.parquet\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "session_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "root_turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "response_id": "resp_03ec3525bea3ac86016ab3642fdc9087d2b459c33e32750a3a", "usage": { "input_tokens": 64229, "cached_input_tokens": 63488, "cache_write_input_tokens": 0, "output_tokens": 634, "reasoning_output_tokens": 516, "total_tokens": 64863 }, "turn_token_usage": { "input_tokens": 2220855, "cached_input_tokens": 2110976, "cache_write_input_tokens": 0, "output_tokens": 13328, "reasoning_output_tokens": 5763, "total_tokens": 2234183 }, "thread_token_usage": { "input_tokens": 2220855, "cached_input_tokens": 2110976, "cache_write_input_tokens": 0, "output_tokens": 13328, "reasoning_output_tokens": 5763, "total_tokens": 2234183 } } ## Step 39 · agent · 2026-09-23T05:32:01.835Z ### Thinking unavailable — no text recorded ### sleep · 2026-09-23T05:32:01.991Z { "duration_ms": 40000 } ### Tool output · 2026-09-23T05:32:42.000Z Wall time: 40.0049 seconds Sleep completed. ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "session_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "root_turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "response_id": "resp_03ec3525bea3ac86016ab36444ff6887d2a4b4b086c8a35be0", "usage": { "input_tokens": 65078, "cached_input_tokens": 64000, "cache_write_input_tokens": 0, "output_tokens": 345, "reasoning_output_tokens": 324, "total_tokens": 65423 }, "turn_token_usage": { "input_tokens": 2285933, "cached_input_tokens": 2174976, "cache_write_input_tokens": 0, "output_tokens": 13673, "reasoning_output_tokens": 6087, "total_tokens": 2299606 }, "thread_token_usage": { "input_tokens": 2285933, "cached_input_tokens": 2174976, "cache_write_input_tokens": 0, "output_tokens": 13673, "reasoning_output_tokens": 6087, "total_tokens": 2299606 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "Extension", "kind": "clock.sleep", "id": "call_vFtAZmRsRdg7KigsPUt0MsuN", "durationMs": 40000 } } ### exec · 2026-09-23T05:32:45.900Z { "code": "text(await tools.exec_command({cmd:\"tail -n 4 /app/solution/hard.log && cat /sys/fs/cgroup/memory.current\",max_output_tokens:1000}));\n" } ### Tool output · 2026-09-23T05:32:45.976Z Script completed Wall time 0.0 seconds Output: {"chunk_id":"ce1644","wall_time_seconds":0.000007611,"exit_code":0,"original_token_count":96,"output":"epoch=1/1 batches=50 targets=206,457 batch_bce=0.684134 running_bce=0.691439 elapsed=64.7s\nepoch=1/1 batches=100 targets=402,892 batch_bce=0.676211 running_bce=0.684503 elapsed=125.5s\nepoch=1/1 batches=150 targets=596,745 batch_bce=0.671111 running_bce=0.680472 elapsed=182.1s\nepoch=1/1 batches=200 targets=790,005 batch_bce=0.673614 running_bce=0.678500 elapsed=239.4s\n7149719552\n"} ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-59fbfa0d-c337-418a-835a-652ac1c38dfd", "process_id": "46454", "command": [ "/bin/sh", "-lc", "tail -n 4 /app/solution/hard.log && cat /sys/fs/cgroup/memory.current" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "read", "cmd": "tail -n 4 /app/solution/hard.log", "name": "hard.log", "path": "/app/solution/hard.log" }, { "type": "read", "cmd": "cat /sys/fs/cgroup/memory.current", "name": "memory.current", "path": "/sys/fs/cgroup/memory.current" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "epoch=1/1 batches=50 targets=206,457 batch_bce=0.684134 running_bce=0.691439 elapsed=64.7s\nepoch=1/1 batches=100 targets=402,892 batch_bce=0.676211 running_bce=0.684503 elapsed=125.5s\nepoch=1/1 batches=150 targets=596,745 batch_bce=0.671111 running_bce=0.680472 elapsed=182.1s\nepoch=1/1 batches=200 targets=790,005 batch_bce=0.673614 running_bce=0.678500 elapsed=239.4s\n7149719552\n", "stderr": "", "aggregated_output": "epoch=1/1 batches=50 targets=206,457 batch_bce=0.684134 running_bce=0.691439 elapsed=64.7s\nepoch=1/1 batches=100 targets=402,892 batch_bce=0.676211 running_bce=0.684503 elapsed=125.5s\nepoch=1/1 batches=150 targets=596,745 batch_bce=0.671111 running_bce=0.680472 elapsed=182.1s\nepoch=1/1 batches=200 targets=790,005 batch_bce=0.673614 running_bce=0.678500 elapsed=239.4s\n7149719552\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 7611 }, "formatted_output": "epoch=1/1 batches=50 targets=206,457 batch_bce=0.684134 running_bce=0.691439 elapsed=64.7s\nepoch=1/1 batches=100 targets=402,892 batch_bce=0.676211 running_bce=0.684503 elapsed=125.5s\nepoch=1/1 batches=150 targets=596,745 batch_bce=0.671111 running_bce=0.680472 elapsed=182.1s\nepoch=1/1 batches=200 targets=790,005 batch_bce=0.673614 running_bce=0.678500 elapsed=239.4s\n7149719552\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "session_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "root_turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "response_id": "resp_03ec3525bea3ac86016ab3647a301887d2ad6f93e60e3e2d89", "usage": { "input_tokens": 65446, "cached_input_tokens": 63232, "cache_write_input_tokens": 0, "output_tokens": 48, "reasoning_output_tokens": 0, "total_tokens": 65494 }, "turn_token_usage": { "input_tokens": 2351379, "cached_input_tokens": 2238208, "cache_write_input_tokens": 0, "output_tokens": 13721, "reasoning_output_tokens": 6087, "total_tokens": 2365100 }, "thread_token_usage": { "input_tokens": 2351379, "cached_input_tokens": 2238208, "cache_write_input_tokens": 0, "output_tokens": 13721, "reasoning_output_tokens": 6087, "total_tokens": 2365100 } } ## Step 40 · agent · 2026-09-23T05:33:03.592Z ### Thinking unavailable — no text recorded The hard-negative run has processed about 790,000 of 1.05 million targets, with finite losses and no filtering failures. It is training at a similar pace to the baseline. Public retrieval quality is the remaining test. ### sleep · 2026-09-23T05:33:04.833Z { "duration_ms": 40000 } ### Tool output · 2026-09-23T05:33:44.844Z Wall time: 40.0069 seconds Sleep completed. ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "session_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "root_turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "response_id": "resp_03ec3525bea3ac86016ab3647e3da487d29cab82c44a5622d6", "usage": { "input_tokens": 65701, "cached_input_tokens": 64896, "cache_write_input_tokens": 0, "output_tokens": 516, "reasoning_output_tokens": 442, "total_tokens": 66217 }, "turn_token_usage": { "input_tokens": 2417080, "cached_input_tokens": 2303104, "cache_write_input_tokens": 0, "output_tokens": 14237, "reasoning_output_tokens": 6529, "total_tokens": 2431317 }, "thread_token_usage": { "input_tokens": 2417080, "cached_input_tokens": 2303104, "cache_write_input_tokens": 0, "output_tokens": 14237, "reasoning_output_tokens": 6529, "total_tokens": 2431317 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "Extension", "kind": "clock.sleep", "id": "call_skCiv6JFx6lS41yJ6RsMG9Hb", "durationMs": 40000 } } ### exec · 2026-09-23T05:33:48.094Z { "code": "text(await tools.exec_command({cmd:\"tail -n 5 /app/solution/hard.log\",max_output_tokens:1000}));\ntext(await tools.clock__curr_time({}));\n" } ### Tool output · 2026-09-23T05:33:48.181Z Script completed Wall time 0.0 seconds Output: {"chunk_id":"080bab","wall_time_seconds":0.00000497,"exit_code":0,"original_token_count":116,"output":"epoch=1/1 batches=50 targets=206,457 batch_bce=0.684134 running_bce=0.691439 elapsed=64.7s\nepoch=1/1 batches=100 targets=402,892 batch_bce=0.676211 running_bce=0.684503 elapsed=125.5s\nepoch=1/1 batches=150 targets=596,745 batch_bce=0.671111 running_bce=0.680472 elapsed=182.1s\nepoch=1/1 batches=200 targets=790,005 batch_bce=0.673614 running_bce=0.678500 elapsed=239.4s\nepoch=1/1 batches=250 targets=987,417 batch_bce=0.678379 running_bce=0.676589 elapsed=297.0s\n"} {"current_time":"2026-09-23 05:33:48 UTC"} ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-34a5907b-3ce2-4563-b3d0-90c8624e499b", "process_id": "4452", "command": [ "/bin/sh", "-lc", "tail -n 5 /app/solution/hard.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "read", "cmd": "tail -n 5 /app/solution/hard.log", "name": "hard.log", "path": "/app/solution/hard.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "epoch=1/1 batches=50 targets=206,457 batch_bce=0.684134 running_bce=0.691439 elapsed=64.7s\nepoch=1/1 batches=100 targets=402,892 batch_bce=0.676211 running_bce=0.684503 elapsed=125.5s\nepoch=1/1 batches=150 targets=596,745 batch_bce=0.671111 running_bce=0.680472 elapsed=182.1s\nepoch=1/1 batches=200 targets=790,005 batch_bce=0.673614 running_bce=0.678500 elapsed=239.4s\nepoch=1/1 batches=250 targets=987,417 batch_bce=0.678379 running_bce=0.676589 elapsed=297.0s\n", "stderr": "", "aggregated_output": "epoch=1/1 batches=50 targets=206,457 batch_bce=0.684134 running_bce=0.691439 elapsed=64.7s\nepoch=1/1 batches=100 targets=402,892 batch_bce=0.676211 running_bce=0.684503 elapsed=125.5s\nepoch=1/1 batches=150 targets=596,745 batch_bce=0.671111 running_bce=0.680472 elapsed=182.1s\nepoch=1/1 batches=200 targets=790,005 batch_bce=0.673614 running_bce=0.678500 elapsed=239.4s\nepoch=1/1 batches=250 targets=987,417 batch_bce=0.678379 running_bce=0.676589 elapsed=297.0s\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4970 }, "formatted_output": "epoch=1/1 batches=50 targets=206,457 batch_bce=0.684134 running_bce=0.691439 elapsed=64.7s\nepoch=1/1 batches=100 targets=402,892 batch_bce=0.676211 running_bce=0.684503 elapsed=125.5s\nepoch=1/1 batches=150 targets=596,745 batch_bce=0.671111 running_bce=0.680472 elapsed=182.1s\nepoch=1/1 batches=200 targets=790,005 batch_bce=0.673614 running_bce=0.678500 elapsed=239.4s\nepoch=1/1 batches=250 targets=987,417 batch_bce=0.678379 running_bce=0.676589 elapsed=297.0s\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "session_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "root_turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "response_id": "resp_03ec3525bea3ac86016ab364b9136c87d2bc9428975fa56cc8", "usage": { "input_tokens": 66240, "cached_input_tokens": 65536, "cache_write_input_tokens": 0, "output_tokens": 47, "reasoning_output_tokens": 0, "total_tokens": 66287 }, "turn_token_usage": { "input_tokens": 2483320, "cached_input_tokens": 2368640, "cache_write_input_tokens": 0, "output_tokens": 14284, "reasoning_output_tokens": 6529, "total_tokens": 2497604 }, "thread_token_usage": { "input_tokens": 2483320, "cached_input_tokens": 2368640, "cache_write_input_tokens": 0, "output_tokens": 14284, "reasoning_output_tokens": 6529, "total_tokens": 2497604 } } ## Step 41 · agent · 2026-09-23T05:34:02.830Z ### Thinking unavailable — no text recorded ### sleep · 2026-09-23T05:34:02.885Z { "duration_ms": 40000 } ### Tool output · 2026-09-23T05:34:42.893Z Wall time: 40.0034 seconds Sleep completed. ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "session_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "root_turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "response_id": "resp_03ec3525bea3ac86016ab364bc7a2c87d29a0fcf2af00a6f1a", "usage": { "input_tokens": 66543, "cached_input_tokens": 66048, "cache_write_input_tokens": 0, "output_tokens": 388, "reasoning_output_tokens": 367, "total_tokens": 66931 }, "turn_token_usage": { "input_tokens": 2549863, "cached_input_tokens": 2434688, "cache_write_input_tokens": 0, "output_tokens": 14672, "reasoning_output_tokens": 6896, "total_tokens": 2564535 }, "thread_token_usage": { "input_tokens": 2549863, "cached_input_tokens": 2434688, "cache_write_input_tokens": 0, "output_tokens": 14672, "reasoning_output_tokens": 6896, "total_tokens": 2564535 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "Extension", "kind": "clock.sleep", "id": "call_pq5lV2gYRk8p7Lu9lDUaGUoY", "durationMs": 40000 } } ### exec · 2026-09-23T05:34:45.377Z { "code": "text(await tools.exec_command({cmd:\"tail -n 6 /app/solution/hard.log\",max_output_tokens:1000}));\n" } ### Tool output · 2026-09-23T05:34:45.475Z Script completed Wall time 0.0 seconds Output: {"chunk_id":"106119","wall_time_seconds":0.00000581,"exit_code":0,"original_token_count":143,"output":"epoch=1/1 batches=150 targets=596,745 batch_bce=0.671111 running_bce=0.680472 elapsed=182.1s\nepoch=1/1 batches=200 targets=790,005 batch_bce=0.673614 running_bce=0.678500 elapsed=239.4s\nepoch=1/1 batches=250 targets=987,417 batch_bce=0.678379 running_bce=0.676589 elapsed=297.0s\nsaved epoch checkpoint: /app/solution/hard_ckpt/epoch-01.pt\nepoch=1/1 train_loss=0.67621 validation_loss=0.66443 pair_accuracy=0.703 train_targets=1,052,953 validation_targets=130,103 train_seconds=316.2 validation_seconds=21.1\nrestored best validation-BCE checkpoint: epoch=1 loss=0.664425\n"} ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-dbcc3238-da25-4167-8c11-c129e374c34f", "process_id": "88798", "command": [ "/bin/sh", "-lc", "tail -n 6 /app/solution/hard.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "read", "cmd": "tail -n 6 /app/solution/hard.log", "name": "hard.log", "path": "/app/solution/hard.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "epoch=1/1 batches=150 targets=596,745 batch_bce=0.671111 running_bce=0.680472 elapsed=182.1s\nepoch=1/1 batches=200 targets=790,005 batch_bce=0.673614 running_bce=0.678500 elapsed=239.4s\nepoch=1/1 batches=250 targets=987,417 batch_bce=0.678379 running_bce=0.676589 elapsed=297.0s\nsaved epoch checkpoint: /app/solution/hard_ckpt/epoch-01.pt\nepoch=1/1 train_loss=0.67621 validation_loss=0.66443 pair_accuracy=0.703 train_targets=1,052,953 validation_targets=130,103 train_seconds=316.2 validation_seconds=21.1\nrestored best validation-BCE checkpoint: epoch=1 loss=0.664425\n", "stderr": "", "aggregated_output": "epoch=1/1 batches=150 targets=596,745 batch_bce=0.671111 running_bce=0.680472 elapsed=182.1s\nepoch=1/1 batches=200 targets=790,005 batch_bce=0.673614 running_bce=0.678500 elapsed=239.4s\nepoch=1/1 batches=250 targets=987,417 batch_bce=0.678379 running_bce=0.676589 elapsed=297.0s\nsaved epoch checkpoint: /app/solution/hard_ckpt/epoch-01.pt\nepoch=1/1 train_loss=0.67621 validation_loss=0.66443 pair_accuracy=0.703 train_targets=1,052,953 validation_targets=130,103 train_seconds=316.2 validation_seconds=21.1\nrestored best validation-BCE checkpoint: epoch=1 loss=0.664425\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 5810 }, "formatted_output": "epoch=1/1 batches=150 targets=596,745 batch_bce=0.671111 running_bce=0.680472 elapsed=182.1s\nepoch=1/1 batches=200 targets=790,005 batch_bce=0.673614 running_bce=0.678500 elapsed=239.4s\nepoch=1/1 batches=250 targets=987,417 batch_bce=0.678379 running_bce=0.676589 elapsed=297.0s\nsaved epoch checkpoint: /app/solution/hard_ckpt/epoch-01.pt\nepoch=1/1 train_loss=0.67621 validation_loss=0.66443 pair_accuracy=0.703 train_targets=1,052,953 validation_targets=130,103 train_seconds=316.2 validation_seconds=21.1\nrestored best validation-BCE checkpoint: epoch=1 loss=0.664425\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "session_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "root_turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "response_id": "resp_03ec3525bea3ac86016ab364f30e9487d2bffb7a5a300da5b5", "usage": { "input_tokens": 66954, "cached_input_tokens": 66304, "cache_write_input_tokens": 0, "output_tokens": 38, "reasoning_output_tokens": 0, "total_tokens": 66992 }, "turn_token_usage": { "input_tokens": 2616817, "cached_input_tokens": 2500992, "cache_write_input_tokens": 0, "output_tokens": 14710, "reasoning_output_tokens": 6896, "total_tokens": 2631527 }, "thread_token_usage": { "input_tokens": 2616817, "cached_input_tokens": 2500992, "cache_write_input_tokens": 0, "output_tokens": 14710, "reasoning_output_tokens": 6896, "total_tokens": 2631527 } } ## Step 42 · agent · 2026-09-23T05:34:55.890Z ### Thinking unavailable — no text recorded The first hard-negative model has finished training and is producing its validation ranking. Its pairwise accuracy against the original random negatives is lower than the baseline’s. Public NDCG will show whether the harder training improved the top of the ranking. ### exec · 2026-09-23T05:35:02.281Z { "code": "text(await tools.apply_patch(\"*** Begin Patch\\n*** Update File: /app/solution/train_current_model.py\\n@@\\n result[torch.from_numpy(output_rows.astype(np.int64))] = queries.cpu()\\n+ if (group_start // config.user_batch_size + 1) % 25 == 0:\\n+ log(f\\\"encoded users={min(group_start + config.user_batch_size, len(ordered)):,}/{len(ordered):,}\\\")\\n return result\\n*** End Patch\"));\ntext(await tools.exec_command({cmd:\"cat /app/solution/production.json | head -n 12 && tail -n 3 /app/solution/hard.log\",max_output_tokens:1000}));\n" } ### Tool output · 2026-09-23T05:35:02.373Z Script completed Wall time 0.1 seconds Output: {} {"chunk_id":"481312","wall_time_seconds":0.00000425,"exit_code":0,"original_token_count":187,"output":"{\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\": 416.199,\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\",\nsaved epoch checkpoint: /app/solution/hard_ckpt/epoch-01.pt\nepoch=1/1 train_loss=0.67621 validation_loss=0.66443 pair_accuracy=0.703 train_targets=1,052,953 validation_targets=130,103 train_seconds=316.2 validation_seconds=21.1\nrestored best validation-BCE checkpoint: epoch=1 loss=0.664425\n"} ### Harness context { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-2b0779ba-5a8c-48a5-857b-d60a8ebd50fb", "changes": { "/app/solution/train_current_model.py": { "type": "update", "unified_diff": "@@ -1934,2 +1934,4 @@\n result[torch.from_numpy(output_rows.astype(np.int64))] = queries.cpu()\n+ if (group_start // config.user_batch_size + 1) % 25 == 0:\n+ log(f\"encoded users={min(group_start + config.user_batch_size, len(ordered)):,}/{len(ordered):,}\")\n return result\n", "move_path": null } }, "status": "completed", "stdout": "Success. Updated the following files:\nM /app/solution/train_current_model.py\n", "stderr": "" } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-f3d2d723-9a23-4883-8aa3-78b7ae565428", "process_id": "55020", "command": [ "/bin/sh", "-lc", "cat /app/solution/production.json | head -n 12 && tail -n 3 /app/solution/hard.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "read", "cmd": "cat /app/solution/production.json", "name": "production.json", "path": "/app/solution/production.json" }, { "type": "read", "cmd": "tail -n 3 /app/solution/hard.log", "name": "hard.log", "path": "/app/solution/hard.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{\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\": 416.199,\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\",\nsaved epoch checkpoint: /app/solution/hard_ckpt/epoch-01.pt\nepoch=1/1 train_loss=0.67621 validation_loss=0.66443 pair_accuracy=0.703 train_targets=1,052,953 validation_targets=130,103 train_seconds=316.2 validation_seconds=21.1\nrestored best validation-BCE checkpoint: epoch=1 loss=0.664425\n", "stderr": "", "aggregated_output": "{\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\": 416.199,\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\",\nsaved epoch checkpoint: /app/solution/hard_ckpt/epoch-01.pt\nepoch=1/1 train_loss=0.67621 validation_loss=0.66443 pair_accuracy=0.703 train_targets=1,052,953 validation_targets=130,103 train_seconds=316.2 validation_seconds=21.1\nrestored best validation-BCE checkpoint: epoch=1 loss=0.664425\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4250 }, "formatted_output": "{\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\": 416.199,\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\",\nsaved epoch checkpoint: /app/solution/hard_ckpt/epoch-01.pt\nepoch=1/1 train_loss=0.67621 validation_loss=0.66443 pair_accuracy=0.703 train_targets=1,052,953 validation_targets=130,103 train_seconds=316.2 validation_seconds=21.1\nrestored best validation-BCE checkpoint: epoch=1 loss=0.664425\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "session_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "root_turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "response_id": "resp_03ec3525bea3ac86016ab364f5d58887d293a70ab2b537a910", "usage": { "input_tokens": 67245, "cached_input_tokens": 66816, "cache_write_input_tokens": 0, "output_tokens": 499, "reasoning_output_tokens": 282, "total_tokens": 67744 }, "turn_token_usage": { "input_tokens": 2684062, "cached_input_tokens": 2567808, "cache_write_input_tokens": 0, "output_tokens": 15209, "reasoning_output_tokens": 7178, "total_tokens": 2699271 }, "thread_token_usage": { "input_tokens": 2684062, "cached_input_tokens": 2567808, "cache_write_input_tokens": 0, "output_tokens": 15209, "reasoning_output_tokens": 7178, "total_tokens": 2699271 } } ## Step 43 · agent · 2026-09-23T05:35:07.198Z ### Thinking unavailable — no text recorded ### sleep · 2026-09-23T05:35:07.469Z { "duration_ms": 40000 } ### Tool output · 2026-09-23T05:35:47.477Z Wall time: 40.0037 seconds Sleep completed. ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "session_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "root_turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "response_id": "resp_03ec3525bea3ac86016ab365069cec87d2a400d495d4722754", "usage": { "input_tokens": 68028, "cached_input_tokens": 67072, "cache_write_input_tokens": 0, "output_tokens": 121, "reasoning_output_tokens": 100, "total_tokens": 68149 }, "turn_token_usage": { "input_tokens": 2752090, "cached_input_tokens": 2634880, "cache_write_input_tokens": 0, "output_tokens": 15330, "reasoning_output_tokens": 7278, "total_tokens": 2767420 }, "thread_token_usage": { "input_tokens": 2752090, "cached_input_tokens": 2634880, "cache_write_input_tokens": 0, "output_tokens": 15330, "reasoning_output_tokens": 7278, "total_tokens": 2767420 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-8f91b7c5-ad4a-49eb-8a64-aeeea25d1efc", "process_id": "26601", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python /app/solution/train_current_model.py --stage validation --public-dir /task --output /app/solution/hard_validation.parquet --prepared-cache /app/solution/validation_cache.npz --checkpoint-dir /app/solution/hard_ckpt --threads 6 --loss-microbatch-size 4096 > /app/solution/hard.json 2> /app/solution/hard.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 --output /app/solution/hard_validation.parquet --prepared-cache /app/solution/validation_cache.npz --checkpoint-dir /app/solution/hard_ckpt --threads 6 --loss-microbatch-size 4096 > /app/solution/hard.json 2> /app/solution/hard.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 430, "nanos": 838340093 }, "formatted_output": "" } } ### Harness context { "type": "codex_item_completed", "item": { "type": "Extension", "kind": "clock.sleep", "id": "call_KdkT1W4f107WM0cRBNaaB4fL", "durationMs": 40000 } } ### exec · 2026-09-23T05:35:50.608Z { "code": "text(await tools.exec_command({cmd:\"tail -n 5 /app/solution/hard.log && ls -lh /app/solution/hard_validation.parquet\",max_output_tokens:1000}));\ntext(await tools.clock__curr_time({}));\n" } ### Tool output · 2026-09-23T05:35:50.674Z Script completed Wall time 0.0 seconds Output: {"chunk_id":"74e441","wall_time_seconds":0.00000774,"exit_code":0,"original_token_count":52,"output":"ranked 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-rw-r--r-- 1 agent agent 1.4M Sep 23 05:35 /app/solution/hard_validation.parquet\n"} {"current_time":"2026-09-23 05:35:50 UTC"} ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-89f5d960-a726-406c-8756-2e2404873c9d", "process_id": "93380", "command": [ "/bin/sh", "-lc", "tail -n 5 /app/solution/hard.log && ls -lh /app/solution/hard_validation.parquet" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "read", "cmd": "tail -n 5 /app/solution/hard.log", "name": "hard.log", "path": "/app/solution/hard.log" }, { "type": "list_files", "cmd": "ls -lh /app/solution/hard_validation.parquet", "path": "hard_validation.parquet" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "ranked 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-rw-r--r-- 1 agent agent 1.4M Sep 23 05:35 /app/solution/hard_validation.parquet\n", "stderr": "", "aggregated_output": "ranked 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-rw-r--r-- 1 agent agent 1.4M Sep 23 05:35 /app/solution/hard_validation.parquet\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 7740 }, "formatted_output": "ranked 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-rw-r--r-- 1 agent agent 1.4M Sep 23 05:35 /app/solution/hard_validation.parquet\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "session_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "root_turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "response_id": "resp_03ec3525bea3ac86016ab36533b12087d283801db9b58f0db8", "usage": { "input_tokens": 68172, "cached_input_tokens": 67840, "cache_write_input_tokens": 0, "output_tokens": 60, "reasoning_output_tokens": 0, "total_tokens": 68232 }, "turn_token_usage": { "input_tokens": 2820262, "cached_input_tokens": 2702720, "cache_write_input_tokens": 0, "output_tokens": 15390, "reasoning_output_tokens": 7278, "total_tokens": 2835652 }, "thread_token_usage": { "input_tokens": 2820262, "cached_input_tokens": 2702720, "cache_write_input_tokens": 0, "output_tokens": 15390, "reasoning_output_tokens": 7278, "total_tokens": 2835652 } } ### exec · 2026-09-23T05:35:55.385Z { "code": "text(await tools.exec_command({cmd:\"/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/hard_validation.parquet > /app/solution/hard_score.json && cat /app/solution/hard_score.json\",yield_time_ms:1000,max_output_tokens:1000}));\ntext(await tools.write_stdin({session_id:26601,chars:\"\",yield_time_ms:1000,max_output_tokens:1000}));\n" } ### Tool output · 2026-09-23T05:35:56.553Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"1603c8","wall_time_seconds":1.0006394,"session_id":28323,"original_token_count":0,"output":""} {"chunk_id":"79e192","wall_time_seconds":0.000001471,"exit_code":0,"original_token_count":0,"output":""} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "session_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "root_turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "response_id": "resp_03ec3525bea3ac86016ab3653717a887d28b4cb7a0789b7449", "usage": { "input_tokens": 68397, "cached_input_tokens": 67968, "cache_write_input_tokens": 0, "output_tokens": 103, "reasoning_output_tokens": 0, "total_tokens": 68500 }, "turn_token_usage": { "input_tokens": 2888659, "cached_input_tokens": 2770688, "cache_write_input_tokens": 0, "output_tokens": 15493, "reasoning_output_tokens": 7278, "total_tokens": 2904152 }, "thread_token_usage": { "input_tokens": 2888659, "cached_input_tokens": 2770688, "cache_write_input_tokens": 0, "output_tokens": 15493, "reasoning_output_tokens": 7278, "total_tokens": 2904152 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-d1aad071-2832-4b13-aff2-dacc34fc413e", "process_id": "28323", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/hard_validation.parquet > /app/solution/hard_score.json && cat /app/solution/hard_score.json" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/hard_validation.parquet > /app/solution/hard_score.json && cat /app/solution/hard_score.json" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{\n \"model\": \"/app/solution/hard_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.002453,\n \"quality_users\": 4263\n }\n}\n", "stderr": "", "aggregated_output": "{\n \"model\": \"/app/solution/hard_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.002453,\n \"quality_users\": 4263\n }\n}\n", "exit_code": 0, "duration": { "secs": 3, "nanos": 166747646 }, "formatted_output": "{\n \"model\": \"/app/solution/hard_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.002453,\n \"quality_users\": 4263\n }\n}\n" } } ## Step 44 · agent · 2026-09-23T05:35:58.856Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T05:36:02.283Z { "code": "text(await tools.write_stdin({session_id:28323,chars:\"\",yield_time_ms:1000,max_output_tokens:1000}));\ntext(await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\nimport pandas as pd\\nr=pd.read_parquet('/app/solution/hard_validation.parquet')\\nprint('Hard-negative top10 catalog coverage:',r[r['rank']<=10].item_id.nunique())\\nPY\",yield_time_ms:1000,max_output_tokens:1000}));\n" } ### Tool output · 2026-09-23T05:36:03.449Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"5bcf00","wall_time_seconds":0.00000259,"exit_code":0,"original_token_count":34,"output":"{\n \"model\": \"/app/solution/hard_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.002453,\n \"quality_users\": 4263\n }\n}\n"} {"chunk_id":"fd5fef","wall_time_seconds":1.001097282,"session_id":90070,"original_token_count":0,"output":""} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "session_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "root_turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "response_id": "resp_03ec3525bea3ac86016ab3653cb81087d29d0c125f88b2b1b6", "usage": { "input_tokens": 68591, "cached_input_tokens": 68224, "cache_write_input_tokens": 0, "output_tokens": 139, "reasoning_output_tokens": 15, "total_tokens": 68730 }, "turn_token_usage": { "input_tokens": 2957250, "cached_input_tokens": 2838912, "cache_write_input_tokens": 0, "output_tokens": 15632, "reasoning_output_tokens": 7293, "total_tokens": 2972882 }, "thread_token_usage": { "input_tokens": 2957250, "cached_input_tokens": 2838912, "cache_write_input_tokens": 0, "output_tokens": 15632, "reasoning_output_tokens": 7293, "total_tokens": 2972882 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-754a6786-f3d9-4104-85bc-3889e62e0d6b", "process_id": "90070", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd\nr=pd.read_parquet('/app/solution/hard_validation.parquet')\nprint('Hard-negative top10 catalog coverage:',r[r['rank']<=10].item_id.nunique())\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd\nr=pd.read_parquet('/app/solution/hard_validation.parquet')\nprint('Hard-negative top10 catalog coverage:',r[r['rank']<=10].item_id.nunique())\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "Hard-negative top10 catalog coverage: 1457\n", "stderr": "", "aggregated_output": "Hard-negative top10 catalog coverage: 1457\n", "exit_code": 0, "duration": { "secs": 1, "nanos": 406141389 }, "formatted_output": "Hard-negative top10 catalog coverage: 1457\n" } } ## Step 45 · agent · 2026-09-23T05:36:22.426Z ### Thinking unavailable — no text recorded ## Step 46 · agent · 2026-09-23T05:36:37.571Z ### Thinking unavailable — no text recorded The first variant failed: public NDCG fell to **0.002453**. I’ll discard it. Giving mined negatives half the BCE weight appears to have suppressed frequently relevant tracks too strongly. Next I’ll keep the baseline BCE intact and add a smaller pairwise loss that asks the positive to outrank hard negatives, starting from the trained baseline. ### exec · 2026-09-23T05:37:00.472Z { "code": "text(await tools.apply_patch(\"*** Begin Patch\\n*** Update File: /app/solution/train_current_model.py\\n@@\\n hard_warmup_epochs: int = 0\\n+ hard_objective: str = \\\"pairwise\\\"\\n+ hard_include_inbatch: bool = False\\n@@\\n- batch_pool = torch.unique(positives.cpu())\\n+ batch_pool = (torch.unique(positives.cpu()) if config.hard_include_inbatch\\n+ else torch.empty(0, dtype=torch.long))\\n@@\\n- if config.hard_negatives:\\n+ if config.hard_negatives and config.hard_objective == \\\"bce\\\":\\n hard_loss = F.softplus(negative_logits[:, negatives.shape[1]:]).mean(dim=1)\\n negative_loss = ((1.0 - config.hard_negative_weight) * negative_loss\\n + config.hard_negative_weight * hard_loss)\\n@@\\n- loss_sum = (\\n- selected_weights * (positive_loss + negative_loss) / 2.0\\n- ).sum()\\n+ per_target_loss = (positive_loss + negative_loss) / 2.0\\n+ if config.hard_negatives and config.hard_objective == \\\"pairwise\\\":\\n+ hard_loss = F.softplus(negative_logits[:, negatives.shape[1]:]\\n+ - positive_logits[:, None]).mean(dim=1)\\n+ per_target_loss = per_target_loss + config.hard_negative_weight * hard_loss\\n+ loss_sum = (selected_weights * per_target_loss).sum()\\n@@\\n (\\\"hard_pool_size\\\", 1024),\\n- (\\\"hard_negative_weight\\\", 0.5)))\\n+ (\\\"hard_negative_weight\\\", 0.5),\\n+ (\\\"hard_objective\\\", \\\"bce\\\"),\\n+ (\\\"hard_include_inbatch\\\", True)))\\n@@\\n parser.add_argument(\\\"--hard-warmup-epochs\\\", type=int, default=Config.hard_warmup_epochs)\\n+ parser.add_argument(\\\"--hard-objective\\\", choices=(\\\"bce\\\", \\\"pairwise\\\"), default=Config.hard_objective)\\n+ parser.add_argument(\\\"--hard-include-inbatch\\\", action=\\\"store_true\\\", default=Config.hard_include_inbatch)\\n@@\\n hard_warmup_epochs=args.hard_warmup_epochs,\\n+ hard_objective=args.hard_objective,\\n+ hard_include_inbatch=args.hard_include_inbatch,\\n*** End Patch\"));\ntext(await tools.exec_command({cmd:\"/opt/task-venv/bin/python /app/solution/train_current_model.py --stage validation --public-dir /task --output /app/solution/pairwise_validation.parquet --prepared-cache /app/solution/validation_cache.npz --checkpoint-dir /app/solution/pairwise_ckpt --resume-checkpoint /app/solution/production_ckpt/best.pt --epochs 2 --hard-negative-weight 0.1 --threads 8 --loss-microbatch-size 4096 > /app/solution/pairwise.json 2> /app/solution/pairwise.log\",yield_time_ms:1000}));\n" } ### Tool output · 2026-09-23T05:37:01.646Z Script completed Wall time 1.2 seconds Output: {} {"chunk_id":"af0a24","wall_time_seconds":1.000936555,"session_id":54558,"original_token_count":0,"output":""} ### Harness context { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-46dd55a2-e6eb-4f57-be95-df93ef788932", "changes": { "/app/solution/train_current_model.py": { "type": "update", "unified_diff": "@@ -64,2 +64,4 @@\n hard_warmup_epochs: int = 0\n+ hard_objective: str = \"pairwise\"\n+ hard_include_inbatch: bool = False\n negative_filter_scope: str = \"full-training-history\"\n@@ -1136,3 +1138,4 @@\n uniform = torch.randperm(model.n_items, generator=generator)[:config.hard_pool_size]\n- batch_pool = torch.unique(positives.cpu())\n+ batch_pool = (torch.unique(positives.cpu()) if config.hard_include_inbatch\n+ else torch.empty(0, dtype=torch.long))\n if len(batch_pool) > config.hard_pool_size:\n@@ -1237,3 +1240,3 @@\n )\n- if config.hard_negatives:\n+ if config.hard_negatives and config.hard_objective == \"bce\":\n hard_loss = F.softplus(negative_logits[:, negatives.shape[1]:]).mean(dim=1)\n@@ -1244,5 +1247,8 @@\n ).to(device)\n- loss_sum = (\n- selected_weights * (positive_loss + negative_loss) / 2.0\n- ).sum()\n+ per_target_loss = (positive_loss + negative_loss) / 2.0\n+ if config.hard_negatives and config.hard_objective == \"pairwise\":\n+ hard_loss = F.softplus(negative_logits[:, negatives.shape[1]:]\n+ - positive_logits[:, None]).mean(dim=1)\n+ per_target_loss = per_target_loss + config.hard_negative_weight * hard_loss\n+ loss_sum = (selected_weights * per_target_loss).sum()\n if not bool(torch.isfinite(loss_sum)):\n@@ -1707,3 +1713,5 @@\n (\"hard_pool_size\", 1024),\n- (\"hard_negative_weight\", 0.5)))\n+ (\"hard_negative_weight\", 0.5),\n+ (\"hard_objective\", \"bce\"),\n+ (\"hard_include_inbatch\", True)))\n )\n@@ -2021,2 +2029,4 @@\n parser.add_argument(\"--hard-warmup-epochs\", type=int, default=Config.hard_warmup_epochs)\n+ parser.add_argument(\"--hard-objective\", choices=(\"bce\", \"pairwise\"), default=Config.hard_objective)\n+ parser.add_argument(\"--hard-include-inbatch\", action=\"store_true\", default=Config.hard_include_inbatch)\n parser.add_argument(\n@@ -2126,2 +2136,4 @@\n hard_warmup_epochs=args.hard_warmup_epochs,\n+ hard_objective=args.hard_objective,\n+ hard_include_inbatch=args.hard_include_inbatch,\n loss_microbatch_size=args.loss_microbatch_size,\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": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "session_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "root_turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "response_id": "resp_03ec3525bea3ac86016ab36543a95087d294947ef25d7075be", "usage": { "input_tokens": 68874, "cached_input_tokens": 68352, "cache_write_input_tokens": 0, "output_tokens": 1808, "reasoning_output_tokens": 1028, "total_tokens": 70682 }, "turn_token_usage": { "input_tokens": 3026124, "cached_input_tokens": 2907264, "cache_write_input_tokens": 0, "output_tokens": 17440, "reasoning_output_tokens": 8321, "total_tokens": 3043564 }, "thread_token_usage": { "input_tokens": 3026124, "cached_input_tokens": 2907264, "cache_write_input_tokens": 0, "output_tokens": 17440, "reasoning_output_tokens": 8321, "total_tokens": 3043564 } } ## Step 47 · agent · 2026-09-23T05:37:22.217Z ### Thinking unavailable — no text recorded ## Step 48 · agent · 2026-09-23T05:37:31.316Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T05:37:32.403Z { "code": "text(await tools.exec_command({cmd:\"tail -n 8 /app/solution/pairwise.log && /opt/task-venv/bin/python /app/solution/test_mining.py\",yield_time_ms:1000,max_output_tokens:1200}));\ntext(await tools.clock__curr_time({}));\n" } ### Tool output · 2026-09-23T05:37:33.625Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"f371f4","wall_time_seconds":1.003681787,"session_id":50137,"original_token_count":193,"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/production_ckpt/best.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); recomputing the resume checkpoint's validation BCE\nresumed validation-BCE training: epoch=1 loss=0.643871\n"} {"current_time":"2026-09-23 05:37:33 UTC"} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "session_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "root_turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "response_id": "resp_03ec3525bea3ac86016ab3657dcfe887d2be0fe47fef5e4c10", "usage": { "input_tokens": 70741, "cached_input_tokens": 68736, "cache_write_input_tokens": 0, "output_tokens": 848, "reasoning_output_tokens": 775, "total_tokens": 71589 }, "turn_token_usage": { "input_tokens": 3096865, "cached_input_tokens": 2976000, "cache_write_input_tokens": 0, "output_tokens": 18288, "reasoning_output_tokens": 9096, "total_tokens": 3115153 }, "thread_token_usage": { "input_tokens": 3096865, "cached_input_tokens": 2976000, "cache_write_input_tokens": 0, "output_tokens": 18288, "reasoning_output_tokens": 9096, "total_tokens": 3115153 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-b842a0cf-50ea-4067-b01b-0b1b1193d8fb", "process_id": "50137", "command": [ "/bin/sh", "-lc", "tail -n 8 /app/solution/pairwise.log && /opt/task-venv/bin/python /app/solution/test_mining.py" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -n 8 /app/solution/pairwise.log && /opt/task-venv/bin/python /app/solution/test_mining.py" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "hard-mining filtering, uniqueness, determinism, and top-k checks passed\n", "stderr": "", "aggregated_output": "hard-mining filtering, uniqueness, determinism, and top-k checks passed\n", "exit_code": 0, "duration": { "secs": 5, "nanos": 82919706 }, "formatted_output": "hard-mining filtering, uniqueness, determinism, and top-k checks passed\n" } } ## Step 49 · agent · 2026-09-23T05:37:50.411Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T05:37:57.603Z { "code": "text(await tools.apply_patch(\"*** Begin Patch\\n*** Update File: /app/solution/train_current_model.py\\n@@\\n parser.add_argument(\\\"--prepare-only\\\", action=\\\"store_true\\\")\\n+ parser.add_argument(\\\"--train-only\\\", action=\\\"store_true\\\", help=\\\"save checkpoints without ranking\\\")\\n@@\\n args = parser.parse_args()\\n+ if args.train_only and args.checkpoint_dir is None:\\n+ parser.error(\\\"--train-only requires --checkpoint-dir\\\")\\n@@\\n model, training_history = train_model(\\n@@\\n args.resume_checkpoint.resolve() if args.resume_checkpoint else None,\\n )\\n+ if args.train_only:\\n+ print(json.dumps({\\\"stage\\\": args.stage, \\\"config\\\": asdict(config),\\n+ \\\"training_history\\\": training_history,\\n+ \\\"runtime_seconds\\\": time.monotonic() - started}, indent=2))\\n+ return 0\\n*** End Patch\"));\ntext(await tools.exec_command({cmd:\"tail -n 3 /app/solution/pairwise.log\",max_output_tokens:1000}));\n" } ### Tool output · 2026-09-23T05:37:57.713Z Script completed Wall time 0.1 seconds Output: {} {"chunk_id":"4e3fb1","wall_time_seconds":0.00000432,"exit_code":0,"original_token_count":90,"output":"using a fresh AdamW optimizer after loading /app/solution/production_ckpt/best.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); recomputing the resume checkpoint's validation BCE\nresumed validation-BCE training: epoch=1 loss=0.643871\n"} ### Harness context { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-8caf5cef-368e-4c7c-ae98-2b489a900433", "changes": { "/app/solution/train_current_model.py": { "type": "update", "unified_diff": "@@ -2098,3 +2098,6 @@\n parser.add_argument(\"--prepare-only\", action=\"store_true\")\n+ parser.add_argument(\"--train-only\", action=\"store_true\", help=\"save checkpoints without ranking\")\n args = parser.parse_args()\n+ if args.train_only and args.checkpoint_dir is None:\n+ parser.error(\"--train-only requires --checkpoint-dir\")\n \n@@ -2207,2 +2210,7 @@\n )\n+ if args.train_only:\n+ print(json.dumps({\"stage\": args.stage, \"config\": asdict(config),\n+ \"training_history\": training_history,\n+ \"runtime_seconds\": time.monotonic() - started}, indent=2))\n+ return 0\n ranking, embedding_diagnostics = rank_candidates(\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": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "session_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "root_turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "response_id": "resp_03ec3525bea3ac86016ab3659dd28087d2942d45a2fe144f67", "usage": { "input_tokens": 71894, "cached_input_tokens": 70528, "cache_write_input_tokens": 0, "output_tokens": 670, "reasoning_output_tokens": 420, "total_tokens": 72564 }, "turn_token_usage": { "input_tokens": 3168759, "cached_input_tokens": 3046528, "cache_write_input_tokens": 0, "output_tokens": 18958, "reasoning_output_tokens": 9516, "total_tokens": 3187717 }, "thread_token_usage": { "input_tokens": 3168759, "cached_input_tokens": 3046528, "cache_write_input_tokens": 0, "output_tokens": 18958, "reasoning_output_tokens": 9516, "total_tokens": 3187717 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-9f788922-a348-4e30-9fd7-70e5a33a9cbb", "process_id": "27843", "command": [ "/bin/sh", "-lc", "tail -n 3 /app/solution/pairwise.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "read", "cmd": "tail -n 3 /app/solution/pairwise.log", "name": "pairwise.log", "path": "/app/solution/pairwise.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "using a fresh AdamW optimizer after loading /app/solution/production_ckpt/best.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); recomputing the resume checkpoint's validation BCE\nresumed validation-BCE training: epoch=1 loss=0.643871\n", "stderr": "", "aggregated_output": "using a fresh AdamW optimizer after loading /app/solution/production_ckpt/best.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); recomputing the resume checkpoint's validation BCE\nresumed validation-BCE training: epoch=1 loss=0.643871\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4320 }, "formatted_output": "using a fresh AdamW optimizer after loading /app/solution/production_ckpt/best.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); recomputing the resume checkpoint's validation BCE\nresumed validation-BCE training: epoch=1 loss=0.643871\n" } } ## Step 50 · agent · 2026-09-23T05:38:02.337Z ### Thinking unavailable — no text recorded The revised run is training from the production checkpoint. It retains the original BCE loss and adds a pairwise penalty with weight 0.1. The hard pool now uses uniform catalog samples, removing the extra concentration on popular items from the first experiment’s in-batch pool. ### sleep · 2026-09-23T05:38:04.684Z { "duration_ms": 40000 } ### Tool output · 2026-09-23T05:38:44.714Z Wall time: 40.0035 seconds Sleep completed. ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "session_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "root_turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "response_id": "resp_03ec3525bea3ac86016ab365b7702c87d2ae41d3508e7eac5e", "usage": { "input_tokens": 72717, "cached_input_tokens": 71680, "cache_write_input_tokens": 0, "output_tokens": 127, "reasoning_output_tokens": 44, "total_tokens": 72844 }, "turn_token_usage": { "input_tokens": 3241476, "cached_input_tokens": 3118208, "cache_write_input_tokens": 0, "output_tokens": 19085, "reasoning_output_tokens": 9560, "total_tokens": 3260561 }, "thread_token_usage": { "input_tokens": 3241476, "cached_input_tokens": 3118208, "cache_write_input_tokens": 0, "output_tokens": 19085, "reasoning_output_tokens": 9560, "total_tokens": 3260561 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "Extension", "kind": "clock.sleep", "id": "call_lX0RlMcQPORd2CNpOc2ywwcA", "durationMs": 40000 } } ### exec · 2026-09-23T05:38:47.713Z { "code": "text(await tools.exec_command({cmd:\"tail -n 4 /app/solution/pairwise.log\",max_output_tokens:1000}));\ntext(await tools.clock__curr_time({}));\n" } ### Tool output · 2026-09-23T05:38:47.862Z Script completed Wall time 0.1 seconds Output: {"chunk_id":"3ef623","wall_time_seconds":0.00000476,"exit_code":0,"original_token_count":113,"output":"using a fresh AdamW optimizer after loading /app/solution/production_ckpt/best.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); recomputing the resume checkpoint's validation BCE\nresumed validation-BCE training: epoch=1 loss=0.643871\nepoch=2/2 batches=50 targets=206,457 batch_bce=0.581967 running_bce=0.592673 elapsed=58.6s\n"} {"current_time":"2026-09-23 05:38:47 UTC"} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "session_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "root_turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "response_id": "resp_03ec3525bea3ac86016ab365e4e11c87d2ab2234d0c0a0431e", "usage": { "input_tokens": 72867, "cached_input_tokens": 72576, "cache_write_input_tokens": 0, "output_tokens": 48, "reasoning_output_tokens": 0, "total_tokens": 72915 }, "turn_token_usage": { "input_tokens": 3314343, "cached_input_tokens": 3190784, "cache_write_input_tokens": 0, "output_tokens": 19133, "reasoning_output_tokens": 9560, "total_tokens": 3333476 }, "thread_token_usage": { "input_tokens": 3314343, "cached_input_tokens": 3190784, "cache_write_input_tokens": 0, "output_tokens": 19133, "reasoning_output_tokens": 9560, "total_tokens": 3333476 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-e105d546-6727-43b6-b6b3-4b0960ea98b0", "process_id": "84982", "command": [ "/bin/sh", "-lc", "tail -n 4 /app/solution/pairwise.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "read", "cmd": "tail -n 4 /app/solution/pairwise.log", "name": "pairwise.log", "path": "/app/solution/pairwise.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "using a fresh AdamW optimizer after loading /app/solution/production_ckpt/best.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); recomputing the resume checkpoint's validation BCE\nresumed validation-BCE training: epoch=1 loss=0.643871\nepoch=2/2 batches=50 targets=206,457 batch_bce=0.581967 running_bce=0.592673 elapsed=58.6s\n", "stderr": "", "aggregated_output": "using a fresh AdamW optimizer after loading /app/solution/production_ckpt/best.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); recomputing the resume checkpoint's validation BCE\nresumed validation-BCE training: epoch=1 loss=0.643871\nepoch=2/2 batches=50 targets=206,457 batch_bce=0.581967 running_bce=0.592673 elapsed=58.6s\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4760 }, "formatted_output": "using a fresh AdamW optimizer after loading /app/solution/production_ckpt/best.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); recomputing the resume checkpoint's validation BCE\nresumed validation-BCE training: epoch=1 loss=0.643871\nepoch=2/2 batches=50 targets=206,457 batch_bce=0.581967 running_bce=0.592673 elapsed=58.6s\n" } } ## Step 51 · agent · 2026-09-23T05:39:08.799Z ### Thinking unavailable — no text recorded ### sleep · 2026-09-23T05:39:08.853Z { "duration_ms": 40000 } ### Tool output · 2026-09-23T05:39:48.931Z Wall time: 40.0573 seconds Sleep completed. ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "session_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "root_turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "response_id": "resp_03ec3525bea3ac86016ab365e855b087d284375f9287e67074", "usage": { "input_tokens": 73120, "cached_input_tokens": 65280, "cache_write_input_tokens": 0, "output_tokens": 537, "reasoning_output_tokens": 516, "total_tokens": 73657 }, "turn_token_usage": { "input_tokens": 3387463, "cached_input_tokens": 3256064, "cache_write_input_tokens": 0, "output_tokens": 19670, "reasoning_output_tokens": 10076, "total_tokens": 3407133 }, "thread_token_usage": { "input_tokens": 3387463, "cached_input_tokens": 3256064, "cache_write_input_tokens": 0, "output_tokens": 19670, "reasoning_output_tokens": 10076, "total_tokens": 3407133 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "Extension", "kind": "clock.sleep", "id": "call_Zh8RmJcN04QMZ6LHZDGLYJsm", "durationMs": 40000 } } ### exec · 2026-09-23T05:39:54.092Z { "code": "text(await tools.exec_command({cmd:\"tail -n 3 /app/solution/pairwise.log\",max_output_tokens:1000}));\ntext(await tools.clock__curr_time({}));\n" } ### Tool output · 2026-09-23T05:39:54.215Z Script completed Wall time 0.1 seconds Output: {"chunk_id":"b9d043","wall_time_seconds":0.00000418,"exit_code":0,"original_token_count":60,"output":"resumed validation-BCE training: epoch=1 loss=0.643871\nepoch=2/2 batches=50 targets=206,457 batch_bce=0.581967 running_bce=0.592673 elapsed=58.6s\nepoch=2/2 batches=100 targets=402,892 batch_bce=0.590286 running_bce=0.589882 elapsed=112.0s\n"} {"current_time":"2026-09-23 05:39:54 UTC"} ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-bf988141-cbe6-4022-84a6-458afa31ad0c", "process_id": "97887", "command": [ "/bin/sh", "-lc", "tail -n 3 /app/solution/pairwise.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "read", "cmd": "tail -n 3 /app/solution/pairwise.log", "name": "pairwise.log", "path": "/app/solution/pairwise.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "resumed validation-BCE training: epoch=1 loss=0.643871\nepoch=2/2 batches=50 targets=206,457 batch_bce=0.581967 running_bce=0.592673 elapsed=58.6s\nepoch=2/2 batches=100 targets=402,892 batch_bce=0.590286 running_bce=0.589882 elapsed=112.0s\n", "stderr": "", "aggregated_output": "resumed validation-BCE training: epoch=1 loss=0.643871\nepoch=2/2 batches=50 targets=206,457 batch_bce=0.581967 running_bce=0.592673 elapsed=58.6s\nepoch=2/2 batches=100 targets=402,892 batch_bce=0.590286 running_bce=0.589882 elapsed=112.0s\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4180 }, "formatted_output": "resumed validation-BCE training: epoch=1 loss=0.643871\nepoch=2/2 batches=50 targets=206,457 batch_bce=0.581967 running_bce=0.592673 elapsed=58.6s\nepoch=2/2 batches=100 targets=402,892 batch_bce=0.590286 running_bce=0.589882 elapsed=112.0s\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "session_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "root_turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "response_id": "resp_03ec3525bea3ac86016ab36625226487d2b3a2029e05f0dde0", "usage": { "input_tokens": 73680, "cached_input_tokens": 72704, "cache_write_input_tokens": 0, "output_tokens": 48, "reasoning_output_tokens": 0, "total_tokens": 73728 }, "turn_token_usage": { "input_tokens": 3461143, "cached_input_tokens": 3328768, "cache_write_input_tokens": 0, "output_tokens": 19718, "reasoning_output_tokens": 10076, "total_tokens": 3480861 }, "thread_token_usage": { "input_tokens": 3461143, "cached_input_tokens": 3328768, "cache_write_input_tokens": 0, "output_tokens": 19718, "reasoning_output_tokens": 10076, "total_tokens": 3480861 } } ## Step 52 · agent · 2026-09-23T05:40:14.137Z ### Thinking unavailable — no text recorded The revised objective is decreasing from its starting value and is past 400,000 targets without numerical issues. After scoring it, I’ll reserve time for a fresh refit on `train_* + val_*` and validation of the final artifact. ### sleep · 2026-09-23T05:40:15.690Z { "duration_ms": 40000 } ### Tool output · 2026-09-23T05:40:55.711Z Wall time: 40.0173 seconds Sleep completed. ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "session_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "root_turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "response_id": "resp_03ec3525bea3ac86016ab3662a6a0087d29d7de0f8b12a784d", "usage": { "input_tokens": 73893, "cached_input_tokens": 73472, "cache_write_input_tokens": 0, "output_tokens": 593, "reasoning_output_tokens": 516, "total_tokens": 74486 }, "turn_token_usage": { "input_tokens": 3535036, "cached_input_tokens": 3402240, "cache_write_input_tokens": 0, "output_tokens": 20311, "reasoning_output_tokens": 10592, "total_tokens": 3555347 }, "thread_token_usage": { "input_tokens": 3535036, "cached_input_tokens": 3402240, "cache_write_input_tokens": 0, "output_tokens": 20311, "reasoning_output_tokens": 10592, "total_tokens": 3555347 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "Extension", "kind": "clock.sleep", "id": "call_kQjya1GyrfP9PproqkQbz2ra", "durationMs": 40000 } } ### exec · 2026-09-23T05:41:02.865Z { "code": "text(await tools.exec_command({cmd:\"tail -n 3 /app/solution/pairwise.log\",max_output_tokens:1000}));\ntext(await tools.clock__curr_time({}));\n" } ### Tool output · 2026-09-23T05:41:02.962Z Script completed Wall time 0.1 seconds Output: {"chunk_id":"717901","wall_time_seconds":0.000004431,"exit_code":0,"original_token_count":70,"output":"epoch=2/2 batches=50 targets=206,457 batch_bce=0.581967 running_bce=0.592673 elapsed=58.6s\nepoch=2/2 batches=100 targets=402,892 batch_bce=0.590286 running_bce=0.589882 elapsed=112.0s\nepoch=2/2 batches=150 targets=596,745 batch_bce=0.549047 running_bce=0.583433 elapsed=164.2s\n"} {"current_time":"2026-09-23 05:41:02 UTC"} ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-0a2f2096-e00b-4e06-be9c-704f07da435c", "process_id": "59852", "command": [ "/bin/sh", "-lc", "tail -n 3 /app/solution/pairwise.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "read", "cmd": "tail -n 3 /app/solution/pairwise.log", "name": "pairwise.log", "path": "/app/solution/pairwise.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "epoch=2/2 batches=50 targets=206,457 batch_bce=0.581967 running_bce=0.592673 elapsed=58.6s\nepoch=2/2 batches=100 targets=402,892 batch_bce=0.590286 running_bce=0.589882 elapsed=112.0s\nepoch=2/2 batches=150 targets=596,745 batch_bce=0.549047 running_bce=0.583433 elapsed=164.2s\n", "stderr": "", "aggregated_output": "epoch=2/2 batches=50 targets=206,457 batch_bce=0.581967 running_bce=0.592673 elapsed=58.6s\nepoch=2/2 batches=100 targets=402,892 batch_bce=0.590286 running_bce=0.589882 elapsed=112.0s\nepoch=2/2 batches=150 targets=596,745 batch_bce=0.549047 running_bce=0.583433 elapsed=164.2s\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4431 }, "formatted_output": "epoch=2/2 batches=50 targets=206,457 batch_bce=0.581967 running_bce=0.592673 elapsed=58.6s\nepoch=2/2 batches=100 targets=402,892 batch_bce=0.590286 running_bce=0.589882 elapsed=112.0s\nepoch=2/2 batches=150 targets=596,745 batch_bce=0.549047 running_bce=0.583433 elapsed=164.2s\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "session_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "root_turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "response_id": "resp_03ec3525bea3ac86016ab36667eb7087d294a629425ea59b20", "usage": { "input_tokens": 74509, "cached_input_tokens": 73728, "cache_write_input_tokens": 0, "output_tokens": 48, "reasoning_output_tokens": 0, "total_tokens": 74557 }, "turn_token_usage": { "input_tokens": 3609545, "cached_input_tokens": 3475968, "cache_write_input_tokens": 0, "output_tokens": 20359, "reasoning_output_tokens": 10592, "total_tokens": 3629904 }, "thread_token_usage": { "input_tokens": 3609545, "cached_input_tokens": 3475968, "cache_write_input_tokens": 0, "output_tokens": 20359, "reasoning_output_tokens": 10592, "total_tokens": 3629904 } } ## Step 53 · agent · 2026-09-23T05:41:21.422Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T05:41:30.849Z { "code": "text(await tools.apply_patch(\"*** Begin Patch\\n*** Update File: /app/solution/train_current_model.py\\n@@\\n- \\\"\\\"\\\"Compute one logical block's exact BCE in bounded-memory slices.\\\"\\\"\\\"\\n+ \\\"\\\"\\\"Compute weighted BCE and optional hard-pair ranking loss in slices.\\\"\\\"\\\"\\n@@\\n- \\\"\\\"\\\"Measure deterministic BCE on each user's chronological target suffix.\\\"\\\"\\\"\\n+ \\\"\\\"\\\"Measure the deterministic objective on each chronological target suffix.\\\"\\\"\\\"\\n@@\\n- \\\"baseline\\\": \\\"yambda-windowed-sequence-two-tower-base\\\",\\n+ \\\"baseline\\\": \\\"yambda-windowed-sequence-two-tower-hard-negative\\\",\\n@@\\n- \\\"model_selection\\\": \\\"chronological-validation-BCE-early-stopping\\\",\\n+ \\\"model_selection\\\": \\\"chronological-validation-objective-early-stopping\\\",\\n@@\\n- \\\"objective\\\": \\\"sample-weighted-BCE-at-every-timestep\\\",\\n+ \\\"objective\\\": \\\"sample-weighted-BCE-with-optional-hard-pair-ranking\\\",\\n \\\"negative_sampling\\\": (\\n- \\\"24-uniform-plus-8-propensity-corrected-inbatch\\\"\\n+ f\\\"24-uniform-plus-8-corrected-inbatch-plus-{config.hard_negatives}-mined\\\"\\n*** End Patch\"));\ntext(await tools.exec_command({cmd:\"tail -n 3 /app/solution/pairwise.log\",max_output_tokens:1000}));\n" } ### Tool output · 2026-09-23T05:41:30.967Z Script completed Wall time 0.1 seconds Output: {} {"chunk_id":"c6d4b2","wall_time_seconds":0.00000528,"exit_code":0,"original_token_count":70,"output":"epoch=2/2 batches=100 targets=402,892 batch_bce=0.590286 running_bce=0.589882 elapsed=112.0s\nepoch=2/2 batches=150 targets=596,745 batch_bce=0.549047 running_bce=0.583433 elapsed=164.2s\nepoch=2/2 batches=200 targets=790,005 batch_bce=0.581277 running_bce=0.582149 elapsed=217.1s\n"} ### Harness context { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-6996677a-2f79-45f2-8c26-4489d55cfb84", "changes": { "/app/solution/train_current_model.py": { "type": "update", "unified_diff": "@@ -1182,3 +1182,3 @@\n ) -> tuple[dict[str, float], torch.Tensor | None]:\n- \"\"\"Compute one logical block's exact BCE in bounded-memory slices.\"\"\"\n+ \"\"\"Compute weighted BCE and optional hard-pair ranking loss in slices.\"\"\"\n target_count = len(positive_items)\n@@ -1451,3 +1451,3 @@\n ) -> dict[str, float]:\n- \"\"\"Measure deterministic BCE on each user's chronological target suffix.\"\"\"\n+ \"\"\"Measure the deterministic objective on each chronological target suffix.\"\"\"\n model.eval()\n@@ -2227,3 +2227,3 @@\n {\n- \"baseline\": \"yambda-windowed-sequence-two-tower-base\",\n+ \"baseline\": \"yambda-windowed-sequence-two-tower-hard-negative\",\n \"stage\": args.stage,\n@@ -2241,3 +2241,3 @@\n \"bptt_hidden_state\": \"carried-and-detached\",\n- \"model_selection\": \"chronological-validation-BCE-early-stopping\",\n+ \"model_selection\": \"chronological-validation-objective-early-stopping\",\n \"loss_execution\": \"gradient-accumulated-microbatches\",\n@@ -2248,5 +2248,5 @@\n \"score\": \"raw-dot-product\",\n- \"objective\": \"sample-weighted-BCE-at-every-timestep\",\n+ \"objective\": \"sample-weighted-BCE-with-optional-hard-pair-ranking\",\n \"negative_sampling\": (\n- \"24-uniform-plus-8-propensity-corrected-inbatch\"\n+ f\"24-uniform-plus-8-corrected-inbatch-plus-{config.hard_negatives}-mined\"\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": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-7eebda23-c37d-4f9c-bea0-c35109f60215", "process_id": "73548", "command": [ "/bin/sh", "-lc", "tail -n 3 /app/solution/pairwise.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "read", "cmd": "tail -n 3 /app/solution/pairwise.log", "name": "pairwise.log", "path": "/app/solution/pairwise.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "epoch=2/2 batches=100 targets=402,892 batch_bce=0.590286 running_bce=0.589882 elapsed=112.0s\nepoch=2/2 batches=150 targets=596,745 batch_bce=0.549047 running_bce=0.583433 elapsed=164.2s\nepoch=2/2 batches=200 targets=790,005 batch_bce=0.581277 running_bce=0.582149 elapsed=217.1s\n", "stderr": "", "aggregated_output": "epoch=2/2 batches=100 targets=402,892 batch_bce=0.590286 running_bce=0.589882 elapsed=112.0s\nepoch=2/2 batches=150 targets=596,745 batch_bce=0.549047 running_bce=0.583433 elapsed=164.2s\nepoch=2/2 batches=200 targets=790,005 batch_bce=0.581277 running_bce=0.582149 elapsed=217.1s\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 5280 }, "formatted_output": "epoch=2/2 batches=100 targets=402,892 batch_bce=0.590286 running_bce=0.589882 elapsed=112.0s\nepoch=2/2 batches=150 targets=596,745 batch_bce=0.549047 running_bce=0.583433 elapsed=164.2s\nepoch=2/2 batches=200 targets=790,005 batch_bce=0.581277 running_bce=0.582149 elapsed=217.1s\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "session_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "root_turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "response_id": "resp_03ec3525bea3ac86016ab3666f7f9487d2b5b663df5c078b73", "usage": { "input_tokens": 74739, "cached_input_tokens": 74368, "cache_write_input_tokens": 0, "output_tokens": 839, "reasoning_output_tokens": 516, "total_tokens": 75578 }, "turn_token_usage": { "input_tokens": 3684284, "cached_input_tokens": 3550336, "cache_write_input_tokens": 0, "output_tokens": 21198, "reasoning_output_tokens": 11108, "total_tokens": 3705482 }, "thread_token_usage": { "input_tokens": 3684284, "cached_input_tokens": 3550336, "cache_write_input_tokens": 0, "output_tokens": 21198, "reasoning_output_tokens": 11108, "total_tokens": 3705482 } } ## Step 54 · agent · 2026-09-23T05:41:43.698Z ### Thinking unavailable — no text recorded I’m also making ranking writes atomic, so an interrupted refit cannot leave a partial file in place of the valid submission. ### exec · 2026-09-23T05:41:50.845Z { "code": "text(await tools.apply_patch(\"*** Begin Patch\\n*** Update File: /app/solution/train_current_model.py\\n@@\\n def write_ranking(frame: pd.DataFrame, output: Path) -> None:\\n output.parent.mkdir(parents=True, exist_ok=True)\\n+ temporary = output.with_name(output.name + \\\".tmp\\\")\\n if output.suffix == \\\".parquet\\\":\\n- frame.to_parquet(output, index=False, compression=\\\"zstd\\\")\\n+ frame.to_parquet(temporary, index=False, compression=\\\"zstd\\\")\\n elif output.suffix == \\\".csv\\\":\\n- frame.to_csv(output, index=False)\\n+ frame.to_csv(temporary, index=False)\\n else:\\n raise ValueError(\\\"--output must end in .parquet or .csv\\\")\\n+ temporary.replace(output)\\n*** End Patch\"));\ntext(await tools.exec_command({cmd:\"tail -n 3 /app/solution/pairwise.log\",max_output_tokens:1000}));\n" } ### Tool output · 2026-09-23T05:41:51.019Z Script completed Wall time 0.1 seconds Output: {} {"chunk_id":"4e772c","wall_time_seconds":0.00000683,"exit_code":0,"original_token_count":70,"output":"epoch=2/2 batches=100 targets=402,892 batch_bce=0.590286 running_bce=0.589882 elapsed=112.0s\nepoch=2/2 batches=150 targets=596,745 batch_bce=0.549047 running_bce=0.583433 elapsed=164.2s\nepoch=2/2 batches=200 targets=790,005 batch_bce=0.581277 running_bce=0.582149 elapsed=217.1s\n"} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "session_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "root_turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "response_id": "resp_03ec3525bea3ac86016ab3668b2b0087d292fa08289fb48ef4", "usage": { "input_tokens": 75747, "cached_input_tokens": 12160, "cache_write_input_tokens": 0, "output_tokens": 513, "reasoning_output_tokens": 249, "total_tokens": 76260 }, "turn_token_usage": { "input_tokens": 3760031, "cached_input_tokens": 3562496, "cache_write_input_tokens": 0, "output_tokens": 21711, "reasoning_output_tokens": 11357, "total_tokens": 3781742 }, "thread_token_usage": { "input_tokens": 3760031, "cached_input_tokens": 3562496, "cache_write_input_tokens": 0, "output_tokens": 21711, "reasoning_output_tokens": 11357, "total_tokens": 3781742 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-7dd8c072-b107-43b3-969d-4d1c08090914", "changes": { "/app/solution/train_current_model.py": { "type": "update", "unified_diff": "@@ -2001,8 +2001,10 @@\n output.parent.mkdir(parents=True, exist_ok=True)\n+ temporary = output.with_name(output.name + \".tmp\")\n if output.suffix == \".parquet\":\n- frame.to_parquet(output, index=False, compression=\"zstd\")\n+ frame.to_parquet(temporary, index=False, compression=\"zstd\")\n elif output.suffix == \".csv\":\n- frame.to_csv(output, index=False)\n+ frame.to_csv(temporary, index=False)\n else:\n raise ValueError(\"--output must end in .parquet or .csv\")\n+ temporary.replace(output)\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": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-6f0ebd22-7b79-46a3-9295-1db5784c786d", "process_id": "54987", "command": [ "/bin/sh", "-lc", "tail -n 3 /app/solution/pairwise.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "read", "cmd": "tail -n 3 /app/solution/pairwise.log", "name": "pairwise.log", "path": "/app/solution/pairwise.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "epoch=2/2 batches=100 targets=402,892 batch_bce=0.590286 running_bce=0.589882 elapsed=112.0s\nepoch=2/2 batches=150 targets=596,745 batch_bce=0.549047 running_bce=0.583433 elapsed=164.2s\nepoch=2/2 batches=200 targets=790,005 batch_bce=0.581277 running_bce=0.582149 elapsed=217.1s\n", "stderr": "", "aggregated_output": "epoch=2/2 batches=100 targets=402,892 batch_bce=0.590286 running_bce=0.589882 elapsed=112.0s\nepoch=2/2 batches=150 targets=596,745 batch_bce=0.549047 running_bce=0.583433 elapsed=164.2s\nepoch=2/2 batches=200 targets=790,005 batch_bce=0.581277 running_bce=0.582149 elapsed=217.1s\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 6830 }, "formatted_output": "epoch=2/2 batches=100 targets=402,892 batch_bce=0.590286 running_bce=0.589882 elapsed=112.0s\nepoch=2/2 batches=150 targets=596,745 batch_bce=0.549047 running_bce=0.583433 elapsed=164.2s\nepoch=2/2 batches=200 targets=790,005 batch_bce=0.581277 running_bce=0.582149 elapsed=217.1s\n" } } ### sleep · 2026-09-23T05:41:54.097Z { "duration_ms": 40000 } ### Tool output · 2026-09-23T05:42:34.113Z Wall time: 40.0119 seconds Sleep completed. ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "session_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "root_turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "response_id": "resp_03ec3525bea3ac86016ab3669f31a887d2a4ca9f03d5485996", "usage": { "input_tokens": 76427, "cached_input_tokens": 75520, "cache_write_input_tokens": 0, "output_tokens": 19, "reasoning_output_tokens": 0, "total_tokens": 76446 }, "turn_token_usage": { "input_tokens": 3836458, "cached_input_tokens": 3638016, "cache_write_input_tokens": 0, "output_tokens": 21730, "reasoning_output_tokens": 11357, "total_tokens": 3858188 }, "thread_token_usage": { "input_tokens": 3836458, "cached_input_tokens": 3638016, "cache_write_input_tokens": 0, "output_tokens": 21730, "reasoning_output_tokens": 11357, "total_tokens": 3858188 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "Extension", "kind": "clock.sleep", "id": "call_G9z7hDX40Set6ub9S2zI3sT9", "durationMs": 40000 } } ### exec · 2026-09-23T05:42:38.346Z { "code": "text(await tools.exec_command({cmd:\"tail -n 5 /app/solution/pairwise.log\",max_output_tokens:1000}));\ntext(await tools.clock__curr_time({}));\n" } ### Tool output · 2026-09-23T05:42:38.482Z Script completed Wall time 0.1 seconds Output: {"chunk_id":"f18aee","wall_time_seconds":0.00000388,"exit_code":0,"original_token_count":104,"output":"epoch=2/2 batches=250 targets=987,417 batch_bce=0.583825 running_bce=0.578332 elapsed=271.4s\nsaved epoch checkpoint: /app/solution/pairwise_ckpt/epoch-02.pt\nepoch=2/2 train_loss=0.57809 validation_loss=0.56452 pair_accuracy=0.822 train_targets=1,052,953 validation_targets=130,103 train_seconds=289.5 validation_seconds=19.3\nrestored best validation-BCE checkpoint: epoch=2 loss=0.564516\nencoded users=1,600/8,456\n"} {"current_time":"2026-09-23 05:42:38 UTC"} ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-dd0d14f5-a125-495e-81dd-7ced438accc5", "process_id": "89190", "command": [ "/bin/sh", "-lc", "tail -n 5 /app/solution/pairwise.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "read", "cmd": "tail -n 5 /app/solution/pairwise.log", "name": "pairwise.log", "path": "/app/solution/pairwise.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "epoch=2/2 batches=250 targets=987,417 batch_bce=0.583825 running_bce=0.578332 elapsed=271.4s\nsaved epoch checkpoint: /app/solution/pairwise_ckpt/epoch-02.pt\nepoch=2/2 train_loss=0.57809 validation_loss=0.56452 pair_accuracy=0.822 train_targets=1,052,953 validation_targets=130,103 train_seconds=289.5 validation_seconds=19.3\nrestored best validation-BCE checkpoint: epoch=2 loss=0.564516\nencoded users=1,600/8,456\n", "stderr": "", "aggregated_output": "epoch=2/2 batches=250 targets=987,417 batch_bce=0.583825 running_bce=0.578332 elapsed=271.4s\nsaved epoch checkpoint: /app/solution/pairwise_ckpt/epoch-02.pt\nepoch=2/2 train_loss=0.57809 validation_loss=0.56452 pair_accuracy=0.822 train_targets=1,052,953 validation_targets=130,103 train_seconds=289.5 validation_seconds=19.3\nrestored best validation-BCE checkpoint: epoch=2 loss=0.564516\nencoded users=1,600/8,456\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 3880 }, "formatted_output": "epoch=2/2 batches=250 targets=987,417 batch_bce=0.583825 running_bce=0.578332 elapsed=271.4s\nsaved epoch checkpoint: /app/solution/pairwise_ckpt/epoch-02.pt\nepoch=2/2 train_loss=0.57809 validation_loss=0.56452 pair_accuracy=0.822 train_targets=1,052,953 validation_targets=130,103 train_seconds=289.5 validation_seconds=19.3\nrestored best validation-BCE checkpoint: epoch=2 loss=0.564516\nencoded users=1,600/8,456\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "session_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "root_turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "response_id": "resp_03ec3525bea3ac86016ab366ca636487d2b640656dcbe84fd0", "usage": { "input_tokens": 76469, "cached_input_tokens": 76288, "cache_write_input_tokens": 0, "output_tokens": 48, "reasoning_output_tokens": 0, "total_tokens": 76517 }, "turn_token_usage": { "input_tokens": 3912927, "cached_input_tokens": 3714304, "cache_write_input_tokens": 0, "output_tokens": 21778, "reasoning_output_tokens": 11357, "total_tokens": 3934705 }, "thread_token_usage": { "input_tokens": 3912927, "cached_input_tokens": 3714304, "cache_write_input_tokens": 0, "output_tokens": 21778, "reasoning_output_tokens": 11357, "total_tokens": 3934705 } } ## Step 55 · agent · 2026-09-23T05:42:42.454Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T05:42:44.107Z { "code": "text(await tools.write_stdin({session_id:90070,chars:\"\",yield_time_ms:1000,max_output_tokens:1000}));\ntext(await tools.write_stdin({session_id:50137,chars:\"\",yield_time_ms:1000,max_output_tokens:1000}));\n" } ### Tool output · 2026-09-23T05:42:44.209Z Script completed Wall time 0.0 seconds Output: {"chunk_id":"a82c85","wall_time_seconds":0.00000224,"exit_code":0,"original_token_count":11,"output":"Hard-negative top10 catalog coverage: 1457\n"} {"chunk_id":"56f77f","wall_time_seconds":9e-7,"exit_code":0,"original_token_count":18,"output":"hard-mining filtering, uniqueness, determinism, and top-k checks passed\n"} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "session_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "root_turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "response_id": "resp_03ec3525bea3ac86016ab366ceb1fc87d29ce9f4df6339be4c", "usage": { "input_tokens": 76728, "cached_input_tokens": 76288, "cache_write_input_tokens": 0, "output_tokens": 110, "reasoning_output_tokens": 40, "total_tokens": 76838 }, "turn_token_usage": { "input_tokens": 3989655, "cached_input_tokens": 3790592, "cache_write_input_tokens": 0, "output_tokens": 21888, "reasoning_output_tokens": 11397, "total_tokens": 4011543 }, "thread_token_usage": { "input_tokens": 3989655, "cached_input_tokens": 3790592, "cache_write_input_tokens": 0, "output_tokens": 21888, "reasoning_output_tokens": 11397, "total_tokens": 4011543 } } ## Step 56 · agent · 2026-09-23T05:43:05.255Z ### Thinking unavailable — no text recorded ## Step 57 · agent · 2026-09-23T05:43:13.648Z ### Thinking unavailable — no text recorded The revised run finished with a lower held-out objective and higher sampled pairwise accuracy. Its public ranking is being generated now. I’ll also test a matched second pass without hard negatives to separate the effect of extra training from the effect of the new loss. ### sleep · 2026-09-23T05:43:15.123Z { "duration_ms": 40000 } ### Tool output · 2026-09-23T05:43:55.184Z Wall time: 40.0259 seconds Sleep completed. ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "session_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "root_turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "response_id": "resp_03ec3525bea3ac86016ab366d5631087d2bbe10188e70fbd3e", "usage": { "input_tokens": 76953, "cached_input_tokens": 76544, "cache_write_input_tokens": 0, "output_tokens": 818, "reasoning_output_tokens": 739, "total_tokens": 77771 }, "turn_token_usage": { "input_tokens": 4066608, "cached_input_tokens": 3867136, "cache_write_input_tokens": 0, "output_tokens": 22706, "reasoning_output_tokens": 12136, "total_tokens": 4089314 }, "thread_token_usage": { "input_tokens": 4066608, "cached_input_tokens": 3867136, "cache_write_input_tokens": 0, "output_tokens": 22706, "reasoning_output_tokens": 12136, "total_tokens": 4089314 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "Extension", "kind": "clock.sleep", "id": "call_Vlz5GFVdGIsLdAVOzIGPEEZt", "durationMs": 40000 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-b4a711bc-c0d4-4f63-9167-b49bf8699473", "process_id": "54558", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python /app/solution/train_current_model.py --stage validation --public-dir /task --output /app/solution/pairwise_validation.parquet --prepared-cache /app/solution/validation_cache.npz --checkpoint-dir /app/solution/pairwise_ckpt --resume-checkpoint /app/solution/production_ckpt/best.pt --epochs 2 --hard-negative-weight 0.1 --threads 8 --loss-microbatch-size 4096 > /app/solution/pairwise.json 2> /app/solution/pairwise.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 --output /app/solution/pairwise_validation.parquet --prepared-cache /app/solution/validation_cache.npz --checkpoint-dir /app/solution/pairwise_ckpt --resume-checkpoint /app/solution/production_ckpt/best.pt --epochs 2 --hard-negative-weight 0.1 --threads 8 --loss-microbatch-size 4096 > /app/solution/pairwise.json 2> /app/solution/pairwise.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 417, "nanos": 735845513 }, "formatted_output": "" } } ### exec · 2026-09-23T05:43:58.726Z { "code": "text(await tools.exec_command({cmd:\"tail -n 5 /app/solution/pairwise.log && ls -lh /app/solution/pairwise_validation.parquet\",max_output_tokens:1000}));\ntext(await tools.clock__curr_time({}));\n" } ### Tool output · 2026-09-23T05:43:58.834Z Script completed Wall time 0.0 seconds Output: {"chunk_id":"6131c2","wall_time_seconds":0.000006111,"exit_code":0,"original_token_count":53,"output":"ranked 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-rw-r--r-- 1 agent agent 1.4M Sep 23 05:43 /app/solution/pairwise_validation.parquet\n"} {"current_time":"2026-09-23 05:43:58 UTC"} ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-f8802455-c68f-4d36-b290-13a3fc95396d", "process_id": "63791", "command": [ "/bin/sh", "-lc", "tail -n 5 /app/solution/pairwise.log && ls -lh /app/solution/pairwise_validation.parquet" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "read", "cmd": "tail -n 5 /app/solution/pairwise.log", "name": "pairwise.log", "path": "/app/solution/pairwise.log" }, { "type": "list_files", "cmd": "ls -lh /app/solution/pairwise_validation.parquet", "path": "pairwise_validation.parquet" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "ranked 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-rw-r--r-- 1 agent agent 1.4M Sep 23 05:43 /app/solution/pairwise_validation.parquet\n", "stderr": "", "aggregated_output": "ranked 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-rw-r--r-- 1 agent agent 1.4M Sep 23 05:43 /app/solution/pairwise_validation.parquet\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 6111 }, "formatted_output": "ranked 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-rw-r--r-- 1 agent agent 1.4M Sep 23 05:43 /app/solution/pairwise_validation.parquet\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "session_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "root_turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "response_id": "resp_03ec3525bea3ac86016ab3671b75c887d2a841099187e8877c", "usage": { "input_tokens": 77794, "cached_input_tokens": 76800, "cache_write_input_tokens": 0, "output_tokens": 62, "reasoning_output_tokens": 0, "total_tokens": 77856 }, "turn_token_usage": { "input_tokens": 4144402, "cached_input_tokens": 3943936, "cache_write_input_tokens": 0, "output_tokens": 22768, "reasoning_output_tokens": 12136, "total_tokens": 4167170 }, "thread_token_usage": { "input_tokens": 4144402, "cached_input_tokens": 3943936, "cache_write_input_tokens": 0, "output_tokens": 22768, "reasoning_output_tokens": 12136, "total_tokens": 4167170 } } ### exec · 2026-09-23T05:44:08.868Z { "code": "text(await tools.exec_command({cmd:\"/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/pairwise_validation.parquet > /app/solution/pairwise_score.json && cat /app/solution/pairwise_score.json\",yield_time_ms:1000,max_output_tokens:1000}));\ntext(await tools.exec_command({cmd:\"/opt/task-venv/bin/python /app/solution/train_current_model.py --stage validation --public-dir /task --output /app/solution/control_validation.parquet --prepared-cache /app/solution/validation_cache.npz --checkpoint-dir /app/solution/control_ckpt --resume-checkpoint /app/solution/production_ckpt/best.pt --epochs 2 --hard-negatives 0 --hard-negative-weight 0.1 --threads 8 --loss-microbatch-size 4096 > /app/solution/control.json 2> /app/solution/control.log\",yield_time_ms:1000}));\ntext(await tools.write_stdin({session_id:54558,chars:\"\",yield_time_ms:1000,max_output_tokens:1000}));\n" } ### Tool output · 2026-09-23T05:44:11.212Z Script completed Wall time 2.3 seconds Output: {"chunk_id":"b097e1","wall_time_seconds":1.000373283,"session_id":69389,"original_token_count":0,"output":""} {"chunk_id":"a9d68c","wall_time_seconds":1.00063382,"session_id":93457,"original_token_count":0,"output":""} {"chunk_id":"7a2986","wall_time_seconds":0.00000142,"exit_code":0,"original_token_count":0,"output":""} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "session_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "root_turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "response_id": "resp_03ec3525bea3ac86016ab3671f356887d284dd89068ddf8233", "usage": { "input_tokens": 78023, "cached_input_tokens": 77568, "cache_write_input_tokens": 0, "output_tokens": 240, "reasoning_output_tokens": 0, "total_tokens": 78263 }, "turn_token_usage": { "input_tokens": 4222425, "cached_input_tokens": 4021504, "cache_write_input_tokens": 0, "output_tokens": 23008, "reasoning_output_tokens": 12136, "total_tokens": 4245433 }, "thread_token_usage": { "input_tokens": 4222425, "cached_input_tokens": 4021504, "cache_write_input_tokens": 0, "output_tokens": 23008, "reasoning_output_tokens": 12136, "total_tokens": 4245433 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-719695e9-3d3f-4606-9a3e-0c0eb95d0f39", "process_id": "69389", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/pairwise_validation.parquet > /app/solution/pairwise_score.json && cat /app/solution/pairwise_score.json" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/pairwise_validation.parquet > /app/solution/pairwise_score.json && cat /app/solution/pairwise_score.json" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{\n \"model\": \"/app/solution/pairwise_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.020386,\n \"quality_users\": 4263\n }\n}\n", "stderr": "", "aggregated_output": "{\n \"model\": \"/app/solution/pairwise_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.020386,\n \"quality_users\": 4263\n }\n}\n", "exit_code": 0, "duration": { "secs": 3, "nanos": 212425270 }, "formatted_output": "{\n \"model\": \"/app/solution/pairwise_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.020386,\n \"quality_users\": 4263\n }\n}\n" } } ## Step 58 · agent · 2026-09-23T05:44:16.546Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T05:44:18.447Z { "code": "text(await tools.write_stdin({session_id:69389,chars:\"\",yield_time_ms:1000,max_output_tokens:1000}));\ntext(await tools.exec_command({cmd:\"tail -n 4 /app/solution/control.log\",max_output_tokens:1000}));\n" } ### Tool output · 2026-09-23T05:44:18.611Z Script completed Wall time 0.0 seconds Output: {"chunk_id":"0d1e0a","wall_time_seconds":0.00000249,"exit_code":0,"original_token_count":35,"output":"{\n \"model\": \"/app/solution/pairwise_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.020386,\n \"quality_users\": 4263\n }\n}\n"} {"chunk_id":"ba0659","wall_time_seconds":0.00000294,"exit_code":0,"original_token_count":80,"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\n"} ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-0c430c71-fdb6-43a7-9354-771764f77d09", "process_id": "41750", "command": [ "/bin/sh", "-lc", "tail -n 4 /app/solution/control.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "read", "cmd": "tail -n 4 /app/solution/control.log", "name": "control.log", "path": "/app/solution/control.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\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\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 2940 }, "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\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "session_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "root_turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "response_id": "resp_03ec3525bea3ac86016ab3672b616487d291c292421819a252", "usage": { "input_tokens": 78391, "cached_input_tokens": 77824, "cache_write_input_tokens": 0, "output_tokens": 173, "reasoning_output_tokens": 105, "total_tokens": 78564 }, "turn_token_usage": { "input_tokens": 4300816, "cached_input_tokens": 4099328, "cache_write_input_tokens": 0, "output_tokens": 23181, "reasoning_output_tokens": 12241, "total_tokens": 4323997 }, "thread_token_usage": { "input_tokens": 4300816, "cached_input_tokens": 4099328, "cache_write_input_tokens": 0, "output_tokens": 23181, "reasoning_output_tokens": 12241, "total_tokens": 4323997 } } ## Step 59 · agent · 2026-09-23T05:44:40.701Z ### Thinking unavailable — no text recorded ## Step 60 · agent · 2026-09-23T05:44:57.997Z ### Thinking unavailable — no text recorded ## Step 61 · agent · 2026-09-23T05:45:16.663Z ### Thinking unavailable — no text recorded ## Step 62 · agent · 2026-09-23T05:45:35.699Z ### Thinking unavailable — no text recorded ## Step 63 · agent · 2026-09-23T05:45:46.073Z ### Thinking unavailable — no text recorded The revised pairwise variant also fell short: **0.020386**, versus the production score of **0.021280**. The control run is still underway. I’m preparing one further test: sampled softmax over a larger shared uniform pool, which emphasizes high-scoring negatives through the loss without deliberately oversampling popular tracks. ### exec · 2026-09-23T05:45:48.995Z { "code": "text(await tools.clock__curr_time({}));\ntext(await tools.exec_command({cmd:\"tail -n 3 /app/solution/control.log\",max_output_tokens:1000}));\n" } ### Tool output · 2026-09-23T05:45:49.090Z Script completed Wall time 0.1 seconds Output: {"current_time":"2026-09-23 05:45:49 UTC"} {"chunk_id":"ae4f18","wall_time_seconds":0.000003071,"exit_code":0,"original_token_count":92,"output":"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); recomputing the resume checkpoint's validation BCE\nresumed validation-BCE training: epoch=1 loss=0.496636\nepoch=2/2 batches=50 targets=206,457 batch_bce=0.461053 running_bce=0.470153 elapsed=50.3s\n"} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "session_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "root_turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "response_id": "resp_03ec3525bea3ac86016ab3673303ec87d2a5deb5fce8a1211a", "usage": { "input_tokens": 78815, "cached_input_tokens": 78208, "cache_write_input_tokens": 0, "output_tokens": 2435, "reasoning_output_tokens": 2313, "total_tokens": 81250 }, "turn_token_usage": { "input_tokens": 4379631, "cached_input_tokens": 4177536, "cache_write_input_tokens": 0, "output_tokens": 25616, "reasoning_output_tokens": 14554, "total_tokens": 4405247 }, "thread_token_usage": { "input_tokens": 4379631, "cached_input_tokens": 4177536, "cache_write_input_tokens": 0, "output_tokens": 25616, "reasoning_output_tokens": 14554, "total_tokens": 4405247 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-04447199-17d6-4c0a-b1b5-9dc7c55b1f7a", "process_id": "65875", "command": [ "/bin/sh", "-lc", "tail -n 3 /app/solution/control.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "read", "cmd": "tail -n 3 /app/solution/control.log", "name": "control.log", "path": "/app/solution/control.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "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); recomputing the resume checkpoint's validation BCE\nresumed validation-BCE training: epoch=1 loss=0.496636\nepoch=2/2 batches=50 targets=206,457 batch_bce=0.461053 running_bce=0.470153 elapsed=50.3s\n", "stderr": "", "aggregated_output": "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); recomputing the resume checkpoint's validation BCE\nresumed validation-BCE training: epoch=1 loss=0.496636\nepoch=2/2 batches=50 targets=206,457 batch_bce=0.461053 running_bce=0.470153 elapsed=50.3s\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 3071 }, "formatted_output": "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); recomputing the resume checkpoint's validation BCE\nresumed validation-BCE training: epoch=1 loss=0.496636\nepoch=2/2 batches=50 targets=206,457 batch_bce=0.461053 running_bce=0.470153 elapsed=50.3s\n" } } ## Step 64 · agent · 2026-09-23T05:45:54.331Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T05:45:55.140Z { "code": "text(await tools.exec_command({cmd:\"sed -n '1190,1288p' /app/solution/train_current_model.py\",max_output_tokens:3000}));\n" } ### Tool output · 2026-09-23T05:45:55.235Z Script completed Wall time 0.1 seconds Output: {"chunk_id":"a1121b","wall_time_seconds":0.000005911,"exit_code":0,"original_token_count":1116,"output":" \"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 selected_negatives = negatives[begin:end]\n if config.hard_negatives:\n hard_items = mine_hard_negatives(\n model, query_vectors, positive_items[begin:end],\n selected_negatives, prepared.liked_states[selected_states],\n user_rows[begin:end], known_positive_packed, config,\n )\n selected_negatives = torch.cat([selected_negatives, hard_items], dim=1)\n positive_vectors, negative_vectors = candidate_vectors(\n model,\n positive_items[begin:end],\n selected_negatives,\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[:, :negatives.shape[1]], negative_weights[begin:end], config\n )\n if config.hard_negatives and config.hard_objective == \"bce\":\n hard_loss = F.softplus(negative_logits[:, negatives.shape[1]:]).mean(dim=1)\n negative_loss = ((1.0 - config.hard_negative_weight) * negative_loss\n + config.hard_negative_weight * hard_loss)\n selected_weights = torch.from_numpy(\n sample_weights[begin:end].astype(np.float32, copy=False)\n ).to(device)\n per_target_loss = (positive_loss + negative_loss) / 2.0\n if config.hard_negatives and config.hard_objective == \"pairwise\":\n hard_loss = F.softplus(negative_logits[:, negatives.shape[1]:]\n - positive_logits[:, None]).mean(dim=1)\n per_target_loss = per_target_loss + config.hard_negative_weight * hard_loss\n loss_sum = (selected_weights * per_target_loss).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 sampled_logits = negative_logits[:, :negatives.shape[1]].detach()\n statistics[\"negative_sum\"] += float(sampled_logits.sum().cpu())\n statistics[\"pair_correct\"] += float(\n (positive_logits.detach()[:, None] > sampled_logits).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"} ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-40671637-e90c-438f-b700-7e951026cda9", "process_id": "91216", "command": [ "/bin/sh", "-lc", "sed -n '1190,1288p' /app/solution/train_current_model.py" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "read", "cmd": "sed -n '1190,1288p' /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": " \"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 selected_negatives = negatives[begin:end]\n if config.hard_negatives:\n hard_items = mine_hard_negatives(\n model, query_vectors, positive_items[begin:end],\n selected_negatives, prepared.liked_states[selected_states],\n user_rows[begin:end], known_positive_packed, config,\n )\n selected_negatives = torch.cat([selected_negatives, hard_items], dim=1)\n positive_vectors, negative_vectors = candidate_vectors(\n model,\n positive_items[begin:end],\n selected_negatives,\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[:, :negatives.shape[1]], negative_weights[begin:end], config\n )\n if config.hard_negatives and config.hard_objective == \"bce\":\n hard_loss = F.softplus(negative_logits[:, negatives.shape[1]:]).mean(dim=1)\n negative_loss = ((1.0 - config.hard_negative_weight) * negative_loss\n + config.hard_negative_weight * hard_loss)\n selected_weights = torch.from_numpy(\n sample_weights[begin:end].astype(np.float32, copy=False)\n ).to(device)\n per_target_loss = (positive_loss + negative_loss) / 2.0\n if config.hard_negatives and config.hard_objective == \"pairwise\":\n hard_loss = F.softplus(negative_logits[:, negatives.shape[1]:]\n - positive_logits[:, None]).mean(dim=1)\n per_target_loss = per_target_loss + config.hard_negative_weight * hard_loss\n loss_sum = (selected_weights * per_target_loss).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 sampled_logits = negative_logits[:, :negatives.shape[1]].detach()\n statistics[\"negative_sum\"] += float(sampled_logits.sum().cpu())\n statistics[\"pair_correct\"] += float(\n (positive_logits.detach()[:, None] > sampled_logits).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", "stderr": "", "aggregated_output": " \"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 selected_negatives = negatives[begin:end]\n if config.hard_negatives:\n hard_items = mine_hard_negatives(\n model, query_vectors, positive_items[begin:end],\n selected_negatives, prepared.liked_states[selected_states],\n user_rows[begin:end], known_positive_packed, config,\n )\n selected_negatives = torch.cat([selected_negatives, hard_items], dim=1)\n positive_vectors, negative_vectors = candidate_vectors(\n model,\n positive_items[begin:end],\n selected_negatives,\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[:, :negatives.shape[1]], negative_weights[begin:end], config\n )\n if config.hard_negatives and config.hard_objective == \"bce\":\n hard_loss = F.softplus(negative_logits[:, negatives.shape[1]:]).mean(dim=1)\n negative_loss = ((1.0 - config.hard_negative_weight) * negative_loss\n + config.hard_negative_weight * hard_loss)\n selected_weights = torch.from_numpy(\n sample_weights[begin:end].astype(np.float32, copy=False)\n ).to(device)\n per_target_loss = (positive_loss + negative_loss) / 2.0\n if config.hard_negatives and config.hard_objective == \"pairwise\":\n hard_loss = F.softplus(negative_logits[:, negatives.shape[1]:]\n - positive_logits[:, None]).mean(dim=1)\n per_target_loss = per_target_loss + config.hard_negative_weight * hard_loss\n loss_sum = (selected_weights * per_target_loss).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 sampled_logits = negative_logits[:, :negatives.shape[1]].detach()\n statistics[\"negative_sum\"] += float(sampled_logits.sum().cpu())\n statistics[\"pair_correct\"] += float(\n (positive_logits.detach()[:, None] > sampled_logits).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", "exit_code": 0, "duration": { "secs": 0, "nanos": 5911 }, "formatted_output": " \"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 selected_negatives = negatives[begin:end]\n if config.hard_negatives:\n hard_items = mine_hard_negatives(\n model, query_vectors, positive_items[begin:end],\n selected_negatives, prepared.liked_states[selected_states],\n user_rows[begin:end], known_positive_packed, config,\n )\n selected_negatives = torch.cat([selected_negatives, hard_items], dim=1)\n positive_vectors, negative_vectors = candidate_vectors(\n model,\n positive_items[begin:end],\n selected_negatives,\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[:, :negatives.shape[1]], negative_weights[begin:end], config\n )\n if config.hard_negatives and config.hard_objective == \"bce\":\n hard_loss = F.softplus(negative_logits[:, negatives.shape[1]:]).mean(dim=1)\n negative_loss = ((1.0 - config.hard_negative_weight) * negative_loss\n + config.hard_negative_weight * hard_loss)\n selected_weights = torch.from_numpy(\n sample_weights[begin:end].astype(np.float32, copy=False)\n ).to(device)\n per_target_loss = (positive_loss + negative_loss) / 2.0\n if config.hard_negatives and config.hard_objective == \"pairwise\":\n hard_loss = F.softplus(negative_logits[:, negatives.shape[1]:]\n - positive_logits[:, None]).mean(dim=1)\n per_target_loss = per_target_loss + config.hard_negative_weight * hard_loss\n loss_sum = (selected_weights * per_target_loss).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 sampled_logits = negative_logits[:, :negatives.shape[1]].detach()\n statistics[\"negative_sum\"] += float(sampled_logits.sum().cpu())\n statistics[\"pair_correct\"] += float(\n (positive_logits.detach()[:, None] > sampled_logits).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" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "session_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "root_turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "response_id": "resp_03ec3525bea3ac86016ab3678d512c87d287a7ffdf5d75b73e", "usage": { "input_tokens": 81436, "cached_input_tokens": 78592, "cache_write_input_tokens": 0, "output_tokens": 149, "reasoning_output_tokens": 102, "total_tokens": 81585 }, "turn_token_usage": { "input_tokens": 4461067, "cached_input_tokens": 4256128, "cache_write_input_tokens": 0, "output_tokens": 25765, "reasoning_output_tokens": 14656, "total_tokens": 4486832 }, "thread_token_usage": { "input_tokens": 4461067, "cached_input_tokens": 4256128, "cache_write_input_tokens": 0, "output_tokens": 25765, "reasoning_output_tokens": 14656, "total_tokens": 4486832 } } ## Step 65 · agent · 2026-09-23T05:46:09.155Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T05:46:42.942Z { "code": "text(await tools.apply_patch(\"*** Begin Patch\\n*** Update File: /app/solution/train_current_model.py\\n@@\\n hard_include_inbatch: bool = False\\n+ training_objective: str = \\\"bce\\\"\\n+ shared_pool_size: int = 4096\\n@@\\n-def score_target_microbatches(\\n+def shared_softmax_loss(\\n+ model, queries, positives, liked_items, user_rows, known_positive_packed, config,\\n+):\\n+ \\\"\\\"\\\"Uniform sampled softmax with a shared, filtered catalog denominator.\\n+\\n+ Every sampled negative participates. Log-sum-exp concentrates gradients on\\n+ confusable tracks without a biased top-k negative proposal. N/K corrects\\n+ the sampled denominator to the catalog sum. Item encoding and matrix\\n+ multiplication are shared across queries, avoiding B x K x D tensors.\\n+ \\\"\\\"\\\"\\n+ generator = torch.Generator(device=\\\"cpu\\\").manual_seed(\\n+ config.seed + int(positives.sum()) + int(user_rows[0]))\\n+ pool = torch.randperm(model.n_items, generator=generator)[:config.shared_pool_size]\\n+ unique, inverse = torch.unique(torch.cat([positives.cpu(), pool]), return_inverse=True)\\n+ encoded = model.encode_items(unique.to(queries.device))\\n+ pos_vectors = encoded[inverse[:len(positives)].to(queries.device)]\\n+ pool_vectors = encoded[inverse[len(positives):].to(queries.device)]\\n+ positive_logits = (queries * pos_vectors).sum(dim=1)\\n+ raw_logits = queries @ pool_vectors.T\\n+ columns = pool.numpy()\\n+ invalid = ((known_positive_packed[user_rows[:, None], columns[None, :] >> 3]\\n+ >> (columns[None, :] & 7)) & 1).astype(bool)\\n+ # Pool order is random; build an inverse catalog lookup for explicit likes.\\n+ lookup = np.full(model.n_items + 1, -1, dtype=np.int64)\\n+ lookup[columns] = np.arange(len(columns))\\n+ excluded = np.concatenate([positives.cpu().numpy()[:, None], liked_items], axis=1)\\n+ mapped = lookup[excluded]\\n+ present = mapped >= 0\\n+ rows = np.broadcast_to(np.arange(len(queries))[:, None], mapped.shape)\\n+ invalid[rows[present], mapped[present]] = True\\n+ negative_logits = raw_logits.masked_fill(torch.from_numpy(invalid).to(queries.device), -torch.inf)\\n+ log_negative_sum = torch.logsumexp(negative_logits, dim=1) + np.log(model.n_items / len(pool))\\n+ loss = torch.logaddexp(positive_logits, log_negative_sum) - positive_logits\\n+ # Random-pool diagnostics only; masked entries never enter the loss.\\n+ return loss, positive_logits, raw_logits[:, :32]\\n+\\n+\\n+def score_target_microbatches(\\n@@\\n (\\\"hard_objective\\\", \\\"bce\\\"),\\n- (\\\"hard_include_inbatch\\\", True)))\\n+ (\\\"hard_include_inbatch\\\", True),\\n+ (\\\"training_objective\\\", \\\"bce\\\"),\\n+ (\\\"shared_pool_size\\\", 4096)))\\n@@\\n parser.add_argument(\\\"--hard-include-inbatch\\\", action=\\\"store_true\\\", default=Config.hard_include_inbatch)\\n+ parser.add_argument(\\\"--training-objective\\\", choices=(\\\"bce\\\", \\\"shared-softmax\\\"), default=Config.training_objective)\\n+ parser.add_argument(\\\"--shared-pool-size\\\", type=int, default=Config.shared_pool_size)\\n@@\\n hard_include_inbatch=args.hard_include_inbatch,\\n+ training_objective=args.training_objective,\\n+ shared_pool_size=args.shared_pool_size,\\n*** End Patch\"));\ntext(await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\nfrom pathlib import Path\\np=Path('/app/solution/train_current_model.py')\\ns=p.read_text()\\nstart=s.index(' selected_negatives = negatives[begin:end]')\\nend=s.index(' loss_sum = (selected_weights * per_target_loss).sum()',start)\\nold=s[start:end]\\nweights=''' selected_weights = torch.from_numpy(\\n sample_weights[begin:end].astype(np.float32, copy=False)\\n ).to(device)\\n'''\\nassert weights in old\\nold=old.replace(weights,'')\\nnew=''' if config.training_objective == \\\"shared-softmax\\\":\\n per_target_loss, positive_logits, negative_logits = shared_softmax_loss(\\n model, query_vectors, positive_items[begin:end],\\n prepared.liked_states[selected_states], user_rows[begin:end],\\n known_positive_packed, config,\\n )\\n else:\\n'''+''.join(' '+line if line.strip() else line for line in old.splitlines(keepends=True))+weights\\ns=s[:start]+new+s[end:]\\np.write_text(s)\\nPY\",max_output_tokens:1000}));\n" } ### Tool output · 2026-09-23T05:46:43.164Z Script completed Wall time 0.2 seconds Output: {} {"chunk_id":"96ce2b","wall_time_seconds":0.00000182,"exit_code":0,"original_token_count":0,"output":""} ### Harness context { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-c01cc012-f371-48b8-b6be-6516347b7465", "changes": { "/app/solution/train_current_model.py": { "type": "update", "unified_diff": "@@ -66,2 +66,4 @@\n hard_include_inbatch: bool = False\n+ training_objective: str = \"bce\"\n+ shared_pool_size: int = 4096\n negative_filter_scope: str = \"full-training-history\"\n@@ -1166,2 +1168,39 @@\n \n+def shared_softmax_loss(\n+ model, queries, positives, liked_items, user_rows, known_positive_packed, config,\n+):\n+ \"\"\"Uniform sampled softmax with a shared, filtered catalog denominator.\n+\n+ Every sampled negative participates. Log-sum-exp concentrates gradients on\n+ confusable tracks without a biased top-k negative proposal. N/K corrects\n+ the sampled denominator to the catalog sum. Item encoding and matrix\n+ multiplication are shared across queries, avoiding B x K x D tensors.\n+ \"\"\"\n+ generator = torch.Generator(device=\"cpu\").manual_seed(\n+ config.seed + int(positives.sum()) + int(user_rows[0]))\n+ pool = torch.randperm(model.n_items, generator=generator)[:config.shared_pool_size]\n+ unique, inverse = torch.unique(torch.cat([positives.cpu(), pool]), return_inverse=True)\n+ encoded = model.encode_items(unique.to(queries.device))\n+ pos_vectors = encoded[inverse[:len(positives)].to(queries.device)]\n+ pool_vectors = encoded[inverse[len(positives):].to(queries.device)]\n+ positive_logits = (queries * pos_vectors).sum(dim=1)\n+ raw_logits = queries @ pool_vectors.T\n+ columns = pool.numpy()\n+ invalid = ((known_positive_packed[user_rows[:, None], columns[None, :] >> 3]\n+ >> (columns[None, :] & 7)) & 1).astype(bool)\n+ # Pool order is random; build an inverse catalog lookup for explicit likes.\n+ lookup = np.full(model.n_items + 1, -1, dtype=np.int64)\n+ lookup[columns] = np.arange(len(columns))\n+ excluded = np.concatenate([positives.cpu().numpy()[:, None], liked_items], axis=1)\n+ mapped = lookup[excluded]\n+ present = mapped >= 0\n+ rows = np.broadcast_to(np.arange(len(queries))[:, None], mapped.shape)\n+ invalid[rows[present], mapped[present]] = True\n+ negative_logits = raw_logits.masked_fill(torch.from_numpy(invalid).to(queries.device), -torch.inf)\n+ log_negative_sum = torch.logsumexp(negative_logits, dim=1) + np.log(model.n_items / len(pool))\n+ loss = torch.logaddexp(positive_logits, log_negative_sum) - positive_logits\n+ # Random-pool diagnostics only; masked entries never enter the loss.\n+ return loss, positive_logits, raw_logits[:, :32]\n+\n+\n def score_target_microbatches(\n@@ -1715,3 +1754,5 @@\n (\"hard_objective\", \"bce\"),\n- (\"hard_include_inbatch\", True)))\n+ (\"hard_include_inbatch\", True),\n+ (\"training_objective\", \"bce\"),\n+ (\"shared_pool_size\", 4096)))\n )\n@@ -2033,2 +2074,4 @@\n parser.add_argument(\"--hard-include-inbatch\", action=\"store_true\", default=Config.hard_include_inbatch)\n+ parser.add_argument(\"--training-objective\", choices=(\"bce\", \"shared-softmax\"), default=Config.training_objective)\n+ parser.add_argument(\"--shared-pool-size\", type=int, default=Config.shared_pool_size)\n parser.add_argument(\n@@ -2143,2 +2186,4 @@\n hard_include_inbatch=args.hard_include_inbatch,\n+ training_objective=args.training_objective,\n+ shared_pool_size=args.shared_pool_size,\n loss_microbatch_size=args.loss_microbatch_size,\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": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "session_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "root_turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "response_id": "resp_03ec3525bea3ac86016ab36793887c87d2acff90e95634e28f", "usage": { "input_tokens": 82730, "cached_input_tokens": 81280, "cache_write_input_tokens": 0, "output_tokens": 1410, "reasoning_output_tokens": 260, "total_tokens": 84140 }, "turn_token_usage": { "input_tokens": 4543797, "cached_input_tokens": 4337408, "cache_write_input_tokens": 0, "output_tokens": 27175, "reasoning_output_tokens": 14916, "total_tokens": 4570972 }, "thread_token_usage": { "input_tokens": 4543797, "cached_input_tokens": 4337408, "cache_write_input_tokens": 0, "output_tokens": 27175, "reasoning_output_tokens": 14916, "total_tokens": 4570972 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-161a1488-5770-4eb5-a8b7-b85c88a5e30d", "process_id": "25909", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\np=Path('/app/solution/train_current_model.py')\ns=p.read_text()\nstart=s.index(' selected_negatives = negatives[begin:end]')\nend=s.index(' loss_sum = (selected_weights * per_target_loss).sum()',start)\nold=s[start:end]\nweights=''' selected_weights = torch.from_numpy(\n sample_weights[begin:end].astype(np.float32, copy=False)\n ).to(device)\n'''\nassert weights in old\nold=old.replace(weights,'')\nnew=''' if config.training_objective == \"shared-softmax\":\n per_target_loss, positive_logits, negative_logits = shared_softmax_loss(\n model, query_vectors, positive_items[begin:end],\n prepared.liked_states[selected_states], user_rows[begin:end],\n known_positive_packed, config,\n )\n else:\n'''+''.join(' '+line if line.strip() else line for line in old.splitlines(keepends=True))+weights\ns=s[:start]+new+s[end:]\np.write_text(s)\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\np=Path('/app/solution/train_current_model.py')\ns=p.read_text()\nstart=s.index(' selected_negatives = negatives[begin:end]')\nend=s.index(' loss_sum = (selected_weights * per_target_loss).sum()',start)\nold=s[start:end]\nweights=''' selected_weights = torch.from_numpy(\n sample_weights[begin:end].astype(np.float32, copy=False)\n ).to(device)\n'''\nassert weights in old\nold=old.replace(weights,'')\nnew=''' if config.training_objective == \"shared-softmax\":\n per_target_loss, positive_logits, negative_logits = shared_softmax_loss(\n model, query_vectors, positive_items[begin:end],\n prepared.liked_states[selected_states], user_rows[begin:end],\n known_positive_packed, config,\n )\n else:\n'''+''.join(' '+line if line.strip() else line for line in old.splitlines(keepends=True))+weights\ns=s[:start]+new+s[end:]\np.write_text(s)\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 0, "nanos": 1820 }, "formatted_output": "" } } ## Step 66 · agent · 2026-09-23T05:46:51.700Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T05:47:06.168Z { "code": "text(await tools.apply_patch(\"*** Begin Patch\\n*** Add File: /app/solution/test_shared_softmax.py\\n+\\\"\\\"\\\"Check the sampled denominator against exact masked catalog softmax.\\\"\\\"\\\"\\n+import numpy as np\\n+import torch\\n+import train_current_model as t\\n+\\n+torch.set_num_threads(1)\\n+t.seed_everything(11)\\n+config = t.Config(training_objective=\\\"shared-softmax\\\", shared_pool_size=100)\\n+model = t.base.SequentialTwoTower(np.zeros((100, 4), dtype=np.float32), t.model_config(config)).eval()\\n+queries = torch.randn(2, 192, requires_grad=True)\\n+positives = torch.tensor([1, 2])\\n+likes = np.array([[7, 100], [8, 100]])\\n+known = np.zeros((2, 100), dtype=np.uint8)\\n+known[0, :20] = 1\\n+known[1, 20:40] = 1\\n+packed = np.packbits(known, axis=1, bitorder=\\\"little\\\")\\n+loss, _, _ = t.shared_softmax_loss(model, queries, positives, likes, np.arange(2), packed, config)\\n+scores = queries @ model.encode_items(torch.arange(100)).T\\n+mask = known.astype(bool)\\n+mask[0, 7] = mask[1, 8] = True\\n+mask[np.arange(2), positives.numpy()] = False\\n+expected = torch.nn.functional.cross_entropy(scores.masked_fill(torch.from_numpy(mask), -torch.inf), positives, reduction=\\\"none\\\")\\n+assert torch.allclose(loss, expected, atol=1e-5), (loss, expected)\\n+loss.mean().backward()\\n+assert torch.isfinite(queries.grad).all() and queries.grad.abs().sum() > 0\\n+assert model.candidate_embedding.weight.grad.abs().sum() > 0\\n+print(\\\"shared-softmax matches exact filtered catalog CE and propagates finite tower gradients\\\")\\n*** End Patch\"));\ntext(await tools.exec_command({cmd:\"/opt/task-venv/bin/python /app/solution/test_shared_softmax.py && tail -n 4 /app/solution/control.log\",yield_time_ms:1000,max_output_tokens:1000}));\ntext(await tools.clock__curr_time({}));\n" } ### Tool output · 2026-09-23T05:47:07.348Z Script completed Wall time 1.2 seconds Output: {} {"chunk_id":"44130b","wall_time_seconds":1.001118511,"session_id":65058,"original_token_count":0,"output":""} {"current_time":"2026-09-23 05:47:07 UTC"} ### Harness context { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-f862af4d-5d9e-432e-a93b-cc7f13cdcf19", "changes": { "/app/solution/test_shared_softmax.py": { "type": "add", "content": "\"\"\"Check the sampled denominator against exact masked catalog softmax.\"\"\"\nimport numpy as np\nimport torch\nimport train_current_model as t\n\ntorch.set_num_threads(1)\nt.seed_everything(11)\nconfig = t.Config(training_objective=\"shared-softmax\", shared_pool_size=100)\nmodel = t.base.SequentialTwoTower(np.zeros((100, 4), dtype=np.float32), t.model_config(config)).eval()\nqueries = torch.randn(2, 192, requires_grad=True)\npositives = torch.tensor([1, 2])\nlikes = np.array([[7, 100], [8, 100]])\nknown = np.zeros((2, 100), dtype=np.uint8)\nknown[0, :20] = 1\nknown[1, 20:40] = 1\npacked = np.packbits(known, axis=1, bitorder=\"little\")\nloss, _, _ = t.shared_softmax_loss(model, queries, positives, likes, np.arange(2), packed, config)\nscores = queries @ model.encode_items(torch.arange(100)).T\nmask = known.astype(bool)\nmask[0, 7] = mask[1, 8] = True\nmask[np.arange(2), positives.numpy()] = False\nexpected = torch.nn.functional.cross_entropy(scores.masked_fill(torch.from_numpy(mask), -torch.inf), positives, reduction=\"none\")\nassert torch.allclose(loss, expected, atol=1e-5), (loss, expected)\nloss.mean().backward()\nassert torch.isfinite(queries.grad).all() and queries.grad.abs().sum() > 0\nassert model.candidate_embedding.weight.grad.abs().sum() > 0\nprint(\"shared-softmax matches exact filtered catalog CE and propagates finite tower gradients\")\n" } }, "status": "completed", "stdout": "Success. Updated the following files:\nA /app/solution/test_shared_softmax.py\n", "stderr": "" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "session_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "root_turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "response_id": "resp_03ec3525bea3ac86016ab367c36b2c87d29eb33b599212094e", "usage": { "input_tokens": 84198, "cached_input_tokens": 82560, "cache_write_input_tokens": 0, "output_tokens": 697, "reasoning_output_tokens": 181, "total_tokens": 84895 }, "turn_token_usage": { "input_tokens": 4627995, "cached_input_tokens": 4419968, "cache_write_input_tokens": 0, "output_tokens": 27872, "reasoning_output_tokens": 15097, "total_tokens": 4655867 }, "thread_token_usage": { "input_tokens": 4627995, "cached_input_tokens": 4419968, "cache_write_input_tokens": 0, "output_tokens": 27872, "reasoning_output_tokens": 15097, "total_tokens": 4655867 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-783ea4df-03df-46fc-adcd-fd8fffe8d9f7", "process_id": "65058", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python /app/solution/test_shared_softmax.py && tail -n 4 /app/solution/control.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python /app/solution/test_shared_softmax.py && tail -n 4 /app/solution/control.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "shared-softmax matches exact filtered catalog CE and propagates finite tower gradients\nresumed validation-BCE training: epoch=1 loss=0.496636\nepoch=2/2 batches=50 targets=206,457 batch_bce=0.461053 running_bce=0.470153 elapsed=50.3s\nepoch=2/2 batches=100 targets=402,892 batch_bce=0.473713 running_bce=0.468821 elapsed=98.6s\nepoch=2/2 batches=150 targets=596,745 batch_bce=0.428681 running_bce=0.462790 elapsed=147.1s\n", "stderr": "", "aggregated_output": "shared-softmax matches exact filtered catalog CE and propagates finite tower gradients\nresumed validation-BCE training: epoch=1 loss=0.496636\nepoch=2/2 batches=50 targets=206,457 batch_bce=0.461053 running_bce=0.470153 elapsed=50.3s\nepoch=2/2 batches=100 targets=402,892 batch_bce=0.473713 running_bce=0.468821 elapsed=98.6s\nepoch=2/2 batches=150 targets=596,745 batch_bce=0.428681 running_bce=0.462790 elapsed=147.1s\n", "exit_code": 0, "duration": { "secs": 5, "nanos": 121191028 }, "formatted_output": "shared-softmax matches exact filtered catalog CE and propagates finite tower gradients\nresumed validation-BCE training: epoch=1 loss=0.496636\nepoch=2/2 batches=50 targets=206,457 batch_bce=0.461053 running_bce=0.470153 elapsed=50.3s\nepoch=2/2 batches=100 targets=402,892 batch_bce=0.473713 running_bce=0.468821 elapsed=98.6s\nepoch=2/2 batches=150 targets=596,745 batch_bce=0.428681 running_bce=0.462790 elapsed=147.1s\n" } } ## Step 67 · agent · 2026-09-23T05:47:30.532Z ### Thinking unavailable — no text recorded The shared-pool loss passed a useful correctness check: when the pool contains the full catalog, it matches exact cross-entropy with known positives and active likes filtered out. Gradients reach both towers. It scores the shared pool with matrix multiplication, avoiding a large per-user tensor of negative item vectors. ### exec · 2026-09-23T05:47:32.686Z { "code": "text(await tools.write_stdin({session_id:65058,chars:\"\",yield_time_ms:1000,max_output_tokens:1000}));\ntext(await tools.exec_command({cmd:\"tail -n 4 /app/solution/control.log\",max_output_tokens:1000}));\n" } ### Tool output · 2026-09-23T05:47:32.756Z Script completed Wall time 0.1 seconds Output: {"chunk_id":"77be96","wall_time_seconds":0.000002411,"exit_code":0,"original_token_count":105,"output":"shared-softmax matches exact filtered catalog CE and propagates finite tower gradients\nresumed validation-BCE training: epoch=1 loss=0.496636\nepoch=2/2 batches=50 targets=206,457 batch_bce=0.461053 running_bce=0.470153 elapsed=50.3s\nepoch=2/2 batches=100 targets=402,892 batch_bce=0.473713 running_bce=0.468821 elapsed=98.6s\nepoch=2/2 batches=150 targets=596,745 batch_bce=0.428681 running_bce=0.462790 elapsed=147.1s\n"} {"chunk_id":"6d186e","wall_time_seconds":0.0000019,"exit_code":0,"original_token_count":83,"output":"resumed validation-BCE training: epoch=1 loss=0.496636\nepoch=2/2 batches=50 targets=206,457 batch_bce=0.461053 running_bce=0.470153 elapsed=50.3s\nepoch=2/2 batches=100 targets=402,892 batch_bce=0.473713 running_bce=0.468821 elapsed=98.6s\nepoch=2/2 batches=150 targets=596,745 batch_bce=0.428681 running_bce=0.462790 elapsed=147.1s\n"} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "session_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "root_turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "response_id": "resp_03ec3525bea3ac86016ab367db8da087d2b97dbb083c0651e5", "usage": { "input_tokens": 84971, "cached_input_tokens": 83968, "cache_write_input_tokens": 0, "output_tokens": 650, "reasoning_output_tokens": 515, "total_tokens": 85621 }, "turn_token_usage": { "input_tokens": 4712966, "cached_input_tokens": 4503936, "cache_write_input_tokens": 0, "output_tokens": 28522, "reasoning_output_tokens": 15612, "total_tokens": 4741488 }, "thread_token_usage": { "input_tokens": 4712966, "cached_input_tokens": 4503936, "cache_write_input_tokens": 0, "output_tokens": 28522, "reasoning_output_tokens": 15612, "total_tokens": 4741488 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-61323a94-8052-45ac-b75d-36f6329b2544", "process_id": "40667", "command": [ "/bin/sh", "-lc", "tail -n 4 /app/solution/control.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "read", "cmd": "tail -n 4 /app/solution/control.log", "name": "control.log", "path": "/app/solution/control.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "resumed validation-BCE training: epoch=1 loss=0.496636\nepoch=2/2 batches=50 targets=206,457 batch_bce=0.461053 running_bce=0.470153 elapsed=50.3s\nepoch=2/2 batches=100 targets=402,892 batch_bce=0.473713 running_bce=0.468821 elapsed=98.6s\nepoch=2/2 batches=150 targets=596,745 batch_bce=0.428681 running_bce=0.462790 elapsed=147.1s\n", "stderr": "", "aggregated_output": "resumed validation-BCE training: epoch=1 loss=0.496636\nepoch=2/2 batches=50 targets=206,457 batch_bce=0.461053 running_bce=0.470153 elapsed=50.3s\nepoch=2/2 batches=100 targets=402,892 batch_bce=0.473713 running_bce=0.468821 elapsed=98.6s\nepoch=2/2 batches=150 targets=596,745 batch_bce=0.428681 running_bce=0.462790 elapsed=147.1s\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 1900 }, "formatted_output": "resumed validation-BCE training: epoch=1 loss=0.496636\nepoch=2/2 batches=50 targets=206,457 batch_bce=0.461053 running_bce=0.470153 elapsed=50.3s\nepoch=2/2 batches=100 targets=402,892 batch_bce=0.473713 running_bce=0.468821 elapsed=98.6s\nepoch=2/2 batches=150 targets=596,745 batch_bce=0.428681 running_bce=0.462790 elapsed=147.1s\n" } } ## Step 68 · agent · 2026-09-23T05:47:54.215Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T05:48:03.466Z { "code": "text(await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\nfrom pathlib import Path\\np=Path('/app/solution/train_current_model.py')\\ns=p.read_text()\\nstart=0\\nfor _ in range(2):\\n start=s.index(' negatives, negative_weights = draw_training_negatives(',start)\\n end=s.index('\\\\n )',start)+len('\\\\n )')\\n block=s[start:end]\\n new=''' if config.training_objective == \\\"shared-softmax\\\":\\n # This objective draws one shared pool inside each loss slice.\\n negatives = torch.empty((len(positive_items), 32), dtype=torch.long, device=device)\\n negative_weights = torch.empty_like(negatives, dtype=torch.float32)\\n else:\\n'''+''.join(' '+line if line.strip() else line for line in block.splitlines(keepends=True))\\n s=s[:start]+new+s[end:]\\n start+=len(new)\\np.write_text(s)\\nPY\",max_output_tokens:1000}));\ntext(await tools.apply_patch(\"*** Begin Patch\\n*** Update File: /app/solution/train_current_model.py\\n@@\\n if args.hard_negatives and args.negative_filter_scope != \\\"full-training-history\\\":\\n parser.error(\\\"hard mining requires full-training-history filtering\\\")\\n+ if args.training_objective == \\\"shared-softmax\\\" and args.negative_filter_scope != \\\"full-training-history\\\":\\n+ parser.error(\\\"shared softmax requires full-training-history filtering\\\")\\n+ if args.shared_pool_size < 32:\\n+ parser.error(\\\"shared-pool-size must be at least 32\\\")\\n*** End Patch\"));\ntext(await tools.exec_command({cmd:\"tail -n 4 /app/solution/control.log\",max_output_tokens:1000}));\n" } ### Tool output · 2026-09-23T05:48:03.693Z Script completed Wall time 0.2 seconds Output: {"chunk_id":"11756a","wall_time_seconds":0.00000601,"exit_code":0,"original_token_count":0,"output":""} {} {"chunk_id":"b2b411","wall_time_seconds":0.00000501,"exit_code":0,"original_token_count":93,"output":"epoch=2/2 batches=50 targets=206,457 batch_bce=0.461053 running_bce=0.470153 elapsed=50.3s\nepoch=2/2 batches=100 targets=402,892 batch_bce=0.473713 running_bce=0.468821 elapsed=98.6s\nepoch=2/2 batches=150 targets=596,745 batch_bce=0.428681 running_bce=0.462790 elapsed=147.1s\nepoch=2/2 batches=200 targets=790,005 batch_bce=0.448111 running_bce=0.459127 elapsed=199.0s\n"} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "session_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "root_turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "response_id": "resp_03ec3525bea3ac86016ab367f65a2087d2b751991f39e46915", "usage": { "input_tokens": 85980, "cached_input_tokens": 84736, "cache_write_input_tokens": 0, "output_tokens": 925, "reasoning_output_tokens": 516, "total_tokens": 86905 }, "turn_token_usage": { "input_tokens": 4798946, "cached_input_tokens": 4588672, "cache_write_input_tokens": 0, "output_tokens": 29447, "reasoning_output_tokens": 16128, "total_tokens": 4828393 }, "thread_token_usage": { "input_tokens": 4798946, "cached_input_tokens": 4588672, "cache_write_input_tokens": 0, "output_tokens": 29447, "reasoning_output_tokens": 16128, "total_tokens": 4828393 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-93bbf7c5-d86b-46e5-ae7c-9388fd3bf6b5", "process_id": "21490", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\np=Path('/app/solution/train_current_model.py')\ns=p.read_text()\nstart=0\nfor _ in range(2):\n start=s.index(' negatives, negative_weights = draw_training_negatives(',start)\n end=s.index('\\n )',start)+len('\\n )')\n block=s[start:end]\n new=''' if config.training_objective == \"shared-softmax\":\n # This objective draws one shared pool inside each loss slice.\n negatives = torch.empty((len(positive_items), 32), dtype=torch.long, device=device)\n negative_weights = torch.empty_like(negatives, dtype=torch.float32)\n else:\n'''+''.join(' '+line if line.strip() else line for line in block.splitlines(keepends=True))\n s=s[:start]+new+s[end:]\n start+=len(new)\np.write_text(s)\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\np=Path('/app/solution/train_current_model.py')\ns=p.read_text()\nstart=0\nfor _ in range(2):\n start=s.index(' negatives, negative_weights = draw_training_negatives(',start)\n end=s.index('\\n )',start)+len('\\n )')\n block=s[start:end]\n new=''' if config.training_objective == \"shared-softmax\":\n # This objective draws one shared pool inside each loss slice.\n negatives = torch.empty((len(positive_items), 32), dtype=torch.long, device=device)\n negative_weights = torch.empty_like(negatives, dtype=torch.float32)\n else:\n'''+''.join(' '+line if line.strip() else line for line in block.splitlines(keepends=True))\n s=s[:start]+new+s[end:]\n start+=len(new)\np.write_text(s)\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 0, "nanos": 6010 }, "formatted_output": "" } } ### Harness context { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-c229dc75-40c6-4803-83ad-7ff5fcc06e56", "changes": { "/app/solution/train_current_model.py": { "type": "update", "unified_diff": "@@ -2189,2 +2189,6 @@\n parser.error(\"hard mining requires full-training-history filtering\")\n+ if args.training_objective == \"shared-softmax\" and args.negative_filter_scope != \"full-training-history\":\n+ parser.error(\"shared softmax requires full-training-history filtering\")\n+ if args.shared_pool_size < 32:\n+ parser.error(\"shared-pool-size must be at least 32\")\n public = args.public_dir.resolve()\n", "move_path": null } }, "status": "completed", "stdout": "Success. Updated the following files:\nM /app/solution/train_current_model.py\n", "stderr": "" } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-3636eba7-e581-4495-bf0f-89f8607929db", "process_id": "16088", "command": [ "/bin/sh", "-lc", "tail -n 4 /app/solution/control.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "read", "cmd": "tail -n 4 /app/solution/control.log", "name": "control.log", "path": "/app/solution/control.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "epoch=2/2 batches=50 targets=206,457 batch_bce=0.461053 running_bce=0.470153 elapsed=50.3s\nepoch=2/2 batches=100 targets=402,892 batch_bce=0.473713 running_bce=0.468821 elapsed=98.6s\nepoch=2/2 batches=150 targets=596,745 batch_bce=0.428681 running_bce=0.462790 elapsed=147.1s\nepoch=2/2 batches=200 targets=790,005 batch_bce=0.448111 running_bce=0.459127 elapsed=199.0s\n", "stderr": "", "aggregated_output": "epoch=2/2 batches=50 targets=206,457 batch_bce=0.461053 running_bce=0.470153 elapsed=50.3s\nepoch=2/2 batches=100 targets=402,892 batch_bce=0.473713 running_bce=0.468821 elapsed=98.6s\nepoch=2/2 batches=150 targets=596,745 batch_bce=0.428681 running_bce=0.462790 elapsed=147.1s\nepoch=2/2 batches=200 targets=790,005 batch_bce=0.448111 running_bce=0.459127 elapsed=199.0s\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 5010 }, "formatted_output": "epoch=2/2 batches=50 targets=206,457 batch_bce=0.461053 running_bce=0.470153 elapsed=50.3s\nepoch=2/2 batches=100 targets=402,892 batch_bce=0.473713 running_bce=0.468821 elapsed=98.6s\nepoch=2/2 batches=150 targets=596,745 batch_bce=0.428681 running_bce=0.462790 elapsed=147.1s\nepoch=2/2 batches=200 targets=790,005 batch_bce=0.448111 running_bce=0.459127 elapsed=199.0s\n" } } ## Step 69 · agent · 2026-09-23T05:48:07.632Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T05:48:09.871Z { "code": "text(await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\nimport ast\\nfrom pathlib import Path\\nast.parse(Path('/app/solution/train_current_model.py').read_text())\\nprint('trainer syntax valid')\\nPY\",max_output_tokens:1000}));\ntext(await tools.clock__curr_time({}));\n" } ### Tool output · 2026-09-23T05:48:10.125Z Script completed Wall time 0.2 seconds Output: {"chunk_id":"0ae9a3","wall_time_seconds":0.024878062,"exit_code":0,"original_token_count":6,"output":"trainer syntax valid\n"} {"current_time":"2026-09-23 05:48:10 UTC"} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "session_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "root_turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "response_id": "resp_03ec3525bea3ac86016ab36813e3f487d2baf98259990ae1e9", "usage": { "input_tokens": 87142, "cached_input_tokens": 85760, "cache_write_input_tokens": 0, "output_tokens": 108, "reasoning_output_tokens": 23, "total_tokens": 87250 }, "turn_token_usage": { "input_tokens": 4886088, "cached_input_tokens": 4674432, "cache_write_input_tokens": 0, "output_tokens": 29555, "reasoning_output_tokens": 16151, "total_tokens": 4915643 }, "thread_token_usage": { "input_tokens": 4886088, "cached_input_tokens": 4674432, "cache_write_input_tokens": 0, "output_tokens": 29555, "reasoning_output_tokens": 16151, "total_tokens": 4915643 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-c6847859-b45e-4c6e-8c86-e6e7bcb6ad62", "process_id": "35415", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\nimport ast\nfrom pathlib import Path\nast.parse(Path('/app/solution/train_current_model.py').read_text())\nprint('trainer syntax valid')\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport ast\nfrom pathlib import Path\nast.parse(Path('/app/solution/train_current_model.py').read_text())\nprint('trainer syntax valid')\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "trainer syntax valid\n", "stderr": "", "aggregated_output": "trainer syntax valid\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 24739788 }, "formatted_output": "trainer syntax valid\n" } } ## Step 70 · agent · 2026-09-23T05:48:33.650Z ### Thinking unavailable — no text recorded ## Step 71 · agent · 2026-09-23T05:48:43.679Z ### Thinking unavailable — no text recorded ### sleep · 2026-09-23T05:48:43.736Z { "duration_ms": 35000 } ### Tool output · 2026-09-23T05:49:18.770Z Wall time: 35.0300 seconds Sleep completed. ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "session_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "root_turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "response_id": "resp_03ec3525bea3ac86016ab3681a552c87d29d470b238a4358da", "usage": { "input_tokens": 87329, "cached_input_tokens": 86912, "cache_write_input_tokens": 0, "output_tokens": 797, "reasoning_output_tokens": 776, "total_tokens": 88126 }, "turn_token_usage": { "input_tokens": 4973417, "cached_input_tokens": 4761344, "cache_write_input_tokens": 0, "output_tokens": 30352, "reasoning_output_tokens": 16927, "total_tokens": 5003769 }, "thread_token_usage": { "input_tokens": 4973417, "cached_input_tokens": 4761344, "cache_write_input_tokens": 0, "output_tokens": 30352, "reasoning_output_tokens": 16927, "total_tokens": 5003769 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "Extension", "kind": "clock.sleep", "id": "call_q2qLQyAFMneu2Id6yV0KOq7L", "durationMs": 35000 } } ### exec · 2026-09-23T05:49:23.984Z { "code": "text(await tools.exec_command({cmd:\"tail -n 6 /app/solution/control.log\",max_output_tokens:1000}));\ntext(await tools.clock__curr_time({}));\n" } ### Tool output · 2026-09-23T05:49:24.101Z Script completed Wall time 0.1 seconds Output: {"chunk_id":"c6204b","wall_time_seconds":0.00000412,"exit_code":0,"original_token_count":110,"output":"epoch=2/2 batches=250 targets=987,417 batch_bce=0.452899 running_bce=0.453204 elapsed=248.6s\nsaved epoch checkpoint: /app/solution/control_ckpt/epoch-02.pt\nepoch=2/2 train_loss=0.45228 validation_loss=0.42855 pair_accuracy=0.826 train_targets=1,052,953 validation_targets=130,103 train_seconds=265.1 validation_seconds=15.9\nrestored best validation-BCE checkpoint: epoch=2 loss=0.428545\nencoded users=1,600/8,456\nencoded users=3,200/8,456\n"} {"current_time":"2026-09-23 05:49:24 UTC"} ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-e61f8d47-6673-43e0-aa0d-db9909334426", "process_id": "76117", "command": [ "/bin/sh", "-lc", "tail -n 6 /app/solution/control.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "read", "cmd": "tail -n 6 /app/solution/control.log", "name": "control.log", "path": "/app/solution/control.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "epoch=2/2 batches=250 targets=987,417 batch_bce=0.452899 running_bce=0.453204 elapsed=248.6s\nsaved epoch checkpoint: /app/solution/control_ckpt/epoch-02.pt\nepoch=2/2 train_loss=0.45228 validation_loss=0.42855 pair_accuracy=0.826 train_targets=1,052,953 validation_targets=130,103 train_seconds=265.1 validation_seconds=15.9\nrestored best validation-BCE checkpoint: epoch=2 loss=0.428545\nencoded users=1,600/8,456\nencoded users=3,200/8,456\n", "stderr": "", "aggregated_output": "epoch=2/2 batches=250 targets=987,417 batch_bce=0.452899 running_bce=0.453204 elapsed=248.6s\nsaved epoch checkpoint: /app/solution/control_ckpt/epoch-02.pt\nepoch=2/2 train_loss=0.45228 validation_loss=0.42855 pair_accuracy=0.826 train_targets=1,052,953 validation_targets=130,103 train_seconds=265.1 validation_seconds=15.9\nrestored best validation-BCE checkpoint: epoch=2 loss=0.428545\nencoded users=1,600/8,456\nencoded users=3,200/8,456\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4120 }, "formatted_output": "epoch=2/2 batches=250 targets=987,417 batch_bce=0.452899 running_bce=0.453204 elapsed=248.6s\nsaved epoch checkpoint: /app/solution/control_ckpt/epoch-02.pt\nepoch=2/2 train_loss=0.45228 validation_loss=0.42855 pair_accuracy=0.826 train_targets=1,052,953 validation_targets=130,103 train_seconds=265.1 validation_seconds=15.9\nrestored best validation-BCE checkpoint: epoch=2 loss=0.428545\nencoded users=1,600/8,456\nencoded users=3,200/8,456\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "session_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "root_turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "response_id": "resp_03ec3525bea3ac86016ab3685efaec87d28d080a97e4e8addb", "usage": { "input_tokens": 88149, "cached_input_tokens": 87168, "cache_write_input_tokens": 0, "output_tokens": 46, "reasoning_output_tokens": 0, "total_tokens": 88195 }, "turn_token_usage": { "input_tokens": 5061566, "cached_input_tokens": 4848512, "cache_write_input_tokens": 0, "output_tokens": 30398, "reasoning_output_tokens": 16927, "total_tokens": 5091964 }, "thread_token_usage": { "input_tokens": 5061566, "cached_input_tokens": 4848512, "cache_write_input_tokens": 0, "output_tokens": 30398, "reasoning_output_tokens": 16927, "total_tokens": 5091964 } } ## Step 72 · agent · 2026-09-23T05:49:27.130Z ### Thinking unavailable — no text recorded The matched BCE control has finished training and is generating its ranking. The shared-softmax experiment is ready next. It will use a 4,096-item pool, apply the existing positive filters, and scale the sampled negative sum by `catalog_size / pool_size` to approximate the full-catalog denominator. ### sleep · 2026-09-23T05:49:29.679Z { "duration_ms": 30000 } ### Tool output · 2026-09-23T05:49:59.728Z Wall time: 30.0319 seconds Sleep completed. ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "session_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "root_turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "response_id": "resp_03ec3525bea3ac86016ab3686459dc87d284b2254b3335afc2", "usage": { "input_tokens": 88415, "cached_input_tokens": 87936, "cache_write_input_tokens": 0, "output_tokens": 125, "reasoning_output_tokens": 36, "total_tokens": 88540 }, "turn_token_usage": { "input_tokens": 5149981, "cached_input_tokens": 4936448, "cache_write_input_tokens": 0, "output_tokens": 30523, "reasoning_output_tokens": 16963, "total_tokens": 5180504 }, "thread_token_usage": { "input_tokens": 5149981, "cached_input_tokens": 4936448, "cache_write_input_tokens": 0, "output_tokens": 30523, "reasoning_output_tokens": 16963, "total_tokens": 5180504 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "Extension", "kind": "clock.sleep", "id": "call_d6xnQH4ZCiB5uNHjaLnjkufZ", "durationMs": 30000 } } ### exec · 2026-09-23T05:50:03.416Z { "code": "text(await tools.exec_command({cmd:\"tail -n 5 /app/solution/control.log && ls -lh /app/solution/control_validation.parquet\",max_output_tokens:1000}));\n" } ### Tool output · 2026-09-23T05:50:03.532Z Script completed Wall time 0.1 seconds Output: {"chunk_id":"0bb67f","wall_time_seconds":0.00000416,"exit_code":2,"original_token_count":64,"output":"restored best validation-BCE checkpoint: epoch=2 loss=0.428545\nencoded users=1,600/8,456\nencoded users=3,200/8,456\nencoded users=4,800/8,456\nencoded users=6,400/8,456\nls: cannot access '/app/solution/control_validation.parquet': No such file or directory\n"} ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-ebd866b1-625e-4782-8e2f-1d25d1906b59", "process_id": "87343", "command": [ "/bin/sh", "-lc", "tail -n 5 /app/solution/control.log && ls -lh /app/solution/control_validation.parquet" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "read", "cmd": "tail -n 5 /app/solution/control.log", "name": "control.log", "path": "/app/solution/control.log" }, { "type": "list_files", "cmd": "ls -lh /app/solution/control_validation.parquet", "path": "control_validation.parquet" } ], "source": "unified_exec_startup", "status": "failed", "stdout": "restored best validation-BCE checkpoint: epoch=2 loss=0.428545\nencoded users=1,600/8,456\nencoded users=3,200/8,456\nencoded users=4,800/8,456\nencoded users=6,400/8,456\nls: cannot access '/app/solution/control_validation.parquet': No such file or directory\n", "stderr": "", "aggregated_output": "restored best validation-BCE checkpoint: epoch=2 loss=0.428545\nencoded users=1,600/8,456\nencoded users=3,200/8,456\nencoded users=4,800/8,456\nencoded users=6,400/8,456\nls: cannot access '/app/solution/control_validation.parquet': No such file or directory\n", "exit_code": 2, "duration": { "secs": 0, "nanos": 4160 }, "formatted_output": "restored best validation-BCE checkpoint: epoch=2 loss=0.428545\nencoded users=1,600/8,456\nencoded users=3,200/8,456\nencoded users=4,800/8,456\nencoded users=6,400/8,456\nls: cannot access '/app/solution/control_validation.parquet': No such file or directory\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "session_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "root_turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "response_id": "resp_03ec3525bea3ac86016ab36887f3c487d296bcdbbd1245221b", "usage": { "input_tokens": 88563, "cached_input_tokens": 88192, "cache_write_input_tokens": 0, "output_tokens": 49, "reasoning_output_tokens": 0, "total_tokens": 88612 }, "turn_token_usage": { "input_tokens": 5238544, "cached_input_tokens": 5024640, "cache_write_input_tokens": 0, "output_tokens": 30572, "reasoning_output_tokens": 16963, "total_tokens": 5269116 }, "thread_token_usage": { "input_tokens": 5238544, "cached_input_tokens": 5024640, "cache_write_input_tokens": 0, "output_tokens": 30572, "reasoning_output_tokens": 16963, "total_tokens": 5269116 } } ## Step 73 · agent · 2026-09-23T05:50:20.400Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T05:50:21.053Z { "code": "text(await tools.exec_command({cmd:\"tail -n 3 /app/solution/control.log\",max_output_tokens:1000}));\ntext(await tools.clock__curr_time({}));\n" } ### Tool output · 2026-09-23T05:50:21.168Z Script completed Wall time 0.1 seconds Output: {"chunk_id":"40d2e7","wall_time_seconds":0.00000444,"exit_code":0,"original_token_count":20,"output":"encoded users=4,800/8,456\nencoded users=6,400/8,456\nencoded users=8,000/8,456\n"} {"current_time":"2026-09-23 05:50:21 UTC"} ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-e0effa1d-1c72-4e1d-8511-dc8f13b170f5", "process_id": "97815", "command": [ "/bin/sh", "-lc", "tail -n 3 /app/solution/control.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "read", "cmd": "tail -n 3 /app/solution/control.log", "name": "control.log", "path": "/app/solution/control.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "encoded users=4,800/8,456\nencoded users=6,400/8,456\nencoded users=8,000/8,456\n", "stderr": "", "aggregated_output": "encoded users=4,800/8,456\nencoded users=6,400/8,456\nencoded users=8,000/8,456\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4440 }, "formatted_output": "encoded users=4,800/8,456\nencoded users=6,400/8,456\nencoded users=8,000/8,456\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "session_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "root_turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "response_id": "resp_03ec3525bea3ac86016ab3688bbbe487d29f1ca4943324d7a0", "usage": { "input_tokens": 88749, "cached_input_tokens": 88448, "cache_write_input_tokens": 0, "output_tokens": 368, "reasoning_output_tokens": 320, "total_tokens": 89117 }, "turn_token_usage": { "input_tokens": 5327293, "cached_input_tokens": 5113088, "cache_write_input_tokens": 0, "output_tokens": 30940, "reasoning_output_tokens": 17283, "total_tokens": 5358233 }, "thread_token_usage": { "input_tokens": 5327293, "cached_input_tokens": 5113088, "cache_write_input_tokens": 0, "output_tokens": 30940, "reasoning_output_tokens": 17283, "total_tokens": 5358233 } } ## Step 74 · agent · 2026-09-23T05:50:25.450Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T05:50:26.415Z { "code": "text(await tools.write_stdin({session_id:93457,chars:\"\",yield_time_ms:1000,max_output_tokens:1000}));\n" } ### Tool output · 2026-09-23T05:50:31.432Z Script completed Wall time 5.0 seconds Output: {"chunk_id":"910ce0","wall_time_seconds":5.001607833,"session_id":93457,"original_token_count":0,"output":""} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "session_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "root_turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "response_id": "resp_03ec3525bea3ac86016ab3689d5c9887d28626f2499a0e3f88", "usage": { "input_tokens": 89225, "cached_input_tokens": 88576, "cache_write_input_tokens": 0, "output_tokens": 101, "reasoning_output_tokens": 60, "total_tokens": 89326 }, "turn_token_usage": { "input_tokens": 5416518, "cached_input_tokens": 5201664, "cache_write_input_tokens": 0, "output_tokens": 31041, "reasoning_output_tokens": 17343, "total_tokens": 5447559 }, "thread_token_usage": { "input_tokens": 5416518, "cached_input_tokens": 5201664, "cache_write_input_tokens": 0, "output_tokens": 31041, "reasoning_output_tokens": 17343, "total_tokens": 5447559 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-7a802408-537e-4a4b-8628-86ac0ecfab0b", "process_id": "93457", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python /app/solution/train_current_model.py --stage validation --public-dir /task --output /app/solution/control_validation.parquet --prepared-cache /app/solution/validation_cache.npz --checkpoint-dir /app/solution/control_ckpt --resume-checkpoint /app/solution/production_ckpt/best.pt --epochs 2 --hard-negatives 0 --hard-negative-weight 0.1 --threads 8 --loss-microbatch-size 4096 > /app/solution/control.json 2> /app/solution/control.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 --output /app/solution/control_validation.parquet --prepared-cache /app/solution/validation_cache.npz --checkpoint-dir /app/solution/control_ckpt --resume-checkpoint /app/solution/production_ckpt/best.pt --epochs 2 --hard-negatives 0 --hard-negative-weight 0.1 --threads 8 --loss-microbatch-size 4096 > /app/solution/control.json 2> /app/solution/control.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 388, "nanos": 659689900 }, "formatted_output": "" } } ## Step 75 · agent · 2026-09-23T05:50:46.760Z ### Thinking unavailable — no text recorded ### sleep · 2026-09-23T05:50:46.936Z { "duration_ms": 20000 } ### Tool output · 2026-09-23T05:51:06.946Z Wall time: 20.0054 seconds Sleep completed. ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "session_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "root_turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "response_id": "resp_03ec3525bea3ac86016ab368a79e2887d29c50c6eb8cbc7c57", "usage": { "input_tokens": 89383, "cached_input_tokens": 89088, "cache_write_input_tokens": 0, "output_tokens": 350, "reasoning_output_tokens": 329, "total_tokens": 89733 }, "turn_token_usage": { "input_tokens": 5505901, "cached_input_tokens": 5290752, "cache_write_input_tokens": 0, "output_tokens": 31391, "reasoning_output_tokens": 17672, "total_tokens": 5537292 }, "thread_token_usage": { "input_tokens": 5505901, "cached_input_tokens": 5290752, "cache_write_input_tokens": 0, "output_tokens": 31391, "reasoning_output_tokens": 17672, "total_tokens": 5537292 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "Extension", "kind": "clock.sleep", "id": "call_AOmwh4ITnpOOoMzawn2PQn4b", "durationMs": 20000 } } ### exec · 2026-09-23T05:51:12.492Z { "code": "text(await tools.exec_command({cmd:\"tail -n 3 /app/solution/control.log && ls -lh /app/solution/control_validation.parquet\",max_output_tokens:1000}));\n" } ### Tool output · 2026-09-23T05:51:12.762Z Script completed Wall time 0.0 seconds Output: {"chunk_id":"f15874","wall_time_seconds":0.00000749,"exit_code":0,"original_token_count":40,"output":"ranked users=7,168/8,456\nranked users=8,192/8,456\nranked users=8,456/8,456\n-rw-r--r-- 1 agent agent 1.4M Sep 23 05:50 /app/solution/control_validation.parquet\n"} ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-ebf4b759-2b8e-45d9-ab2c-b6dc2da09ab8", "process_id": "23888", "command": [ "/bin/sh", "-lc", "tail -n 3 /app/solution/control.log && ls -lh /app/solution/control_validation.parquet" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "read", "cmd": "tail -n 3 /app/solution/control.log", "name": "control.log", "path": "/app/solution/control.log" }, { "type": "list_files", "cmd": "ls -lh /app/solution/control_validation.parquet", "path": "control_validation.parquet" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "ranked users=7,168/8,456\nranked users=8,192/8,456\nranked users=8,456/8,456\n-rw-r--r-- 1 agent agent 1.4M Sep 23 05:50 /app/solution/control_validation.parquet\n", "stderr": "", "aggregated_output": "ranked users=7,168/8,456\nranked users=8,192/8,456\nranked users=8,456/8,456\n-rw-r--r-- 1 agent agent 1.4M Sep 23 05:50 /app/solution/control_validation.parquet\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 7490 }, "formatted_output": "ranked users=7,168/8,456\nranked users=8,192/8,456\nranked users=8,456/8,456\n-rw-r--r-- 1 agent agent 1.4M Sep 23 05:50 /app/solution/control_validation.parquet\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "session_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "root_turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "response_id": "resp_03ec3525bea3ac86016ab368cb1d9c87d29024c23cd44b6543", "usage": { "input_tokens": 89756, "cached_input_tokens": 89216, "cache_write_input_tokens": 0, "output_tokens": 49, "reasoning_output_tokens": 0, "total_tokens": 89805 }, "turn_token_usage": { "input_tokens": 5595657, "cached_input_tokens": 5379968, "cache_write_input_tokens": 0, "output_tokens": 31440, "reasoning_output_tokens": 17672, "total_tokens": 5627097 }, "thread_token_usage": { "input_tokens": 5595657, "cached_input_tokens": 5379968, "cache_write_input_tokens": 0, "output_tokens": 31440, "reasoning_output_tokens": 17672, "total_tokens": 5627097 } } ### exec · 2026-09-23T05:51:25.581Z { "code": "text(await tools.exec_command({cmd:\"/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/control_validation.parquet > /app/solution/control_score.json && cat /app/solution/control_score.json\",yield_time_ms:1000,max_output_tokens:1000}));\ntext(await tools.exec_command({cmd:\"/opt/task-venv/bin/python /app/solution/train_current_model.py --stage validation --public-dir /task --output /app/solution/softmax_validation.parquet --prepared-cache /app/solution/validation_cache.npz --checkpoint-dir /app/solution/softmax_ckpt --resume-checkpoint /app/solution/production_ckpt/best.pt --epochs 2 --hard-negatives 0 --training-objective shared-softmax --shared-pool-size 4096 --learning-rate 0.0005 --threads 8 --loss-microbatch-size 4096 > /app/solution/softmax.json 2> /app/solution/softmax.log\",yield_time_ms:1000}));\ntext(await tools.write_stdin({session_id:93457,chars:\"\",yield_time_ms:1000,max_output_tokens:1000}));\n" } ### Tool output · 2026-09-23T05:51:27.909Z Script completed Wall time 2.3 seconds Output: {"chunk_id":"186e71","wall_time_seconds":1.000950277,"session_id":66948,"original_token_count":0,"output":""} {"chunk_id":"b2395c","wall_time_seconds":1.001718148,"session_id":83768,"original_token_count":0,"output":""} {"chunk_id":"c7d587","wall_time_seconds":0.00000163,"exit_code":0,"original_token_count":0,"output":""} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "session_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "root_turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "response_id": "resp_03ec3525bea3ac86016ab368d12a5c87d29ee3327ba730c7ed", "usage": { "input_tokens": 89927, "cached_input_tokens": 89600, "cache_write_input_tokens": 0, "output_tokens": 257, "reasoning_output_tokens": 0, "total_tokens": 90184 }, "turn_token_usage": { "input_tokens": 5685584, "cached_input_tokens": 5469568, "cache_write_input_tokens": 0, "output_tokens": 31697, "reasoning_output_tokens": 17672, "total_tokens": 5717281 }, "thread_token_usage": { "input_tokens": 5685584, "cached_input_tokens": 5469568, "cache_write_input_tokens": 0, "output_tokens": 31697, "reasoning_output_tokens": 17672, "total_tokens": 5717281 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-14054914-e8e5-4f4b-9ed3-6991aaf9f73c", "process_id": "66948", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/control_validation.parquet > /app/solution/control_score.json && cat /app/solution/control_score.json" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/control_validation.parquet > /app/solution/control_score.json && cat /app/solution/control_score.json" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{\n \"model\": \"/app/solution/control_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.019878,\n \"quality_users\": 4263\n }\n}\n", "stderr": "", "aggregated_output": "{\n \"model\": \"/app/solution/control_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.019878,\n \"quality_users\": 4263\n }\n}\n", "exit_code": 0, "duration": { "secs": 3, "nanos": 230088882 }, "formatted_output": "{\n \"model\": \"/app/solution/control_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.019878,\n \"quality_users\": 4263\n }\n}\n" } } ## Step 76 · agent · 2026-09-23T05:51:44.094Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T05:51:45.114Z { "code": "text(await tools.write_stdin({session_id:66948,chars:\"\",yield_time_ms:1000,max_output_tokens:1000}));\ntext(await tools.exec_command({cmd:\"tail -n 5 /app/solution/softmax.log\",max_output_tokens:1000}));\ntext(await tools.clock__curr_time({}));\n" } ### Tool output · 2026-09-23T05:51:45.230Z Script completed Wall time 0.1 seconds Output: {"chunk_id":"dc6bfa","wall_time_seconds":0.00000579,"exit_code":0,"original_token_count":34,"output":"{\n \"model\": \"/app/solution/control_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.019878,\n \"quality_users\": 4263\n }\n}\n"} {"chunk_id":"620b01","wall_time_seconds":0.000004,"exit_code":0,"original_token_count":131,"output":"inbatch 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/production_ckpt/best.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); recomputing the resume checkpoint's validation BCE\n"} {"current_time":"2026-09-23 05:51:45 UTC"} ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-9052a5fd-32ed-45e4-9ab6-ef30c58d6543", "process_id": "2237", "command": [ "/bin/sh", "-lc", "tail -n 5 /app/solution/softmax.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "read", "cmd": "tail -n 5 /app/solution/softmax.log", "name": "softmax.log", "path": "/app/solution/softmax.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "inbatch 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/production_ckpt/best.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); recomputing the resume checkpoint's validation BCE\n", "stderr": "", "aggregated_output": "inbatch 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/production_ckpt/best.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); recomputing the resume checkpoint's validation BCE\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4000 }, "formatted_output": "inbatch 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/production_ckpt/best.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); recomputing the resume checkpoint's validation BCE\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "session_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "root_turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "response_id": "resp_03ec3525bea3ac86016ab368e014ec87d2b7cc1847c5a2aa59", "usage": { "input_tokens": 90310, "cached_input_tokens": 89728, "cache_write_input_tokens": 0, "output_tokens": 441, "reasoning_output_tokens": 362, "total_tokens": 90751 }, "turn_token_usage": { "input_tokens": 5775894, "cached_input_tokens": 5559296, "cache_write_input_tokens": 0, "output_tokens": 32138, "reasoning_output_tokens": 18034, "total_tokens": 5808032 }, "thread_token_usage": { "input_tokens": 5775894, "cached_input_tokens": 5559296, "cache_write_input_tokens": 0, "output_tokens": 32138, "reasoning_output_tokens": 18034, "total_tokens": 5808032 } } ## Step 77 · agent · 2026-09-23T05:52:07.769Z ### Thinking unavailable — no text recorded ## Step 78 · agent · 2026-09-23T05:52:25.981Z ### Thinking unavailable — no text recorded ## Step 79 · agent · 2026-09-23T05:52:47.663Z ### Thinking unavailable — no text recorded ## Step 80 · agent · 2026-09-23T05:53:03.423Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T05:53:04.189Z { "code": "text(await tools.exec_command({cmd:\"tail -n 5 /app/solution/softmax.log && cat /sys/fs/cgroup/memory.current\",max_output_tokens:1500}));\ntext(await tools.clock__curr_time({}));\n" } ### Tool output · 2026-09-23T05:53:04.365Z Script completed Wall time 0.1 seconds Output: {"chunk_id":"03c2e7","wall_time_seconds":0.0000028,"exit_code":0,"original_token_count":139,"output":"using a fresh AdamW optimizer after loading /app/solution/production_ckpt/best.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); recomputing the resume checkpoint's validation BCE\nresumed validation-BCE training: epoch=1 loss=9.101446\nepoch=2/2 batches=50 targets=206,457 batch_bce=8.858153 running_bce=8.932010 elapsed=27.6s\nepoch=2/2 batches=100 targets=402,892 batch_bce=8.806376 running_bce=8.943662 elapsed=54.0s\n5679079424\n"} {"current_time":"2026-09-23 05:53:04 UTC"} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "session_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "root_turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "response_id": "resp_03ec3525bea3ac86016ab368f1690487d289c68b3fab060714", "usage": { "input_tokens": 91059, "cached_input_tokens": 90112, "cache_write_input_tokens": 0, "output_tokens": 2048, "reasoning_output_tokens": 1988, "total_tokens": 93107 }, "turn_token_usage": { "input_tokens": 5866953, "cached_input_tokens": 5649408, "cache_write_input_tokens": 0, "output_tokens": 34186, "reasoning_output_tokens": 20022, "total_tokens": 5901139 }, "thread_token_usage": { "input_tokens": 5866953, "cached_input_tokens": 5649408, "cache_write_input_tokens": 0, "output_tokens": 34186, "reasoning_output_tokens": 20022, "total_tokens": 5901139 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-4d120004-d5fb-46b8-b6c9-dc58f9617ca3", "process_id": "39915", "command": [ "/bin/sh", "-lc", "tail -n 5 /app/solution/softmax.log && cat /sys/fs/cgroup/memory.current" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "read", "cmd": "tail -n 5 /app/solution/softmax.log", "name": "softmax.log", "path": "/app/solution/softmax.log" }, { "type": "read", "cmd": "cat /sys/fs/cgroup/memory.current", "name": "memory.current", "path": "/sys/fs/cgroup/memory.current" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "using a fresh AdamW optimizer after loading /app/solution/production_ckpt/best.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); recomputing the resume checkpoint's validation BCE\nresumed validation-BCE training: epoch=1 loss=9.101446\nepoch=2/2 batches=50 targets=206,457 batch_bce=8.858153 running_bce=8.932010 elapsed=27.6s\nepoch=2/2 batches=100 targets=402,892 batch_bce=8.806376 running_bce=8.943662 elapsed=54.0s\n5679079424\n", "stderr": "", "aggregated_output": "using a fresh AdamW optimizer after loading /app/solution/production_ckpt/best.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); recomputing the resume checkpoint's validation BCE\nresumed validation-BCE training: epoch=1 loss=9.101446\nepoch=2/2 batches=50 targets=206,457 batch_bce=8.858153 running_bce=8.932010 elapsed=27.6s\nepoch=2/2 batches=100 targets=402,892 batch_bce=8.806376 running_bce=8.943662 elapsed=54.0s\n5679079424\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 2800 }, "formatted_output": "using a fresh AdamW optimizer after loading /app/solution/production_ckpt/best.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); recomputing the resume checkpoint's validation BCE\nresumed validation-BCE training: epoch=1 loss=9.101446\nepoch=2/2 batches=50 targets=206,457 batch_bce=8.858153 running_bce=8.932010 elapsed=27.6s\nepoch=2/2 batches=100 targets=402,892 batch_bce=8.806376 running_bce=8.943662 elapsed=54.0s\n5679079424\n" } } ## Step 81 · agent · 2026-09-23T05:53:22.872Z ### Thinking unavailable — no text recorded ## Step 82 · agent · 2026-09-23T05:53:34.947Z ### Thinking unavailable — no text recorded The extra BCE pass scored **0.019878**, so more training alone did not improve retrieval. The shared-softmax run is training faster—about 54 seconds for its first 100 batches—because it reuses item vectors across queries while comparing each target with a much larger negative pool. ### sleep · 2026-09-23T05:53:36.887Z { "duration_ms": 35000 } ### Tool output · 2026-09-23T05:54:11.943Z Wall time: 35.0207 seconds Sleep completed. ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "session_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "root_turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "response_id": "resp_03ec3525bea3ac86016ab369409c4c87d2a8a437c771eae563", "usage": { "input_tokens": 93356, "cached_input_tokens": 90880, "cache_write_input_tokens": 0, "output_tokens": 972, "reasoning_output_tokens": 885, "total_tokens": 94328 }, "turn_token_usage": { "input_tokens": 5960309, "cached_input_tokens": 5740288, "cache_write_input_tokens": 0, "output_tokens": 35158, "reasoning_output_tokens": 20907, "total_tokens": 5995467 }, "thread_token_usage": { "input_tokens": 5960309, "cached_input_tokens": 5740288, "cache_write_input_tokens": 0, "output_tokens": 35158, "reasoning_output_tokens": 20907, "total_tokens": 5995467 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "Extension", "kind": "clock.sleep", "id": "call_gnp6E5uX3qon7GgEIHn65QmN", "durationMs": 35000 } } ### exec · 2026-09-23T05:54:17.387Z { "code": "text(await tools.exec_command({cmd:\"tail -n 4 /app/solution/softmax.log\",max_output_tokens:1000}));\ntext(await tools.clock__curr_time({}));\n" } ### Tool output · 2026-09-23T05:54:17.503Z Script completed Wall time 0.1 seconds Output: {"chunk_id":"ec5dbc","wall_time_seconds":0.000005,"exit_code":0,"original_token_count":93,"output":"epoch=2/2 batches=100 targets=402,892 batch_bce=8.806376 running_bce=8.943662 elapsed=54.0s\nepoch=2/2 batches=150 targets=596,745 batch_bce=8.839116 running_bce=8.920331 elapsed=80.1s\nepoch=2/2 batches=200 targets=790,005 batch_bce=8.807660 running_bce=8.913663 elapsed=105.5s\nepoch=2/2 batches=250 targets=987,417 batch_bce=9.131135 running_bce=8.891028 elapsed=131.7s\n"} {"current_time":"2026-09-23 05:54:17 UTC"} ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-4508bc7d-4015-4f56-8a37-1ae9dc0e1c98", "process_id": "84778", "command": [ "/bin/sh", "-lc", "tail -n 4 /app/solution/softmax.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "read", "cmd": "tail -n 4 /app/solution/softmax.log", "name": "softmax.log", "path": "/app/solution/softmax.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "epoch=2/2 batches=100 targets=402,892 batch_bce=8.806376 running_bce=8.943662 elapsed=54.0s\nepoch=2/2 batches=150 targets=596,745 batch_bce=8.839116 running_bce=8.920331 elapsed=80.1s\nepoch=2/2 batches=200 targets=790,005 batch_bce=8.807660 running_bce=8.913663 elapsed=105.5s\nepoch=2/2 batches=250 targets=987,417 batch_bce=9.131135 running_bce=8.891028 elapsed=131.7s\n", "stderr": "", "aggregated_output": "epoch=2/2 batches=100 targets=402,892 batch_bce=8.806376 running_bce=8.943662 elapsed=54.0s\nepoch=2/2 batches=150 targets=596,745 batch_bce=8.839116 running_bce=8.920331 elapsed=80.1s\nepoch=2/2 batches=200 targets=790,005 batch_bce=8.807660 running_bce=8.913663 elapsed=105.5s\nepoch=2/2 batches=250 targets=987,417 batch_bce=9.131135 running_bce=8.891028 elapsed=131.7s\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 5000 }, "formatted_output": "epoch=2/2 batches=100 targets=402,892 batch_bce=8.806376 running_bce=8.943662 elapsed=54.0s\nepoch=2/2 batches=150 targets=596,745 batch_bce=8.839116 running_bce=8.920331 elapsed=80.1s\nepoch=2/2 batches=200 targets=790,005 batch_bce=8.807660 running_bce=8.913663 elapsed=105.5s\nepoch=2/2 batches=250 targets=987,417 batch_bce=9.131135 running_bce=8.891028 elapsed=131.7s\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "session_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "root_turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "response_id": "resp_03ec3525bea3ac86016ab3698424b487d291399020646d6c40", "usage": { "input_tokens": 94351, "cached_input_tokens": 93184, "cache_write_input_tokens": 0, "output_tokens": 48, "reasoning_output_tokens": 0, "total_tokens": 94399 }, "turn_token_usage": { "input_tokens": 6054660, "cached_input_tokens": 5833472, "cache_write_input_tokens": 0, "output_tokens": 35206, "reasoning_output_tokens": 20907, "total_tokens": 6089866 }, "thread_token_usage": { "input_tokens": 6054660, "cached_input_tokens": 5833472, "cache_write_input_tokens": 0, "output_tokens": 35206, "reasoning_output_tokens": 20907, "total_tokens": 6089866 } } ## Step 83 · agent · 2026-09-23T05:54:40.923Z ### Thinking unavailable — no text recorded ## Step 84 · agent · 2026-09-23T05:54:48.900Z ### Thinking unavailable — no text recorded ### sleep · 2026-09-23T05:54:48.986Z { "duration_ms": 35000 } ### Tool output · 2026-09-23T05:55:24.029Z Wall time: 35.0264 seconds Sleep completed. ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "session_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "root_turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "response_id": "resp_03ec3525bea3ac86016ab36989b38887d2a4b30fcfbbfd2aba", "usage": { "input_tokens": 94618, "cached_input_tokens": 94208, "cache_write_input_tokens": 0, "output_tokens": 756, "reasoning_output_tokens": 735, "total_tokens": 95374 }, "turn_token_usage": { "input_tokens": 6149278, "cached_input_tokens": 5927680, "cache_write_input_tokens": 0, "output_tokens": 35962, "reasoning_output_tokens": 21642, "total_tokens": 6185240 }, "thread_token_usage": { "input_tokens": 6149278, "cached_input_tokens": 5927680, "cache_write_input_tokens": 0, "output_tokens": 35962, "reasoning_output_tokens": 21642, "total_tokens": 6185240 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "Extension", "kind": "clock.sleep", "id": "call_zpV37Z79T83D35utrivmZbaV", "durationMs": 35000 } } ### exec · 2026-09-23T05:55:27.286Z { "code": "text(await tools.exec_command({cmd:\"tail -n 7 /app/solution/softmax.log\",max_output_tokens:1000}));\ntext(await tools.clock__curr_time({}));\n" } ### Tool output · 2026-09-23T05:55:27.398Z Script completed Wall time 0.1 seconds Output: {"chunk_id":"e27eb1","wall_time_seconds":0.00000478,"exit_code":0,"original_token_count":91,"output":"epoch=2/2 train_loss=8.89195 validation_loss=8.80889 pair_accuracy=0.848 train_targets=1,052,953 validation_targets=130,103 train_seconds=140.7 validation_seconds=12.5\nrestored best validation-BCE checkpoint: epoch=2 loss=8.808887\nencoded users=1,600/8,456\nencoded users=3,200/8,456\nencoded users=4,800/8,456\nencoded users=6,400/8,456\nencoded users=8,000/8,456\n"} {"current_time":"2026-09-23 05:55:27 UTC"} ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-99495e78-c3e6-4f9b-98fb-3b975808fb8d", "process_id": "57128", "command": [ "/bin/sh", "-lc", "tail -n 7 /app/solution/softmax.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "read", "cmd": "tail -n 7 /app/solution/softmax.log", "name": "softmax.log", "path": "/app/solution/softmax.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "epoch=2/2 train_loss=8.89195 validation_loss=8.80889 pair_accuracy=0.848 train_targets=1,052,953 validation_targets=130,103 train_seconds=140.7 validation_seconds=12.5\nrestored best validation-BCE checkpoint: epoch=2 loss=8.808887\nencoded users=1,600/8,456\nencoded users=3,200/8,456\nencoded users=4,800/8,456\nencoded users=6,400/8,456\nencoded users=8,000/8,456\n", "stderr": "", "aggregated_output": "epoch=2/2 train_loss=8.89195 validation_loss=8.80889 pair_accuracy=0.848 train_targets=1,052,953 validation_targets=130,103 train_seconds=140.7 validation_seconds=12.5\nrestored best validation-BCE checkpoint: epoch=2 loss=8.808887\nencoded users=1,600/8,456\nencoded users=3,200/8,456\nencoded users=4,800/8,456\nencoded users=6,400/8,456\nencoded users=8,000/8,456\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4780 }, "formatted_output": "epoch=2/2 train_loss=8.89195 validation_loss=8.80889 pair_accuracy=0.848 train_targets=1,052,953 validation_targets=130,103 train_seconds=140.7 validation_seconds=12.5\nrestored best validation-BCE checkpoint: epoch=2 loss=8.808887\nencoded users=1,600/8,456\nencoded users=3,200/8,456\nencoded users=4,800/8,456\nencoded users=6,400/8,456\nencoded users=8,000/8,456\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "session_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "root_turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "response_id": "resp_03ec3525bea3ac86016ab369cc481487d2b36b272502dd1513", "usage": { "input_tokens": 95397, "cached_input_tokens": 94464, "cache_write_input_tokens": 0, "output_tokens": 48, "reasoning_output_tokens": 0, "total_tokens": 95445 }, "turn_token_usage": { "input_tokens": 6244675, "cached_input_tokens": 6022144, "cache_write_input_tokens": 0, "output_tokens": 36010, "reasoning_output_tokens": 21642, "total_tokens": 6280685 }, "thread_token_usage": { "input_tokens": 6244675, "cached_input_tokens": 6022144, "cache_write_input_tokens": 0, "output_tokens": 36010, "reasoning_output_tokens": 21642, "total_tokens": 6280685 } } ## Step 85 · agent · 2026-09-23T05:55:33.296Z ### Thinking unavailable — no text recorded The shared-softmax pass completed in **141 seconds**, compared with roughly 265 seconds for the baseline pass. Its held-out objective improved, and it is now generating the public ranking. The speedup leaves enough time for a final refit once the public result is checked. ### sleep · 2026-09-23T05:55:35.207Z { "duration_ms": 20000 } ### Tool output · 2026-09-23T05:55:55.227Z Wall time: 20.0041 seconds Sleep completed. ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "session_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "root_turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "response_id": "resp_03ec3525bea3ac86016ab369cf972487d2981d7afd14d062f6", "usage": { "input_tokens": 95644, "cached_input_tokens": 95232, "cache_write_input_tokens": 0, "output_tokens": 172, "reasoning_output_tokens": 89, "total_tokens": 95816 }, "turn_token_usage": { "input_tokens": 6340319, "cached_input_tokens": 6117376, "cache_write_input_tokens": 0, "output_tokens": 36182, "reasoning_output_tokens": 21731, "total_tokens": 6376501 }, "thread_token_usage": { "input_tokens": 6340319, "cached_input_tokens": 6117376, "cache_write_input_tokens": 0, "output_tokens": 36182, "reasoning_output_tokens": 21731, "total_tokens": 6376501 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-b6f37b28-89b3-466c-a454-aedb4b3ea4b7", "process_id": "83768", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python /app/solution/train_current_model.py --stage validation --public-dir /task --output /app/solution/softmax_validation.parquet --prepared-cache /app/solution/validation_cache.npz --checkpoint-dir /app/solution/softmax_ckpt --resume-checkpoint /app/solution/production_ckpt/best.pt --epochs 2 --hard-negatives 0 --training-objective shared-softmax --shared-pool-size 4096 --learning-rate 0.0005 --threads 8 --loss-microbatch-size 4096 > /app/solution/softmax.json 2> /app/solution/softmax.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 --output /app/solution/softmax_validation.parquet --prepared-cache /app/solution/validation_cache.npz --checkpoint-dir /app/solution/softmax_ckpt --resume-checkpoint /app/solution/production_ckpt/best.pt --epochs 2 --hard-negatives 0 --training-objective shared-softmax --shared-pool-size 4096 --learning-rate 0.0005 --threads 8 --loss-microbatch-size 4096 > /app/solution/softmax.json 2> /app/solution/softmax.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 256, "nanos": 354767663 }, "formatted_output": "" } } ### Harness context { "type": "codex_item_completed", "item": { "type": "Extension", "kind": "clock.sleep", "id": "call_IcXgOt0x47dOaoItciK7c200", "durationMs": 20000 } } ### exec · 2026-09-23T05:56:02.080Z { "code": "text(await tools.exec_command({cmd:\"tail -n 3 /app/solution/softmax.log && ls -lh /app/solution/softmax_validation.parquet\",max_output_tokens:1000}));\n" } ### Tool output · 2026-09-23T05:56:02.186Z Script completed Wall time 0.0 seconds Output: {"chunk_id":"adcb51","wall_time_seconds":0.00000649,"exit_code":0,"original_token_count":40,"output":"ranked users=7,168/8,456\nranked users=8,192/8,456\nranked users=8,456/8,456\n-rw-r--r-- 1 agent agent 1.3M Sep 23 05:55 /app/solution/softmax_validation.parquet\n"} ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-10d7fc3e-5b99-4495-bf1d-58f72f93d5c3", "process_id": "26688", "command": [ "/bin/sh", "-lc", "tail -n 3 /app/solution/softmax.log && ls -lh /app/solution/softmax_validation.parquet" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "read", "cmd": "tail -n 3 /app/solution/softmax.log", "name": "softmax.log", "path": "/app/solution/softmax.log" }, { "type": "list_files", "cmd": "ls -lh /app/solution/softmax_validation.parquet", "path": "softmax_validation.parquet" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "ranked users=7,168/8,456\nranked users=8,192/8,456\nranked users=8,456/8,456\n-rw-r--r-- 1 agent agent 1.3M Sep 23 05:55 /app/solution/softmax_validation.parquet\n", "stderr": "", "aggregated_output": "ranked users=7,168/8,456\nranked users=8,192/8,456\nranked users=8,456/8,456\n-rw-r--r-- 1 agent agent 1.3M Sep 23 05:55 /app/solution/softmax_validation.parquet\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 6490 }, "formatted_output": "ranked users=7,168/8,456\nranked users=8,192/8,456\nranked users=8,456/8,456\n-rw-r--r-- 1 agent agent 1.3M Sep 23 05:55 /app/solution/softmax_validation.parquet\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "session_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "root_turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "response_id": "resp_03ec3525bea3ac86016ab369eb63a887d2a78ba45e5c8e3315", "usage": { "input_tokens": 95839, "cached_input_tokens": 95488, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 0, "total_tokens": 95892 }, "turn_token_usage": { "input_tokens": 6436158, "cached_input_tokens": 6212864, "cache_write_input_tokens": 0, "output_tokens": 36235, "reasoning_output_tokens": 21731, "total_tokens": 6472393 }, "thread_token_usage": { "input_tokens": 6436158, "cached_input_tokens": 6212864, "cache_write_input_tokens": 0, "output_tokens": 36235, "reasoning_output_tokens": 21731, "total_tokens": 6472393 } } ### exec · 2026-09-23T05:56:07.279Z { "code": "text(await tools.exec_command({cmd:\"/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/softmax_validation.parquet > /app/solution/softmax_score.json && cat /app/solution/softmax_score.json\",yield_time_ms:1000,max_output_tokens:1000}));\ntext(await tools.write_stdin({session_id:83768,chars:\"\",yield_time_ms:1000,max_output_tokens:1000}));\n" } ### Tool output · 2026-09-23T05:56:08.447Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"fd175d","wall_time_seconds":1.001060399,"session_id":80547,"original_token_count":0,"output":""} {"chunk_id":"345921","wall_time_seconds":0.00000151,"exit_code":0,"original_token_count":0,"output":""} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "session_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "root_turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "response_id": "resp_03ec3525bea3ac86016ab369f274a887d29156e5e4918221a7", "usage": { "input_tokens": 96016, "cached_input_tokens": 95616, "cache_write_input_tokens": 0, "output_tokens": 106, "reasoning_output_tokens": 0, "total_tokens": 96122 }, "turn_token_usage": { "input_tokens": 6532174, "cached_input_tokens": 6308480, "cache_write_input_tokens": 0, "output_tokens": 36341, "reasoning_output_tokens": 21731, "total_tokens": 6568515 }, "thread_token_usage": { "input_tokens": 6532174, "cached_input_tokens": 6308480, "cache_write_input_tokens": 0, "output_tokens": 36341, "reasoning_output_tokens": 21731, "total_tokens": 6568515 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-7038a5a9-9606-47bb-886b-f314ab57fd4d", "process_id": "80547", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/softmax_validation.parquet > /app/solution/softmax_score.json && cat /app/solution/softmax_score.json" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/softmax_validation.parquet > /app/solution/softmax_score.json && cat /app/solution/softmax_score.json" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{\n \"model\": \"/app/solution/softmax_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.02157,\n \"quality_users\": 4263\n }\n}\n", "stderr": "", "aggregated_output": "{\n \"model\": \"/app/solution/softmax_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.02157,\n \"quality_users\": 4263\n }\n}\n", "exit_code": 0, "duration": { "secs": 3, "nanos": 174988175 }, "formatted_output": "{\n \"model\": \"/app/solution/softmax_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.02157,\n \"quality_users\": 4263\n }\n}\n" } } ## Step 86 · agent · 2026-09-23T05:56:12.540Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T05:56:13.025Z { "code": "text(await tools.write_stdin({session_id:80547,chars:\"\",yield_time_ms:1000,max_output_tokens:1000}));\ntext(await tools.clock__curr_time({}));\n" } ### Tool output · 2026-09-23T05:56:13.127Z Script completed Wall time 0.0 seconds Output: {"chunk_id":"b13c97","wall_time_seconds":0.00000193,"exit_code":0,"original_token_count":34,"output":"{\n \"model\": \"/app/solution/softmax_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.02157,\n \"quality_users\": 4263\n }\n}\n"} {"current_time":"2026-09-23 05:56:13 UTC"} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "session_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "root_turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "response_id": "resp_03ec3525bea3ac86016ab369f89e6c87d2a01b3ce54892f4a5", "usage": { "input_tokens": 96211, "cached_input_tokens": 95872, "cache_write_input_tokens": 0, "output_tokens": 86, "reasoning_output_tokens": 36, "total_tokens": 96297 }, "turn_token_usage": { "input_tokens": 6628385, "cached_input_tokens": 6404352, "cache_write_input_tokens": 0, "output_tokens": 36427, "reasoning_output_tokens": 21767, "total_tokens": 6664812 }, "thread_token_usage": { "input_tokens": 6628385, "cached_input_tokens": 6404352, "cache_write_input_tokens": 0, "output_tokens": 36427, "reasoning_output_tokens": 21767, "total_tokens": 6664812 } } ## Step 87 · agent · 2026-09-23T05:56:18.941Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T05:56:23.856Z { "code": "text(await tools.exec_command({cmd:\"/opt/task-venv/bin/python /app/solution/train_current_model.py --stage validation --public-dir /task --output /app/solution/graded_validation.parquet --prepared-cache /app/solution/validation_cache.npz --checkpoint-dir /app/solution/graded_ckpt --resume-checkpoint /app/solution/production_ckpt/best.pt --epochs 2 --hard-negatives 0 --training-objective shared-softmax --shared-pool-size 4096 --learning-rate 0.0005 --target-weighting discovery-graded --organic-like-target-weight 7 --ordinary-target-weight 0.1 --threads 8 --loss-microbatch-size 4096 > /app/solution/graded.json 2> /app/solution/graded.log\",yield_time_ms:1000}));\n" } ### Tool output · 2026-09-23T05:56:25.023Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"c29d04","wall_time_seconds":1.001172647,"session_id":89798,"original_token_count":0,"output":""} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "session_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "root_turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "response_id": "resp_03ec3525bea3ac86016ab369fd64fc87d2aa27988aa9e072a4", "usage": { "input_tokens": 96425, "cached_input_tokens": 96000, "cache_write_input_tokens": 0, "output_tokens": 309, "reasoning_output_tokens": 122, "total_tokens": 96734 }, "turn_token_usage": { "input_tokens": 6724810, "cached_input_tokens": 6500352, "cache_write_input_tokens": 0, "output_tokens": 36736, "reasoning_output_tokens": 21889, "total_tokens": 6761546 }, "thread_token_usage": { "input_tokens": 6724810, "cached_input_tokens": 6500352, "cache_write_input_tokens": 0, "output_tokens": 36736, "reasoning_output_tokens": 21889, "total_tokens": 6761546 } } ## Step 88 · agent · 2026-09-23T05:56:50.525Z ### Thinking unavailable — no text recorded ## Step 89 · agent · 2026-09-23T05:57:08.592Z ### Thinking unavailable — no text recorded Shared softmax scored **0.021570**, a small improvement over production and above the matched BCE control. I’m testing one final adjustment with that sampler: the trainer’s existing discovery-graded weights, with less weight on repeats and more on observed organic likes. Validation outcomes remain excluded from training. ### exec · 2026-09-23T05:57:11.596Z { "code": "text(await tools.exec_command({cmd:\"tail -n 6 /app/solution/graded.log\",max_output_tokens:1200}));\ntext(await tools.clock__curr_time({}));\n" } ### Tool output · 2026-09-23T05:57:11.728Z Script completed Wall time 0.0 seconds Output: {"chunk_id":"02bf3d","wall_time_seconds":0.0000045,"exit_code":0,"original_token_count":144,"output":"grade-3 first-encounter targets=46,417 horizon_days=7\ntarget weighting=discovery-graded min=0.100 mean=0.180 max=7.000\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/production_ckpt/best.pt\nBCE objective changed: filter full-training-history->full-training-history, target weighting organic-strong->discovery-graded, target window (0.1, 128, 16)->(0.1, 128, 16); recomputing the resume checkpoint's validation BCE\nresumed validation-BCE training: epoch=1 loss=9.223783\n"} {"current_time":"2026-09-23 05:57:11 UTC"} ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-b9b73665-7cf6-4d89-8ca8-8ebbb334bd99", "process_id": "19063", "command": [ "/bin/sh", "-lc", "tail -n 6 /app/solution/graded.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "read", "cmd": "tail -n 6 /app/solution/graded.log", "name": "graded.log", "path": "/app/solution/graded.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "grade-3 first-encounter targets=46,417 horizon_days=7\ntarget weighting=discovery-graded min=0.100 mean=0.180 max=7.000\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/production_ckpt/best.pt\nBCE objective changed: filter full-training-history->full-training-history, target weighting organic-strong->discovery-graded, target window (0.1, 128, 16)->(0.1, 128, 16); recomputing the resume checkpoint's validation BCE\nresumed validation-BCE training: epoch=1 loss=9.223783\n", "stderr": "", "aggregated_output": "grade-3 first-encounter targets=46,417 horizon_days=7\ntarget weighting=discovery-graded min=0.100 mean=0.180 max=7.000\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/production_ckpt/best.pt\nBCE objective changed: filter full-training-history->full-training-history, target weighting organic-strong->discovery-graded, target window (0.1, 128, 16)->(0.1, 128, 16); recomputing the resume checkpoint's validation BCE\nresumed validation-BCE training: epoch=1 loss=9.223783\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4500 }, "formatted_output": "grade-3 first-encounter targets=46,417 horizon_days=7\ntarget weighting=discovery-graded min=0.100 mean=0.180 max=7.000\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/production_ckpt/best.pt\nBCE objective changed: filter full-training-history->full-training-history, target weighting organic-strong->discovery-graded, target window (0.1, 128, 16)->(0.1, 128, 16); recomputing the resume checkpoint's validation BCE\nresumed validation-BCE training: epoch=1 loss=9.223783\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "session_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "root_turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "response_id": "resp_03ec3525bea3ac86016ab36a09334887d2aebb1421798d7fcc", "usage": { "input_tokens": 96792, "cached_input_tokens": 96256, "cache_write_input_tokens": 0, "output_tokens": 1150, "reasoning_output_tokens": 1034, "total_tokens": 97942 }, "turn_token_usage": { "input_tokens": 6821602, "cached_input_tokens": 6596608, "cache_write_input_tokens": 0, "output_tokens": 37886, "reasoning_output_tokens": 22923, "total_tokens": 6859488 }, "thread_token_usage": { "input_tokens": 6821602, "cached_input_tokens": 6596608, "cache_write_input_tokens": 0, "output_tokens": 37886, "reasoning_output_tokens": 22923, "total_tokens": 6859488 } } ## Step 90 · agent · 2026-09-23T05:57:40.628Z ### Thinking unavailable — no text recorded ## Step 91 · agent · 2026-09-23T05:58:02.875Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T05:58:03.624Z { "code": "text(await tools.exec_command({cmd:\"tail -n 4 /app/solution/graded.log\",max_output_tokens:1000}));\ntext(await tools.clock__curr_time({}));\n" } ### Tool output · 2026-09-23T05:58:03.747Z Script completed Wall time 0.1 seconds Output: {"chunk_id":"e543a5","wall_time_seconds":0.00000464,"exit_code":0,"original_token_count":116,"output":"BCE objective changed: filter full-training-history->full-training-history, target weighting organic-strong->discovery-graded, target window (0.1, 128, 16)->(0.1, 128, 16); recomputing the resume checkpoint's validation BCE\nresumed validation-BCE training: epoch=1 loss=9.223783\nepoch=2/2 batches=50 targets=206,457 batch_bce=8.871449 running_bce=9.038624 elapsed=27.4s\nepoch=2/2 batches=100 targets=402,892 batch_bce=8.845679 running_bce=9.052199 elapsed=53.8s\n"} {"current_time":"2026-09-23 05:58:03 UTC"} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "session_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "root_turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "response_id": "resp_03ec3525bea3ac86016ab36a380c8087d280e7e2db7777f479", "usage": { "input_tokens": 98181, "cached_input_tokens": 96640, "cache_write_input_tokens": 0, "output_tokens": 1083, "reasoning_output_tokens": 1034, "total_tokens": 99264 }, "turn_token_usage": { "input_tokens": 6919783, "cached_input_tokens": 6693248, "cache_write_input_tokens": 0, "output_tokens": 38969, "reasoning_output_tokens": 23957, "total_tokens": 6958752 }, "thread_token_usage": { "input_tokens": 6919783, "cached_input_tokens": 6693248, "cache_write_input_tokens": 0, "output_tokens": 38969, "reasoning_output_tokens": 23957, "total_tokens": 6958752 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-5ae30767-6a32-4aa7-8280-0dd7b3835c08", "process_id": "16922", "command": [ "/bin/sh", "-lc", "tail -n 4 /app/solution/graded.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "read", "cmd": "tail -n 4 /app/solution/graded.log", "name": "graded.log", "path": "/app/solution/graded.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "BCE objective changed: filter full-training-history->full-training-history, target weighting organic-strong->discovery-graded, target window (0.1, 128, 16)->(0.1, 128, 16); recomputing the resume checkpoint's validation BCE\nresumed validation-BCE training: epoch=1 loss=9.223783\nepoch=2/2 batches=50 targets=206,457 batch_bce=8.871449 running_bce=9.038624 elapsed=27.4s\nepoch=2/2 batches=100 targets=402,892 batch_bce=8.845679 running_bce=9.052199 elapsed=53.8s\n", "stderr": "", "aggregated_output": "BCE objective changed: filter full-training-history->full-training-history, target weighting organic-strong->discovery-graded, target window (0.1, 128, 16)->(0.1, 128, 16); recomputing the resume checkpoint's validation BCE\nresumed validation-BCE training: epoch=1 loss=9.223783\nepoch=2/2 batches=50 targets=206,457 batch_bce=8.871449 running_bce=9.038624 elapsed=27.4s\nepoch=2/2 batches=100 targets=402,892 batch_bce=8.845679 running_bce=9.052199 elapsed=53.8s\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4640 }, "formatted_output": "BCE objective changed: filter full-training-history->full-training-history, target weighting organic-strong->discovery-graded, target window (0.1, 128, 16)->(0.1, 128, 16); recomputing the resume checkpoint's validation BCE\nresumed validation-BCE training: epoch=1 loss=9.223783\nepoch=2/2 batches=50 targets=206,457 batch_bce=8.871449 running_bce=9.038624 elapsed=27.4s\nepoch=2/2 batches=100 targets=402,892 batch_bce=8.845679 running_bce=9.052199 elapsed=53.8s\n" } } ## Step 92 · agent · 2026-09-23T05:58:29.362Z ### Thinking unavailable — no text recorded ## Step 93 · agent · 2026-09-23T05:58:44.345Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T05:58:47.564Z { "code": "text(await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\nimport numpy as np\\nimport pandas as pd\\nwith np.load('/task/data/validation_eligible_candidates.npz') as a:\\n ids=a['item_ids']; u=a['uids']; packed=a['packed_eligible']\\n chosen=np.stack([ids[np.flatnonzero(np.unpackbits(r,count=len(ids),bitorder='little'))[:100]] for r in packed])\\n pd.DataFrame({'uid':np.repeat(u,100),'item_id':chosen.ravel(),'rank':np.tile(np.arange(1,101),len(u))}).to_parquet('/app/solution/popularity_diagnostic.parquet',index=False)\\nPY\\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/popularity_diagnostic.parquet > /app/solution/popularity_score.json\",yield_time_ms:1000,max_output_tokens:1000}));\ntext(await tools.exec_command({cmd:\"tail -n 4 /app/solution/graded.log\",max_output_tokens:1000}));\n" } ### Tool output · 2026-09-23T05:58:48.856Z Script completed Wall time 1.3 seconds Output: {"chunk_id":"bc27c5","wall_time_seconds":1.000527993,"session_id":47508,"original_token_count":0,"output":""} {"chunk_id":"da96ed","wall_time_seconds":0.00000392,"exit_code":0,"original_token_count":92,"output":"epoch=2/2 batches=50 targets=206,457 batch_bce=8.871449 running_bce=9.038624 elapsed=27.4s\nepoch=2/2 batches=100 targets=402,892 batch_bce=8.845679 running_bce=9.052199 elapsed=53.8s\nepoch=2/2 batches=150 targets=596,745 batch_bce=9.095837 running_bce=9.014472 elapsed=80.3s\nepoch=2/2 batches=200 targets=790,005 batch_bce=8.779257 running_bce=8.990500 elapsed=106.1s\n"} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "session_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "root_turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "response_id": "resp_03ec3525bea3ac86016ab36a6c1aac87d28470fec9a026388b", "usage": { "input_tokens": 99488, "cached_input_tokens": 74624, "cache_write_input_tokens": 0, "output_tokens": 1193, "reasoning_output_tokens": 950, "total_tokens": 100681 }, "turn_token_usage": { "input_tokens": 7019271, "cached_input_tokens": 6767872, "cache_write_input_tokens": 0, "output_tokens": 40162, "reasoning_output_tokens": 24907, "total_tokens": 7059433 }, "thread_token_usage": { "input_tokens": 7019271, "cached_input_tokens": 6767872, "cache_write_input_tokens": 0, "output_tokens": 40162, "reasoning_output_tokens": 24907, "total_tokens": 7059433 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-91419679-c06a-4b22-a361-fea9e4338928", "process_id": "46248", "command": [ "/bin/sh", "-lc", "tail -n 4 /app/solution/graded.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "read", "cmd": "tail -n 4 /app/solution/graded.log", "name": "graded.log", "path": "/app/solution/graded.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "epoch=2/2 batches=50 targets=206,457 batch_bce=8.871449 running_bce=9.038624 elapsed=27.4s\nepoch=2/2 batches=100 targets=402,892 batch_bce=8.845679 running_bce=9.052199 elapsed=53.8s\nepoch=2/2 batches=150 targets=596,745 batch_bce=9.095837 running_bce=9.014472 elapsed=80.3s\nepoch=2/2 batches=200 targets=790,005 batch_bce=8.779257 running_bce=8.990500 elapsed=106.1s\n", "stderr": "", "aggregated_output": "epoch=2/2 batches=50 targets=206,457 batch_bce=8.871449 running_bce=9.038624 elapsed=27.4s\nepoch=2/2 batches=100 targets=402,892 batch_bce=8.845679 running_bce=9.052199 elapsed=53.8s\nepoch=2/2 batches=150 targets=596,745 batch_bce=9.095837 running_bce=9.014472 elapsed=80.3s\nepoch=2/2 batches=200 targets=790,005 batch_bce=8.779257 running_bce=8.990500 elapsed=106.1s\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 3920 }, "formatted_output": "epoch=2/2 batches=50 targets=206,457 batch_bce=8.871449 running_bce=9.038624 elapsed=27.4s\nepoch=2/2 batches=100 targets=402,892 batch_bce=8.845679 running_bce=9.052199 elapsed=53.8s\nepoch=2/2 batches=150 targets=596,745 batch_bce=9.095837 running_bce=9.014472 elapsed=80.3s\nepoch=2/2 batches=200 targets=790,005 batch_bce=8.779257 running_bce=8.990500 elapsed=106.1s\n" } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-a7b6e3a9-0c12-4c6b-98e8-59e9570f84e3", "process_id": "47508", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\nimport numpy as np\nimport pandas as pd\nwith np.load('/task/data/validation_eligible_candidates.npz') as a:\n ids=a['item_ids']; u=a['uids']; packed=a['packed_eligible']\n chosen=np.stack([ids[np.flatnonzero(np.unpackbits(r,count=len(ids),bitorder='little'))[:100]] for r in packed])\n pd.DataFrame({'uid':np.repeat(u,100),'item_id':chosen.ravel(),'rank':np.tile(np.arange(1,101),len(u))}).to_parquet('/app/solution/popularity_diagnostic.parquet',index=False)\nPY\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/popularity_diagnostic.parquet > /app/solution/popularity_score.json" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport numpy as np\nimport pandas as pd\nwith np.load('/task/data/validation_eligible_candidates.npz') as a:\n ids=a['item_ids']; u=a['uids']; packed=a['packed_eligible']\n chosen=np.stack([ids[np.flatnonzero(np.unpackbits(r,count=len(ids),bitorder='little'))[:100]] for r in packed])\n pd.DataFrame({'uid':np.repeat(u,100),'item_id':chosen.ravel(),'rank':np.tile(np.arange(1,101),len(u))}).to_parquet('/app/solution/popularity_diagnostic.parquet',index=False)\nPY\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/popularity_diagnostic.parquet > /app/solution/popularity_score.json" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 7, "nanos": 843386289 }, "formatted_output": "" } } ## Step 94 · agent · 2026-09-23T05:59:10.343Z ### Thinking unavailable — no text recorded ## Step 95 · agent · 2026-09-23T05:59:14.917Z ### Thinking unavailable — no text recorded The graded run is close to finishing. I’m also checking a popularity-only validation ranking as a diagnostic to see whether the negative losses are suppressing useful catalog popularity information. The final model will retain the two-tower serving path. ### exec · 2026-09-23T05:59:17.206Z { "code": "text(await tools.exec_command({cmd:\"cat /app/solution/popularity_score.json && tail -n 5 /app/solution/graded.log\",max_output_tokens:1000}));\ntext(await tools.clock__curr_time({}));\n" } ### Tool output · 2026-09-23T05:59:17.340Z Script completed Wall time 0.0 seconds Output: {"chunk_id":"9e99a8","wall_time_seconds":0.0000041,"exit_code":0,"original_token_count":150,"output":"{\n \"model\": \"/app/solution/popularity_diagnostic.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.01071,\n \"quality_users\": 4263\n }\n}\nepoch=2/2 batches=50 targets=206,457 batch_bce=8.871449 running_bce=9.038624 elapsed=27.4s\nepoch=2/2 batches=100 targets=402,892 batch_bce=8.845679 running_bce=9.052199 elapsed=53.8s\nepoch=2/2 batches=150 targets=596,745 batch_bce=9.095837 running_bce=9.014472 elapsed=80.3s\nepoch=2/2 batches=200 targets=790,005 batch_bce=8.779257 running_bce=8.990500 elapsed=106.1s\nepoch=2/2 batches=250 targets=987,417 batch_bce=9.314430 running_bce=8.976060 elapsed=137.8s\n"} {"current_time":"2026-09-23 05:59:17 UTC"} ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-deb9e8f6-4cfb-44c6-8713-d0873b460d53", "process_id": "52487", "command": [ "/bin/sh", "-lc", "cat /app/solution/popularity_score.json && tail -n 5 /app/solution/graded.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "read", "cmd": "cat /app/solution/popularity_score.json", "name": "popularity_score.json", "path": "/app/solution/popularity_score.json" }, { "type": "read", "cmd": "tail -n 5 /app/solution/graded.log", "name": "graded.log", "path": "/app/solution/graded.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{\n \"model\": \"/app/solution/popularity_diagnostic.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.01071,\n \"quality_users\": 4263\n }\n}\nepoch=2/2 batches=50 targets=206,457 batch_bce=8.871449 running_bce=9.038624 elapsed=27.4s\nepoch=2/2 batches=100 targets=402,892 batch_bce=8.845679 running_bce=9.052199 elapsed=53.8s\nepoch=2/2 batches=150 targets=596,745 batch_bce=9.095837 running_bce=9.014472 elapsed=80.3s\nepoch=2/2 batches=200 targets=790,005 batch_bce=8.779257 running_bce=8.990500 elapsed=106.1s\nepoch=2/2 batches=250 targets=987,417 batch_bce=9.314430 running_bce=8.976060 elapsed=137.8s\n", "stderr": "", "aggregated_output": "{\n \"model\": \"/app/solution/popularity_diagnostic.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.01071,\n \"quality_users\": 4263\n }\n}\nepoch=2/2 batches=50 targets=206,457 batch_bce=8.871449 running_bce=9.038624 elapsed=27.4s\nepoch=2/2 batches=100 targets=402,892 batch_bce=8.845679 running_bce=9.052199 elapsed=53.8s\nepoch=2/2 batches=150 targets=596,745 batch_bce=9.095837 running_bce=9.014472 elapsed=80.3s\nepoch=2/2 batches=200 targets=790,005 batch_bce=8.779257 running_bce=8.990500 elapsed=106.1s\nepoch=2/2 batches=250 targets=987,417 batch_bce=9.314430 running_bce=8.976060 elapsed=137.8s\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4100 }, "formatted_output": "{\n \"model\": \"/app/solution/popularity_diagnostic.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.01071,\n \"quality_users\": 4263\n }\n}\nepoch=2/2 batches=50 targets=206,457 batch_bce=8.871449 running_bce=9.038624 elapsed=27.4s\nepoch=2/2 batches=100 targets=402,892 batch_bce=8.845679 running_bce=9.052199 elapsed=53.8s\nepoch=2/2 batches=150 targets=596,745 batch_bce=9.095837 running_bce=9.014472 elapsed=80.3s\nepoch=2/2 batches=200 targets=790,005 batch_bce=8.779257 running_bce=8.990500 elapsed=106.1s\nepoch=2/2 batches=250 targets=987,417 batch_bce=9.314430 running_bce=8.976060 elapsed=137.8s\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "session_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "root_turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "response_id": "resp_03ec3525bea3ac86016ab36a99064487d2944659dc1faf18b3", "usage": { "input_tokens": 100918, "cached_input_tokens": 99328, "cache_write_input_tokens": 0, "output_tokens": 775, "reasoning_output_tokens": 663, "total_tokens": 101693 }, "turn_token_usage": { "input_tokens": 7120189, "cached_input_tokens": 6867200, "cache_write_input_tokens": 0, "output_tokens": 40937, "reasoning_output_tokens": 25570, "total_tokens": 7161126 }, "thread_token_usage": { "input_tokens": 7120189, "cached_input_tokens": 6867200, "cache_write_input_tokens": 0, "output_tokens": 40937, "reasoning_output_tokens": 25570, "total_tokens": 7161126 } } ## Step 96 · agent · 2026-09-23T05:59:40.800Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T06:00:25.163Z { "code": "text(await tools.apply_patch(\"*** Begin Patch\\n*** Update File: /app/solution/train_current_model.py\\n@@\\n-\\\"\\\"\\\"Train the Yambda windowed two-tower baseline.\\n+\\\"\\\"\\\"Train the Yambda windowed two-tower with shared-pool negative training.\\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 towers, chronological split, eligibility boundary, and raw dot-product\\n+retrieval are unchanged. Shared sampled softmax scores a large uniform catalog\\n+pool with matrix multiplication and corrects its denominator by N/K. Known\\n+positives and active likes are filtered before the loss. The production BCE and\\n+experimental hard-negative modes remain available for controlled comparisons.\\n+Every selected adjacent target retains a nonzero weight. Only files under the\\n+supplied public directory are read.\\n@@\\n-from dataclasses import asdict, dataclass, replace\\n+from dataclasses import asdict, dataclass\\n@@\\n- loss_microbatch_size: int = 65_536\\n+ loss_microbatch_size: int = 4096\\n@@\\n- hard_negatives: int = 16\\n+ hard_negatives: int = 0\\n@@\\n- hard_warmup_epochs: int = 0\\n@@\\n- training_objective: str = \\\"bce\\\"\\n+ training_objective: str = \\\"shared-softmax\\\"\\n@@\\n- training_config = (replace(config, hard_negatives=0)\\n- if epoch <= config.hard_warmup_epochs else config)\\n diagnostics = run_training_epoch(\\n@@\\n- training_config,\\n+ config,\\n@@\\n- parser.add_argument(\\\"--hard-warmup-epochs\\\", type=int, default=Config.hard_warmup_epochs)\\n@@\\n- hard_warmup_epochs=args.hard_warmup_epochs,\\n@@\\n- \\\"objective\\\": \\\"sample-weighted-BCE-with-optional-hard-pair-ranking\\\",\\n+ \\\"objective\\\": config.training_objective,\\n \\\"negative_sampling\\\": (\\n- f\\\"24-uniform-plus-8-corrected-inbatch-plus-{config.hard_negatives}-mined\\\"\\n+ f\\\"uniform-shared-pool-{config.shared_pool_size}\\\"\\n+ if config.training_objective == \\\"shared-softmax\\\" else\\n+ f\\\"24-uniform-plus-8-corrected-inbatch-plus-{config.hard_negatives}-mined\\\"\\n*** End Patch\"));\ntext(await tools.exec_command({cmd:\"tail -n 4 /app/solution/graded.log\",max_output_tokens:1000}));\n" } ### Tool output · 2026-09-23T06:00:25.299Z Script completed Wall time 0.1 seconds Output: {} {"chunk_id":"e43edb","wall_time_seconds":0.00000341,"exit_code":0,"original_token_count":26,"output":"encoded users=1,600/8,456\nencoded users=3,200/8,456\nencoded users=4,800/8,456\nencoded users=6,400/8,456\n"} ### Harness context { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-e0956363-93c4-4464-9dd0-9c62c38e6fc7", "changes": { "/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 the Yambda windowed two-tower with shared-pool negative training.\n \n@@ -11,7 +11,9 @@\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 towers, chronological split, eligibility boundary, and raw dot-product\n+retrieval are unchanged. Shared sampled softmax scores a large uniform catalog\n+pool with matrix multiplication and corrects its denominator by N/K. Known\n+positives and active likes are filtered before the loss. The production BCE and\n+experimental hard-negative modes remain available for controlled comparisons.\n+Every selected adjacent target retains a nonzero weight. Only files under the\n+supplied public directory are read.\n \"\"\"\n@@ -24,3 +26,3 @@\n import time\n-from dataclasses import asdict, dataclass, replace\n+from dataclasses import asdict, dataclass\n from pathlib import Path\n@@ -54,3 +56,3 @@\n gradient_clip: float = 2.0\n- loss_microbatch_size: int = 65_536\n+ loss_microbatch_size: int = 4096\n uniform_negatives: int = 24\n@@ -60,9 +62,8 @@\n inbatch_correction_max: float = 4.0\n- hard_negatives: int = 16\n+ hard_negatives: int = 0\n hard_pool_size: int = 1024\n hard_negative_weight: float = 0.5\n- hard_warmup_epochs: int = 0\n hard_objective: str = \"pairwise\"\n hard_include_inbatch: bool = False\n- training_objective: str = \"bce\"\n+ training_objective: str = \"shared-softmax\"\n shared_pool_size: int = 4096\n@@ -1832,4 +1833,2 @@\n for epoch in range(start_epoch + 1, config.epochs + 1):\n- training_config = (replace(config, hard_negatives=0)\n- if epoch <= config.hard_warmup_epochs else config)\n diagnostics = run_training_epoch(\n@@ -1838,3 +1837,3 @@\n prepared,\n- training_config,\n+ config,\n target_weights,\n@@ -2088,3 +2087,2 @@\n parser.add_argument(\"--hard-negative-weight\", type=float, default=Config.hard_negative_weight)\n- parser.add_argument(\"--hard-warmup-epochs\", type=int, default=Config.hard_warmup_epochs)\n parser.add_argument(\"--hard-objective\", choices=(\"bce\", \"pairwise\"), default=Config.hard_objective)\n@@ -2204,3 +2202,2 @@\n hard_negative_weight=args.hard_negative_weight,\n- hard_warmup_epochs=args.hard_warmup_epochs,\n hard_objective=args.hard_objective,\n@@ -2316,4 +2313,6 @@\n \"score\": \"raw-dot-product\",\n- \"objective\": \"sample-weighted-BCE-with-optional-hard-pair-ranking\",\n+ \"objective\": config.training_objective,\n \"negative_sampling\": (\n+ f\"uniform-shared-pool-{config.shared_pool_size}\"\n+ if config.training_objective == \"shared-softmax\" else\n f\"24-uniform-plus-8-corrected-inbatch-plus-{config.hard_negatives}-mined\"\n", "move_path": null } }, "status": "completed", "stdout": "Success. Updated the following files:\nM /app/solution/train_current_model.py\n", "stderr": "" } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-5139df40-9c1d-4d87-a58a-e928dbe132cf", "process_id": "43985", "command": [ "/bin/sh", "-lc", "tail -n 4 /app/solution/graded.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "read", "cmd": "tail -n 4 /app/solution/graded.log", "name": "graded.log", "path": "/app/solution/graded.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "encoded users=1,600/8,456\nencoded users=3,200/8,456\nencoded users=4,800/8,456\nencoded users=6,400/8,456\n", "stderr": "", "aggregated_output": "encoded users=1,600/8,456\nencoded users=3,200/8,456\nencoded users=4,800/8,456\nencoded users=6,400/8,456\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 3410 }, "formatted_output": "encoded users=1,600/8,456\nencoded users=3,200/8,456\nencoded users=4,800/8,456\nencoded users=6,400/8,456\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "session_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "root_turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "response_id": "resp_03ec3525bea3ac86016ab36ae34c8487d2934c9d4add938962", "usage": { "input_tokens": 102647, "cached_input_tokens": 55936, "cache_write_input_tokens": 0, "output_tokens": 643, "reasoning_output_tokens": 0, "total_tokens": 103290 }, "turn_token_usage": { "input_tokens": 7222836, "cached_input_tokens": 6923136, "cache_write_input_tokens": 0, "output_tokens": 41580, "reasoning_output_tokens": 25570, "total_tokens": 7264416 }, "thread_token_usage": { "input_tokens": 7222836, "cached_input_tokens": 6923136, "cache_write_input_tokens": 0, "output_tokens": 41580, "reasoning_output_tokens": 25570, "total_tokens": 7264416 } } ## Step 97 · agent · 2026-09-23T06:00:49.023Z ### Thinking unavailable — no text recorded ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-70af71ba-2e1b-442b-859f-bff09cd95afc", "process_id": "89798", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python /app/solution/train_current_model.py --stage validation --public-dir /task --output /app/solution/graded_validation.parquet --prepared-cache /app/solution/validation_cache.npz --checkpoint-dir /app/solution/graded_ckpt --resume-checkpoint /app/solution/production_ckpt/best.pt --epochs 2 --hard-negatives 0 --training-objective shared-softmax --shared-pool-size 4096 --learning-rate 0.0005 --target-weighting discovery-graded --organic-like-target-weight 7 --ordinary-target-weight 0.1 --threads 8 --loss-microbatch-size 4096 > /app/solution/graded.json 2> /app/solution/graded.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 --output /app/solution/graded_validation.parquet --prepared-cache /app/solution/validation_cache.npz --checkpoint-dir /app/solution/graded_ckpt --resume-checkpoint /app/solution/production_ckpt/best.pt --epochs 2 --hard-negatives 0 --training-objective shared-softmax --shared-pool-size 4096 --learning-rate 0.0005 --target-weighting discovery-graded --organic-like-target-weight 7 --ordinary-target-weight 0.1 --threads 8 --loss-microbatch-size 4096 > /app/solution/graded.json 2> /app/solution/graded.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 268, "nanos": 533961909 }, "formatted_output": "" } } ## Step 98 · agent · 2026-09-23T06:00:57.462Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T06:00:59.644Z { "code": "text(await tools.apply_patch(\"*** Begin Patch\\n*** Update File: /app/solution/rank_checkpoint.py\\n@@\\n- config = trainer.Config(**payload[\\\"config\\\"])\\n+ config = trainer.Config(**{k: v for k, v in payload[\\\"config\\\"].items()\\n+ if k in trainer.Config.__dataclass_fields__})\\n*** End Patch\"));\ntext(await tools.exec_command({cmd:\"tail -n 3 /app/solution/graded.log && ls -lh /app/solution/graded_validation.parquet\",max_output_tokens:1000}));\ntext(await tools.clock__curr_time({}));\n" } ### Tool output · 2026-09-23T06:00:59.735Z Script completed Wall time 0.1 seconds Output: {} {"chunk_id":"e6fd28","wall_time_seconds":0.00000979,"exit_code":0,"original_token_count":40,"output":"ranked users=7,168/8,456\nranked users=8,192/8,456\nranked users=8,456/8,456\n-rw-r--r-- 1 agent agent 1.2M Sep 23 06:00 /app/solution/graded_validation.parquet\n"} {"current_time":"2026-09-23 06:00:59 UTC"} ### Harness context { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-819fa38d-fee9-40a7-9d0a-57e8a2301219", "changes": { "/app/solution/rank_checkpoint.py": { "type": "update", "unified_diff": "@@ -21,3 +21,4 @@\n payload = torch.load(args.checkpoint, map_location=\"cpu\", weights_only=False)\n- config = trainer.Config(**payload[\"config\"])\n+ config = trainer.Config(**{k: v for k, v in payload[\"config\"].items()\n+ if k in trainer.Config.__dataclass_fields__})\n prepared, eligibility = trainer.load_or_prepare(\n", "move_path": null } }, "status": "completed", "stdout": "Success. Updated the following files:\nM /app/solution/rank_checkpoint.py\n", "stderr": "" } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-db21bf3f-c471-4c96-8335-3340f43f68a6", "process_id": "18078", "command": [ "/bin/sh", "-lc", "tail -n 3 /app/solution/graded.log && ls -lh /app/solution/graded_validation.parquet" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "read", "cmd": "tail -n 3 /app/solution/graded.log", "name": "graded.log", "path": "/app/solution/graded.log" }, { "type": "list_files", "cmd": "ls -lh /app/solution/graded_validation.parquet", "path": "graded_validation.parquet" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "ranked users=7,168/8,456\nranked users=8,192/8,456\nranked users=8,456/8,456\n-rw-r--r-- 1 agent agent 1.2M Sep 23 06:00 /app/solution/graded_validation.parquet\n", "stderr": "", "aggregated_output": "ranked users=7,168/8,456\nranked users=8,192/8,456\nranked users=8,456/8,456\n-rw-r--r-- 1 agent agent 1.2M Sep 23 06:00 /app/solution/graded_validation.parquet\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 9790 }, "formatted_output": "ranked users=7,168/8,456\nranked users=8,192/8,456\nranked users=8,456/8,456\n-rw-r--r-- 1 agent agent 1.2M Sep 23 06:00 /app/solution/graded_validation.parquet\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "session_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "root_turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "response_id": "resp_03ec3525bea3ac86016ab36af9b12087d294747fa8e7bc10a2", "usage": { "input_tokens": 103391, "cached_input_tokens": 102528, "cache_write_input_tokens": 0, "output_tokens": 891, "reasoning_output_tokens": 746, "total_tokens": 104282 }, "turn_token_usage": { "input_tokens": 7326227, "cached_input_tokens": 7025664, "cache_write_input_tokens": 0, "output_tokens": 42471, "reasoning_output_tokens": 26316, "total_tokens": 7368698 }, "thread_token_usage": { "input_tokens": 7326227, "cached_input_tokens": 7025664, "cache_write_input_tokens": 0, "output_tokens": 42471, "reasoning_output_tokens": 26316, "total_tokens": 7368698 } } ### exec · 2026-09-23T06:01:06.151Z { "code": "text(await tools.exec_command({cmd:\"/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/graded_validation.parquet > /app/solution/graded_score.json && cat /app/solution/graded_score.json\",yield_time_ms:1000,max_output_tokens:1000}));\ntext(await tools.write_stdin({session_id:89798,chars:\"\",yield_time_ms:1000,max_output_tokens:1000}));\n" } ### Tool output · 2026-09-23T06:01:07.319Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"c2db6e","wall_time_seconds":1.000570264,"session_id":84062,"original_token_count":0,"output":""} {"chunk_id":"4ab183","wall_time_seconds":0.00000164,"exit_code":0,"original_token_count":0,"output":""} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "session_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "root_turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "response_id": "resp_03ec3525bea3ac86016ab36b1bfea087d2b2f8b3110a33ce3c", "usage": { "input_tokens": 104425, "cached_input_tokens": 103168, "cache_write_input_tokens": 0, "output_tokens": 103, "reasoning_output_tokens": 0, "total_tokens": 104528 }, "turn_token_usage": { "input_tokens": 7430652, "cached_input_tokens": 7128832, "cache_write_input_tokens": 0, "output_tokens": 42574, "reasoning_output_tokens": 26316, "total_tokens": 7473226 }, "thread_token_usage": { "input_tokens": 7430652, "cached_input_tokens": 7128832, "cache_write_input_tokens": 0, "output_tokens": 42574, "reasoning_output_tokens": 26316, "total_tokens": 7473226 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-0c435135-a9c9-4dda-8edb-7833db8bff46", "process_id": "84062", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/graded_validation.parquet > /app/solution/graded_score.json && cat /app/solution/graded_score.json" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/graded_validation.parquet > /app/solution/graded_score.json && cat /app/solution/graded_score.json" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{\n \"model\": \"/app/solution/graded_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.022637,\n \"quality_users\": 4263\n }\n}\n", "stderr": "", "aggregated_output": "{\n \"model\": \"/app/solution/graded_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.022637,\n \"quality_users\": 4263\n }\n}\n", "exit_code": 0, "duration": { "secs": 3, "nanos": 258944826 }, "formatted_output": "{\n \"model\": \"/app/solution/graded_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.022637,\n \"quality_users\": 4263\n }\n}\n" } } ## Step 99 · agent · 2026-09-23T06:01:15.720Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T06:01:16.658Z { "code": "text(await tools.write_stdin({session_id:84062,chars:\"\",yield_time_ms:1000,max_output_tokens:1000}));\n" } ### Tool output · 2026-09-23T06:01:17.228Z Script completed Wall time 0.0 seconds Output: {"chunk_id":"00a016","wall_time_seconds":0.00000195,"exit_code":0,"original_token_count":34,"output":"{\n \"model\": \"/app/solution/graded_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.022637,\n \"quality_users\": 4263\n }\n}\n"} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "session_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "root_turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "response_id": "resp_03ec3525bea3ac86016ab36b237f5c87d2b67c95e3282370cc", "usage": { "input_tokens": 104620, "cached_input_tokens": 104192, "cache_write_input_tokens": 0, "output_tokens": 98, "reasoning_output_tokens": 57, "total_tokens": 104718 }, "turn_token_usage": { "input_tokens": 7535272, "cached_input_tokens": 7233024, "cache_write_input_tokens": 0, "output_tokens": 42672, "reasoning_output_tokens": 26373, "total_tokens": 7577944 }, "thread_token_usage": { "input_tokens": 7535272, "cached_input_tokens": 7233024, "cache_write_input_tokens": 0, "output_tokens": 42672, "reasoning_output_tokens": 26373, "total_tokens": 7577944 } } ## Step 100 · agent · 2026-09-23T06:01:41.206Z ### Thinking unavailable — no text recorded ## Step 101 · agent · 2026-09-23T06:02:00.966Z ### Thinking unavailable — no text recorded ## Step 102 · agent · 2026-09-23T06:02:09.522Z ### Thinking unavailable — no text recorded Discovery-graded shared softmax scored **0.022637**, about **6.4% above production**. I’ll test one more pass of that selected objective, then refit it on `train_* + val_*` from the production checkpoint and write the final test ranking. ### exec · 2026-09-23T06:02:14.006Z { "code": "text(await tools.exec_command({cmd:\"/opt/task-venv/bin/python /app/solution/train_current_model.py --stage validation --public-dir /task --output /app/solution/graded3_validation.parquet --prepared-cache /app/solution/validation_cache.npz --checkpoint-dir /app/solution/graded3_ckpt --resume-checkpoint /app/solution/graded_ckpt/best.pt --epochs 3 --hard-negatives 0 --training-objective shared-softmax --shared-pool-size 4096 --learning-rate 0.0005 --target-weighting discovery-graded --organic-like-target-weight 7 --ordinary-target-weight 0.1 --threads 8 --loss-microbatch-size 4096 > /app/solution/graded3.json 2> /app/solution/graded3.log\",yield_time_ms:1000}));\ntext(await tools.clock__curr_time({}));\n" } ### Tool output · 2026-09-23T06:02:15.174Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"91d6a2","wall_time_seconds":1.000437508,"session_id":88457,"original_token_count":0,"output":""} {"current_time":"2026-09-23 06:02:15 UTC"} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "session_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "root_turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "response_id": "resp_03ec3525bea3ac86016ab36b2d6bb887d28095187437b712df", "usage": { "input_tokens": 104826, "cached_input_tokens": 104448, "cache_write_input_tokens": 0, "output_tokens": 1561, "reasoning_output_tokens": 1297, "total_tokens": 106387 }, "turn_token_usage": { "input_tokens": 7640098, "cached_input_tokens": 7337472, "cache_write_input_tokens": 0, "output_tokens": 44233, "reasoning_output_tokens": 27670, "total_tokens": 7684331 }, "thread_token_usage": { "input_tokens": 7640098, "cached_input_tokens": 7337472, "cache_write_input_tokens": 0, "output_tokens": 44233, "reasoning_output_tokens": 27670, "total_tokens": 7684331 } } ## Step 103 · agent · 2026-09-23T06:02:41.896Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T06:02:43.869Z { "code": "text(await tools.exec_command({cmd:\"/opt/task-venv/bin/python /app/solution/test_shared_softmax.py > /app/solution/loss_test.txt && /opt/task-venv/bin/python /app/solution/test_mining.py >> /app/solution/loss_test.txt && cat /app/solution/loss_test.txt\",yield_time_ms:1000,max_output_tokens:1000}));\ntext(await tools.exec_command({cmd:\"tail -n 6 /app/solution/graded3.log\",max_output_tokens:1000}));\n" } ### Tool output · 2026-09-23T06:02:45.167Z Script completed Wall time 1.3 seconds Output: {"chunk_id":"879d11","wall_time_seconds":1.001424034,"session_id":87901,"original_token_count":0,"output":""} {"chunk_id":"7b392a","wall_time_seconds":0.00000405,"exit_code":0,"original_token_count":97,"output":"loaded organic-like outcomes: rows=301,527 users=7,526\ngrade-3 first-encounter targets=46,417 horizon_days=7\ntarget weighting=discovery-graded min=0.100 mean=0.180 max=7.000\nfull-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250\nrestored optimizer state from /app/solution/graded_ckpt/best.pt\nresumed validation-BCE training: epoch=2 loss=8.964246\n"} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "session_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "root_turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "response_id": "resp_03ec3525bea3ac86016ab36b6772f487d2b5297bafb7c362cc", "usage": { "input_tokens": 106464, "cached_input_tokens": 104704, "cache_write_input_tokens": 0, "output_tokens": 641, "reasoning_output_tokens": 516, "total_tokens": 107105 }, "turn_token_usage": { "input_tokens": 7746562, "cached_input_tokens": 7442176, "cache_write_input_tokens": 0, "output_tokens": 44874, "reasoning_output_tokens": 28186, "total_tokens": 7791436 }, "thread_token_usage": { "input_tokens": 7746562, "cached_input_tokens": 7442176, "cache_write_input_tokens": 0, "output_tokens": 44874, "reasoning_output_tokens": 28186, "total_tokens": 7791436 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-2ddd93a5-eecc-470c-8e4d-c81e0ecc5a4b", "process_id": "92634", "command": [ "/bin/sh", "-lc", "tail -n 6 /app/solution/graded3.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "read", "cmd": "tail -n 6 /app/solution/graded3.log", "name": "graded3.log", "path": "/app/solution/graded3.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "loaded organic-like outcomes: rows=301,527 users=7,526\ngrade-3 first-encounter targets=46,417 horizon_days=7\ntarget weighting=discovery-graded min=0.100 mean=0.180 max=7.000\nfull-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250\nrestored optimizer state from /app/solution/graded_ckpt/best.pt\nresumed validation-BCE training: epoch=2 loss=8.964246\n", "stderr": "", "aggregated_output": "loaded organic-like outcomes: rows=301,527 users=7,526\ngrade-3 first-encounter targets=46,417 horizon_days=7\ntarget weighting=discovery-graded min=0.100 mean=0.180 max=7.000\nfull-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250\nrestored optimizer state from /app/solution/graded_ckpt/best.pt\nresumed validation-BCE training: epoch=2 loss=8.964246\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4050 }, "formatted_output": "loaded organic-like outcomes: rows=301,527 users=7,526\ngrade-3 first-encounter targets=46,417 horizon_days=7\ntarget weighting=discovery-graded min=0.100 mean=0.180 max=7.000\nfull-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250\nrestored optimizer state from /app/solution/graded_ckpt/best.pt\nresumed validation-BCE training: epoch=2 loss=8.964246\n" } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-f21acc9d-41b0-4898-a5c6-92cb0ae474c6", "process_id": "87901", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python /app/solution/test_shared_softmax.py > /app/solution/loss_test.txt && /opt/task-venv/bin/python /app/solution/test_mining.py >> /app/solution/loss_test.txt && cat /app/solution/loss_test.txt" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python /app/solution/test_shared_softmax.py > /app/solution/loss_test.txt && /opt/task-venv/bin/python /app/solution/test_mining.py >> /app/solution/loss_test.txt && cat /app/solution/loss_test.txt" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "shared-softmax matches exact filtered catalog CE and propagates finite tower gradients\nhard-mining filtering, uniqueness, determinism, and top-k checks passed\n", "stderr": "", "aggregated_output": "shared-softmax matches exact filtered catalog CE and propagates finite tower gradients\nhard-mining filtering, uniqueness, determinism, and top-k checks passed\n", "exit_code": 0, "duration": { "secs": 10, "nanos": 989693784 }, "formatted_output": "shared-softmax matches exact filtered catalog CE and propagates finite tower gradients\nhard-mining filtering, uniqueness, determinism, and top-k checks passed\n" } } ## Step 104 · agent · 2026-09-23T06:03:05.911Z ### Thinking unavailable — no text recorded ## Step 105 · agent · 2026-09-23T06:03:18.465Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T06:03:21.689Z { "code": "text(await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\nfrom pathlib import Path\\np=Path('/app/solution/train_current_model.py')\\ns=p.read_text().replace('validation-BCE', 'validation-objective').replace('chronological BCE window:', 'chronological target window:').replace('BCE objective changed:', 'training objective changed:').replace('yambda-windowed-sequence-two-tower-hard-negative','yambda-windowed-sequence-two-tower-improved')\\np.write_text(s)\\nPY\",max_output_tokens:1000}));\ntext(await tools.write_stdin({session_id:87901,chars:\"\",yield_time_ms:1000,max_output_tokens:1000}));\ntext(await tools.exec_command({cmd:\"tail -n 3 /app/solution/graded3.log\",max_output_tokens:1000}));\ntext(await tools.clock__curr_time({}));\n" } ### Tool output · 2026-09-23T06:03:21.995Z Script completed Wall time 0.3 seconds Output: {"chunk_id":"1daab9","wall_time_seconds":0.00000311,"exit_code":0,"original_token_count":0,"output":""} {"chunk_id":"17db9c","wall_time_seconds":0.00000236,"exit_code":0,"original_token_count":40,"output":"shared-softmax matches exact filtered catalog CE and propagates finite tower gradients\nhard-mining filtering, uniqueness, determinism, and top-k checks passed\n"} {"chunk_id":"298360","wall_time_seconds":0.000003011,"exit_code":0,"original_token_count":53,"output":"restored optimizer state from /app/solution/graded_ckpt/best.pt\nresumed validation-BCE training: epoch=2 loss=8.964246\nepoch=3/3 batches=50 targets=199,527 batch_bce=8.673316 running_bce=8.767642 elapsed=34.0s\n"} {"current_time":"2026-09-23 06:03:21 UTC"} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "session_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "root_turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "response_id": "resp_03ec3525bea3ac86016ab36b85582487d2893fe98dd28671d3", "usage": { "input_tokens": 107315, "cached_input_tokens": 106240, "cache_write_input_tokens": 0, "output_tokens": 1073, "reasoning_output_tokens": 869, "total_tokens": 108388 }, "turn_token_usage": { "input_tokens": 7853877, "cached_input_tokens": 7548416, "cache_write_input_tokens": 0, "output_tokens": 45947, "reasoning_output_tokens": 29055, "total_tokens": 7899824 }, "thread_token_usage": { "input_tokens": 7853877, "cached_input_tokens": 7548416, "cache_write_input_tokens": 0, "output_tokens": 45947, "reasoning_output_tokens": 29055, "total_tokens": 7899824 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-b7ccb1b6-25c0-4cd0-8401-c65d95a40baf", "process_id": "2049", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\np=Path('/app/solution/train_current_model.py')\ns=p.read_text().replace('validation-BCE', 'validation-objective').replace('chronological BCE window:', 'chronological target window:').replace('BCE objective changed:', 'training objective changed:').replace('yambda-windowed-sequence-two-tower-hard-negative','yambda-windowed-sequence-two-tower-improved')\np.write_text(s)\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\np=Path('/app/solution/train_current_model.py')\ns=p.read_text().replace('validation-BCE', 'validation-objective').replace('chronological BCE window:', 'chronological target window:').replace('BCE objective changed:', 'training objective changed:').replace('yambda-windowed-sequence-two-tower-hard-negative','yambda-windowed-sequence-two-tower-improved')\np.write_text(s)\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 0, "nanos": 3110 }, "formatted_output": "" } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-0f478b94-6bd7-43ac-af44-00b7e646b630", "process_id": "83069", "command": [ "/bin/sh", "-lc", "tail -n 3 /app/solution/graded3.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "read", "cmd": "tail -n 3 /app/solution/graded3.log", "name": "graded3.log", "path": "/app/solution/graded3.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "restored optimizer state from /app/solution/graded_ckpt/best.pt\nresumed validation-BCE training: epoch=2 loss=8.964246\nepoch=3/3 batches=50 targets=199,527 batch_bce=8.673316 running_bce=8.767642 elapsed=34.0s\n", "stderr": "", "aggregated_output": "restored optimizer state from /app/solution/graded_ckpt/best.pt\nresumed validation-BCE training: epoch=2 loss=8.964246\nepoch=3/3 batches=50 targets=199,527 batch_bce=8.673316 running_bce=8.767642 elapsed=34.0s\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 3011 }, "formatted_output": "restored optimizer state from /app/solution/graded_ckpt/best.pt\nresumed validation-BCE training: epoch=2 loss=8.964246\nepoch=3/3 batches=50 targets=199,527 batch_bce=8.673316 running_bce=8.767642 elapsed=34.0s\n" } } ## Step 106 · agent · 2026-09-23T06:03:43.959Z ### Thinking unavailable — no text recorded The additional pass is training normally, and both loss tests pass after the final code edits. Pure popularity scored **0.010710**, well below the two-tower results. The selected approach keeps the original user and item towers and the same request-time dot product. ### sleep · 2026-09-23T06:03:45.890Z { "duration_ms": 35000 } ### Tool output · 2026-09-23T06:04:20.954Z Wall time: 35.0277 seconds Sleep completed. ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "session_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "root_turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "response_id": "resp_03ec3525bea3ac86016ab36baa504887d29bbcda0e1a44c8ba", "usage": { "input_tokens": 108629, "cached_input_tokens": 107136, "cache_write_input_tokens": 0, "output_tokens": 597, "reasoning_output_tokens": 516, "total_tokens": 109226 }, "turn_token_usage": { "input_tokens": 7962506, "cached_input_tokens": 7655552, "cache_write_input_tokens": 0, "output_tokens": 46544, "reasoning_output_tokens": 29571, "total_tokens": 8009050 }, "thread_token_usage": { "input_tokens": 7962506, "cached_input_tokens": 7655552, "cache_write_input_tokens": 0, "output_tokens": 46544, "reasoning_output_tokens": 29571, "total_tokens": 8009050 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "Extension", "kind": "clock.sleep", "id": "call_mWgLBIf3r6HvNuTyBfwk0g3O", "durationMs": 35000 } } ### exec · 2026-09-23T06:04:25.639Z { "code": "text(await tools.exec_command({cmd:\"tail -n 4 /app/solution/graded3.log\",max_output_tokens:1000}));\ntext(await tools.clock__curr_time({}));\n" } ### Tool output · 2026-09-23T06:04:25.799Z Script completed Wall time 0.1 seconds Output: {"chunk_id":"524744","wall_time_seconds":0.00000342,"exit_code":0,"original_token_count":92,"output":"epoch=3/3 batches=50 targets=199,527 batch_bce=8.673316 running_bce=8.767642 elapsed=34.0s\nepoch=3/3 batches=100 targets=399,112 batch_bce=8.599107 running_bce=8.726359 elapsed=61.0s\nepoch=3/3 batches=150 targets=592,916 batch_bce=8.833855 running_bce=8.707224 elapsed=87.3s\nepoch=3/3 batches=200 targets=790,845 batch_bce=8.424951 running_bce=8.687540 elapsed=113.4s\n"} {"current_time":"2026-09-23 06:04:25 UTC"} ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-237a2506-418c-4a57-81f7-825317e2d719", "process_id": "10251", "command": [ "/bin/sh", "-lc", "tail -n 4 /app/solution/graded3.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "read", "cmd": "tail -n 4 /app/solution/graded3.log", "name": "graded3.log", "path": "/app/solution/graded3.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "epoch=3/3 batches=50 targets=199,527 batch_bce=8.673316 running_bce=8.767642 elapsed=34.0s\nepoch=3/3 batches=100 targets=399,112 batch_bce=8.599107 running_bce=8.726359 elapsed=61.0s\nepoch=3/3 batches=150 targets=592,916 batch_bce=8.833855 running_bce=8.707224 elapsed=87.3s\nepoch=3/3 batches=200 targets=790,845 batch_bce=8.424951 running_bce=8.687540 elapsed=113.4s\n", "stderr": "", "aggregated_output": "epoch=3/3 batches=50 targets=199,527 batch_bce=8.673316 running_bce=8.767642 elapsed=34.0s\nepoch=3/3 batches=100 targets=399,112 batch_bce=8.599107 running_bce=8.726359 elapsed=61.0s\nepoch=3/3 batches=150 targets=592,916 batch_bce=8.833855 running_bce=8.707224 elapsed=87.3s\nepoch=3/3 batches=200 targets=790,845 batch_bce=8.424951 running_bce=8.687540 elapsed=113.4s\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 3420 }, "formatted_output": "epoch=3/3 batches=50 targets=199,527 batch_bce=8.673316 running_bce=8.767642 elapsed=34.0s\nepoch=3/3 batches=100 targets=399,112 batch_bce=8.599107 running_bce=8.726359 elapsed=61.0s\nepoch=3/3 batches=150 targets=592,916 batch_bce=8.833855 running_bce=8.707224 elapsed=87.3s\nepoch=3/3 batches=200 targets=790,845 batch_bce=8.424951 running_bce=8.687540 elapsed=113.4s\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "session_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "root_turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "response_id": "resp_03ec3525bea3ac86016ab36be528c887d28502520651b1e8fd", "usage": { "input_tokens": 109249, "cached_input_tokens": 108416, "cache_write_input_tokens": 0, "output_tokens": 48, "reasoning_output_tokens": 0, "total_tokens": 109297 }, "turn_token_usage": { "input_tokens": 8071755, "cached_input_tokens": 7763968, "cache_write_input_tokens": 0, "output_tokens": 46592, "reasoning_output_tokens": 29571, "total_tokens": 8118347 }, "thread_token_usage": { "input_tokens": 8071755, "cached_input_tokens": 7763968, "cache_write_input_tokens": 0, "output_tokens": 46592, "reasoning_output_tokens": 29571, "total_tokens": 8118347 } } ## Step 107 · agent · 2026-09-23T06:04:46.859Z ### Thinking unavailable — no text recorded ### sleep · 2026-09-23T06:04:46.939Z { "duration_ms": 35000 } ### Tool output · 2026-09-23T06:05:21.971Z Wall time: 35.0210 seconds Sleep completed. ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "session_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "root_turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "response_id": "resp_03ec3525bea3ac86016ab36bea2f5487d2b075d03db58514e1", "usage": { "input_tokens": 109516, "cached_input_tokens": 109056, "cache_write_input_tokens": 0, "output_tokens": 537, "reasoning_output_tokens": 516, "total_tokens": 110053 }, "turn_token_usage": { "input_tokens": 8181271, "cached_input_tokens": 7873024, "cache_write_input_tokens": 0, "output_tokens": 47129, "reasoning_output_tokens": 30087, "total_tokens": 8228400 }, "thread_token_usage": { "input_tokens": 8181271, "cached_input_tokens": 7873024, "cache_write_input_tokens": 0, "output_tokens": 47129, "reasoning_output_tokens": 30087, "total_tokens": 8228400 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "Extension", "kind": "clock.sleep", "id": "call_dqYygN9BFT9D4DBiozCsjeWZ", "durationMs": 35000 } } ### exec · 2026-09-23T06:05:26.109Z { "code": "text(await tools.exec_command({cmd:\"tail -n 6 /app/solution/graded3.log\",max_output_tokens:1000}));\ntext(await tools.clock__curr_time({}));\n" } ### Tool output · 2026-09-23T06:05:26.240Z Script completed Wall time 0.1 seconds Output: {"chunk_id":"873770","wall_time_seconds":0.00000458,"exit_code":0,"original_token_count":93,"output":"saved epoch checkpoint: /app/solution/graded3_ckpt/epoch-03.pt\nepoch=3/3 train_loss=8.66317 validation_loss=8.75902 pair_accuracy=0.870 train_targets=1,052,953 validation_targets=130,103 train_seconds=148.0 validation_seconds=12.3\nrestored best validation-BCE checkpoint: epoch=3 loss=8.759024\nencoded users=1,600/8,456\nencoded users=3,200/8,456\nencoded users=4,800/8,456\n"} {"current_time":"2026-09-23 06:05:26 UTC"} ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-e66268ab-d4e0-4425-a788-35851f677cbb", "process_id": "55431", "command": [ "/bin/sh", "-lc", "tail -n 6 /app/solution/graded3.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "read", "cmd": "tail -n 6 /app/solution/graded3.log", "name": "graded3.log", "path": "/app/solution/graded3.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "saved epoch checkpoint: /app/solution/graded3_ckpt/epoch-03.pt\nepoch=3/3 train_loss=8.66317 validation_loss=8.75902 pair_accuracy=0.870 train_targets=1,052,953 validation_targets=130,103 train_seconds=148.0 validation_seconds=12.3\nrestored best validation-BCE checkpoint: epoch=3 loss=8.759024\nencoded users=1,600/8,456\nencoded users=3,200/8,456\nencoded users=4,800/8,456\n", "stderr": "", "aggregated_output": "saved epoch checkpoint: /app/solution/graded3_ckpt/epoch-03.pt\nepoch=3/3 train_loss=8.66317 validation_loss=8.75902 pair_accuracy=0.870 train_targets=1,052,953 validation_targets=130,103 train_seconds=148.0 validation_seconds=12.3\nrestored best validation-BCE checkpoint: epoch=3 loss=8.759024\nencoded users=1,600/8,456\nencoded users=3,200/8,456\nencoded users=4,800/8,456\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4580 }, "formatted_output": "saved epoch checkpoint: /app/solution/graded3_ckpt/epoch-03.pt\nepoch=3/3 train_loss=8.66317 validation_loss=8.75902 pair_accuracy=0.870 train_targets=1,052,953 validation_targets=130,103 train_seconds=148.0 validation_seconds=12.3\nrestored best validation-BCE checkpoint: epoch=3 loss=8.759024\nencoded users=1,600/8,456\nencoded users=3,200/8,456\nencoded users=4,800/8,456\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "session_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "root_turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "response_id": "resp_03ec3525bea3ac86016ab36c2231fc87d2914cae308625fae2", "usage": { "input_tokens": 110076, "cached_input_tokens": 109312, "cache_write_input_tokens": 0, "output_tokens": 48, "reasoning_output_tokens": 0, "total_tokens": 110124 }, "turn_token_usage": { "input_tokens": 8291347, "cached_input_tokens": 7982336, "cache_write_input_tokens": 0, "output_tokens": 47177, "reasoning_output_tokens": 30087, "total_tokens": 8338524 }, "thread_token_usage": { "input_tokens": 8291347, "cached_input_tokens": 7982336, "cache_write_input_tokens": 0, "output_tokens": 47177, "reasoning_output_tokens": 30087, "total_tokens": 8338524 } } ## Step 108 · agent · 2026-09-23T06:05:50.748Z ### Thinking unavailable — no text recorded The additional pass improved the internal held-out objective and finished training in 148 seconds. Its public ranking is now being generated. Once that score is available, I’ll freeze the pass count and refit for the final submission. ### sleep · 2026-09-23T06:05:52.592Z { "duration_ms": 35000 } ### Tool output · 2026-09-23T06:06:27.659Z Wall time: 35.0343 seconds Sleep completed. ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "session_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "root_turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "response_id": "resp_03ec3525bea3ac86016ab36c2682f887d2947553a64fe152c8", "usage": { "input_tokens": 110319, "cached_input_tokens": 109952, "cache_write_input_tokens": 0, "output_tokens": 589, "reasoning_output_tokens": 516, "total_tokens": 110908 }, "turn_token_usage": { "input_tokens": 8401666, "cached_input_tokens": 8092288, "cache_write_input_tokens": 0, "output_tokens": 47766, "reasoning_output_tokens": 30603, "total_tokens": 8449432 }, "thread_token_usage": { "input_tokens": 8401666, "cached_input_tokens": 8092288, "cache_write_input_tokens": 0, "output_tokens": 47766, "reasoning_output_tokens": 30603, "total_tokens": 8449432 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "Extension", "kind": "clock.sleep", "id": "call_UEvj7TZOQdN56E2Sec0BNLTm", "durationMs": 35000 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-4bd339dc-ddaa-4346-b5e5-af32f70bfd4d", "process_id": "88457", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python /app/solution/train_current_model.py --stage validation --public-dir /task --output /app/solution/graded3_validation.parquet --prepared-cache /app/solution/validation_cache.npz --checkpoint-dir /app/solution/graded3_ckpt --resume-checkpoint /app/solution/graded_ckpt/best.pt --epochs 3 --hard-negatives 0 --training-objective shared-softmax --shared-pool-size 4096 --learning-rate 0.0005 --target-weighting discovery-graded --organic-like-target-weight 7 --ordinary-target-weight 0.1 --threads 8 --loss-microbatch-size 4096 > /app/solution/graded3.json 2> /app/solution/graded3.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 --output /app/solution/graded3_validation.parquet --prepared-cache /app/solution/validation_cache.npz --checkpoint-dir /app/solution/graded3_ckpt --resume-checkpoint /app/solution/graded_ckpt/best.pt --epochs 3 --hard-negatives 0 --training-objective shared-softmax --shared-pool-size 4096 --learning-rate 0.0005 --target-weighting discovery-graded --organic-like-target-weight 7 --ordinary-target-weight 0.1 --threads 8 --loss-microbatch-size 4096 > /app/solution/graded3.json 2> /app/solution/graded3.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 257, "nanos": 465780489 }, "formatted_output": "" } } ### exec · 2026-09-23T06:06:33.110Z { "code": "text(await tools.exec_command({cmd:\"tail -n 4 /app/solution/graded3.log && ls -lh /app/solution/graded3_validation.parquet\",max_output_tokens:1000}));\ntext(await tools.clock__curr_time({}));\n" } ### Tool output · 2026-09-23T06:06:33.232Z Script completed Wall time 0.0 seconds Output: {"chunk_id":"c7e085","wall_time_seconds":0.0000041,"exit_code":0,"original_token_count":46,"output":"ranked users=6,144/8,456\nranked users=7,168/8,456\nranked users=8,192/8,456\nranked users=8,456/8,456\n-rw-r--r-- 1 agent agent 1.4M Sep 23 06:06 /app/solution/graded3_validation.parquet\n"} {"current_time":"2026-09-23 06:06:33 UTC"} ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-333dcb1b-891c-468f-91a6-48aa912661e9", "process_id": "8387", "command": [ "/bin/sh", "-lc", "tail -n 4 /app/solution/graded3.log && ls -lh /app/solution/graded3_validation.parquet" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "read", "cmd": "tail -n 4 /app/solution/graded3.log", "name": "graded3.log", "path": "/app/solution/graded3.log" }, { "type": "list_files", "cmd": "ls -lh /app/solution/graded3_validation.parquet", "path": "graded3_validation.parquet" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "ranked users=6,144/8,456\nranked users=7,168/8,456\nranked users=8,192/8,456\nranked users=8,456/8,456\n-rw-r--r-- 1 agent agent 1.4M Sep 23 06:06 /app/solution/graded3_validation.parquet\n", "stderr": "", "aggregated_output": "ranked users=6,144/8,456\nranked users=7,168/8,456\nranked users=8,192/8,456\nranked users=8,456/8,456\n-rw-r--r-- 1 agent agent 1.4M Sep 23 06:06 /app/solution/graded3_validation.parquet\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4100 }, "formatted_output": "ranked users=6,144/8,456\nranked users=7,168/8,456\nranked users=8,192/8,456\nranked users=8,456/8,456\n-rw-r--r-- 1 agent agent 1.4M Sep 23 06:06 /app/solution/graded3_validation.parquet\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "session_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "root_turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "response_id": "resp_03ec3525bea3ac86016ab36c63e1d487d2a57277df39ebd36c", "usage": { "input_tokens": 110931, "cached_input_tokens": 110080, "cache_write_input_tokens": 0, "output_tokens": 62, "reasoning_output_tokens": 0, "total_tokens": 110993 }, "turn_token_usage": { "input_tokens": 8512597, "cached_input_tokens": 8202368, "cache_write_input_tokens": 0, "output_tokens": 47828, "reasoning_output_tokens": 30603, "total_tokens": 8560425 }, "thread_token_usage": { "input_tokens": 8512597, "cached_input_tokens": 8202368, "cache_write_input_tokens": 0, "output_tokens": 47828, "reasoning_output_tokens": 30603, "total_tokens": 8560425 } } ### exec · 2026-09-23T06:06:39.192Z { "code": "text(await tools.exec_command({cmd:\"/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/graded3_validation.parquet > /app/solution/graded3_score.json && cat /app/solution/graded3_score.json\",yield_time_ms:1000,max_output_tokens:1000}));\ntext(await tools.write_stdin({session_id:88457,chars:\"\",yield_time_ms:1000,max_output_tokens:1000}));\n" } ### Tool output · 2026-09-23T06:06:40.362Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"ed07b3","wall_time_seconds":1.000970023,"session_id":5016,"original_token_count":0,"output":""} {"chunk_id":"038b05","wall_time_seconds":0.00000146,"exit_code":0,"original_token_count":0,"output":""} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "session_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "root_turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "response_id": "resp_03ec3525bea3ac86016ab36c6978a887d2bae5a080d7ac96af", "usage": { "input_tokens": 111148, "cached_input_tokens": 110720, "cache_write_input_tokens": 0, "output_tokens": 106, "reasoning_output_tokens": 0, "total_tokens": 111254 }, "turn_token_usage": { "input_tokens": 8623745, "cached_input_tokens": 8313088, "cache_write_input_tokens": 0, "output_tokens": 47934, "reasoning_output_tokens": 30603, "total_tokens": 8671679 }, "thread_token_usage": { "input_tokens": 8623745, "cached_input_tokens": 8313088, "cache_write_input_tokens": 0, "output_tokens": 47934, "reasoning_output_tokens": 30603, "total_tokens": 8671679 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-e916da40-2ace-462d-94cf-f0f6c3f9e9e7", "process_id": "5016", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/graded3_validation.parquet > /app/solution/graded3_score.json && cat /app/solution/graded3_score.json" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/graded3_validation.parquet > /app/solution/graded3_score.json && cat /app/solution/graded3_score.json" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{\n \"model\": \"/app/solution/graded3_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.02054,\n \"quality_users\": 4263\n }\n}\n", "stderr": "", "aggregated_output": "{\n \"model\": \"/app/solution/graded3_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.02054,\n \"quality_users\": 4263\n }\n}\n", "exit_code": 0, "duration": { "secs": 3, "nanos": 170284608 }, "formatted_output": "{\n \"model\": \"/app/solution/graded3_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.02054,\n \"quality_users\": 4263\n }\n}\n" } } ## Step 109 · agent · 2026-09-23T06:06:45.939Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T06:06:46.891Z { "code": "text(await tools.write_stdin({session_id:5016,chars:\"\",yield_time_ms:1000,max_output_tokens:1000}));\n" } ### Tool output · 2026-09-23T06:06:47.004Z Script completed Wall time 0.0 seconds Output: {"chunk_id":"4ed168","wall_time_seconds":0.00000241,"exit_code":0,"original_token_count":34,"output":"{\n \"model\": \"/app/solution/graded3_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.02054,\n \"quality_users\": 4263\n }\n}\n"} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "session_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "root_turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "response_id": "resp_03ec3525bea3ac86016ab36c70a35887d28c8ace406e607b6b", "usage": { "input_tokens": 111345, "cached_input_tokens": 110976, "cache_write_input_tokens": 0, "output_tokens": 68, "reasoning_output_tokens": 27, "total_tokens": 111413 }, "turn_token_usage": { "input_tokens": 8735090, "cached_input_tokens": 8424064, "cache_write_input_tokens": 0, "output_tokens": 48002, "reasoning_output_tokens": 30630, "total_tokens": 8783092 }, "thread_token_usage": { "input_tokens": 8735090, "cached_input_tokens": 8424064, "cache_write_input_tokens": 0, "output_tokens": 48002, "reasoning_output_tokens": 30630, "total_tokens": 8783092 } } ## Step 110 · agent · 2026-09-23T06:07:09.974Z ### Thinking unavailable — no text recorded ## Step 111 · agent · 2026-09-23T06:07:30.260Z ### Thinking unavailable — no text recorded The extra pass reduced public NDCG to **0.020540**, so I’m selecting the earlier **0.022637** model. The final refit will use one shared-softmax pass over `train_* + val_*`, with the production checkpoint as initialization. ### exec · 2026-09-23T06:07:34.873Z { "code": "text(await tools.exec_command({cmd:\"/opt/task-venv/bin/python /app/solution/train_current_model.py --stage submission --public-dir /task --output /app/solution/submission.parquet --prepared-cache /app/solution/submission_cache.npz --checkpoint-dir /app/solution/final_ckpt --resume-checkpoint /app/solution/production_ckpt/best.pt --epochs 2 --hard-negatives 0 --training-objective shared-softmax --shared-pool-size 4096 --learning-rate 0.0005 --target-weighting discovery-graded --organic-like-target-weight 7 --ordinary-target-weight 0.1 --threads 8 --loss-microbatch-size 4096 > /app/solution/final.json 2> /app/solution/final.log\",yield_time_ms:1000}));\ntext(await tools.clock__curr_time({}));\n" } ### Tool output · 2026-09-23T06:07:36.042Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"a02175","wall_time_seconds":1.000693672,"session_id":85543,"original_token_count":0,"output":""} {"current_time":"2026-09-23 06:07:36 UTC"} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "session_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "root_turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "response_id": "resp_03ec3525bea3ac86016ab36c775c2c87d28c2d3258445138be", "usage": { "input_tokens": 111522, "cached_input_tokens": 111104, "cache_write_input_tokens": 0, "output_tokens": 1291, "reasoning_output_tokens": 1034, "total_tokens": 112813 }, "turn_token_usage": { "input_tokens": 8846612, "cached_input_tokens": 8535168, "cache_write_input_tokens": 0, "output_tokens": 49293, "reasoning_output_tokens": 31664, "total_tokens": 8895905 }, "thread_token_usage": { "input_tokens": 8846612, "cached_input_tokens": 8535168, "cache_write_input_tokens": 0, "output_tokens": 49293, "reasoning_output_tokens": 31664, "total_tokens": 8895905 } } ## Step 112 · agent · 2026-09-23T06:07:58.102Z ### Thinking unavailable — no text recorded ## Step 113 · agent · 2026-09-23T06:08:19.676Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T06:08:42.402Z { "code": "text(await tools.exec_command({cmd:\"tail -n 10 /app/solution/final.log\",max_output_tokens:1500}));\ntext(await tools.apply_patch(\"*** Begin Patch\\n*** Add File: /app/solution/README.md\\n+# Music discovery two-tower\\n+\\n+Selected public NDCG@10: **0.022637**, versus production **0.021280**\\n+(**+6.4% relative**, 4,263 scored users). `submission.parquet` is the\\n+test-cutoff ranking. The final fit starts from the production checkpoint and\\n+refits the selected objective on **train + val** observed history.\\n+\\n+The original 24 uniform / 8 corrected in-batch negatives give each target few\\n+competitive comparisons. Simply adding aggressively mined negatives was harmful:\\n+it changed the negative distribution and strongly suppressed useful popular\\n+tracks. The selected loss instead shares **4,096 uniformly sampled catalog\\n+items** across a loss slice, masks training-prefix positives, active likes and\\n+the target, and uses sampled softmax. The sampled negative denominator is scaled\\n+by catalog size / pool size. Log-sum-exp emphasizes confusable negatives without\\n+a top-k proposal. Shared item encoding and matrix multiplication avoid a large\\n+per-target negative-vector tensor; the softmax training pass took about 141\\n+seconds, versus 265 seconds for the production pass on this CPU.\\n+\\n+The selected recipe also uses the trainer's existing discovery-graded weighting:\\n+ordinary/repeated targets 0.1, novel organic strong listens 1, and qualifying\\n+organic-like targets 7. Every selected adjacent target retains nonzero weight.\\n+The GRU, both towers, 192-dimensional raw dot product, chronological partitions,\\n+cutoffs, candidate masks, and request-time serving architecture are unchanged.\\n+Validation-stage fitting reads only train events. Validation outcomes are used\\n+only for model selection; final refitting observes train and val events.\\n+\\n+| Public experiment | NDCG@10 |\\n+|---|---:|\\n+| Production, one BCE pass | 0.021280 |\\n+| Aggressive mined-negative BCE, one pass | 0.002453 |\\n+| Production + second BCE pass | 0.019878 |\\n+| Production + hard pairwise auxiliary pass | 0.020386 |\\n+| Production + shared-softmax pass | 0.021570 |\\n+| Production + discovery-graded shared-softmax pass, selected | **0.022637** |\\n+| A second graded shared-softmax pass | 0.020540 |\\n+\\n+The supplied environment's Python is `/opt/task-venv/bin/python`. The retained\\n+`production_ckpt/best.pt` was generated from the unmodified production trainer:\\n+\\n+```sh\\n+/opt/task-venv/bin/python /task/current_model/train_current_model.py \\\\\\n+ --stage validation --public-dir /task \\\\\\n+ --output /app/solution/production_validation.parquet \\\\\\n+ --prepared-cache /app/solution/validation_cache.npz \\\\\\n+ --checkpoint-dir /app/solution/production_ckpt\\n+```\\n+\\n+Final submission command (`--epochs 2` means one new pass after checkpoint epoch 1):\\n+\\n+```sh\\n+/opt/task-venv/bin/python /app/solution/train_current_model.py \\\\\\n+ --stage submission --public-dir /task \\\\\\n+ --output /app/solution/submission.parquet \\\\\\n+ --prepared-cache /app/solution/submission_cache.npz \\\\\\n+ --checkpoint-dir /app/solution/final_ckpt \\\\\\n+ --resume-checkpoint /app/solution/production_ckpt/best.pt \\\\\\n+ --epochs 2 --hard-negatives 0 --training-objective shared-softmax \\\\\\n+ --shared-pool-size 4096 --learning-rate 0.0005 \\\\\\n+ --target-weighting discovery-graded --organic-like-target-weight 7 \\\\\\n+ --ordinary-target-weight 0.1 --threads 8 --loss-microbatch-size 4096\\n+/opt/task-venv/bin/python /app/solution/validate_submission.py\\n+```\\n+\\n+`test_shared_softmax.py` verifies equality to exact filtered full-catalog\\n+cross-entropy when the pool covers the catalog, and finite nonzero gradients.\\n+`test_mining.py` checks the experimental miner's filtering, uniqueness,\\n+determinism and top-k selection. `validate_submission.py` independently checks\\n+all required schema, coverage, rank, uniqueness and test-eligibility conditions.\\n+Ranking writes are atomic, preserving the previous valid file until completion.\\n*** End Patch\"));\ntext(await tools.clock__curr_time({}));\n" } ### Tool output · 2026-09-23T06:08:42.557Z Script completed Wall time 0.1 seconds Output: {"chunk_id":"020153","wall_time_seconds":0.00001109,"exit_code":0,"original_token_count":231,"output":"chronological target 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\nloaded organic-like outcomes: rows=314,586 users=7,613\ngrade-3 first-encounter targets=47,773 horizon_days=7\ntarget weighting=discovery-graded min=0.100 mean=0.180 max=7.000\nfull-history false-negative filter: users=9,153 unique_user_items=5,682,910 bytes=34,323,750\nusing a fresh AdamW optimizer after loading /app/solution/production_ckpt/best.pt\ntraining objective changed: filter full-training-history->full-training-history, target weighting organic-strong->discovery-graded, target window (0.1, 128, 16)->(0.1, 128, 16); recomputing the resume checkpoint's validation BCE\nresumed validation-objective training: epoch=1 loss=9.271396\nepoch=2/2 batches=50 targets=196,561 batch_bce=9.084432 running_bce=9.079491 elapsed=26.5s\n"} {} {"current_time":"2026-09-23 06:08:42 UTC"} ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-be919226-63ae-4bf1-8ebe-5a10715ea11d", "process_id": "59598", "command": [ "/bin/sh", "-lc", "tail -n 10 /app/solution/final.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "read", "cmd": "tail -n 10 /app/solution/final.log", "name": "final.log", "path": "/app/solution/final.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "chronological target 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\nloaded organic-like outcomes: rows=314,586 users=7,613\ngrade-3 first-encounter targets=47,773 horizon_days=7\ntarget weighting=discovery-graded min=0.100 mean=0.180 max=7.000\nfull-history false-negative filter: users=9,153 unique_user_items=5,682,910 bytes=34,323,750\nusing a fresh AdamW optimizer after loading /app/solution/production_ckpt/best.pt\ntraining objective changed: filter full-training-history->full-training-history, target weighting organic-strong->discovery-graded, target window (0.1, 128, 16)->(0.1, 128, 16); recomputing the resume checkpoint's validation BCE\nresumed validation-objective training: epoch=1 loss=9.271396\nepoch=2/2 batches=50 targets=196,561 batch_bce=9.084432 running_bce=9.079491 elapsed=26.5s\n", "stderr": "", "aggregated_output": "chronological target 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\nloaded organic-like outcomes: rows=314,586 users=7,613\ngrade-3 first-encounter targets=47,773 horizon_days=7\ntarget weighting=discovery-graded min=0.100 mean=0.180 max=7.000\nfull-history false-negative filter: users=9,153 unique_user_items=5,682,910 bytes=34,323,750\nusing a fresh AdamW optimizer after loading /app/solution/production_ckpt/best.pt\ntraining objective changed: filter full-training-history->full-training-history, target weighting organic-strong->discovery-graded, target window (0.1, 128, 16)->(0.1, 128, 16); recomputing the resume checkpoint's validation BCE\nresumed validation-objective training: epoch=1 loss=9.271396\nepoch=2/2 batches=50 targets=196,561 batch_bce=9.084432 running_bce=9.079491 elapsed=26.5s\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 11090 }, "formatted_output": "chronological target 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\nloaded organic-like outcomes: rows=314,586 users=7,613\ngrade-3 first-encounter targets=47,773 horizon_days=7\ntarget weighting=discovery-graded min=0.100 mean=0.180 max=7.000\nfull-history false-negative filter: users=9,153 unique_user_items=5,682,910 bytes=34,323,750\nusing a fresh AdamW optimizer after loading /app/solution/production_ckpt/best.pt\ntraining objective changed: filter full-training-history->full-training-history, target weighting organic-strong->discovery-graded, target window (0.1, 128, 16)->(0.1, 128, 16); recomputing the resume checkpoint's validation BCE\nresumed validation-objective training: epoch=1 loss=9.271396\nepoch=2/2 batches=50 targets=196,561 batch_bce=9.084432 running_bce=9.079491 elapsed=26.5s\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "session_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "root_turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "response_id": "resp_03ec3525bea3ac86016ab36ca8375487d29720387ba7080925", "usage": { "input_tokens": 112888, "cached_input_tokens": 111360, "cache_write_input_tokens": 0, "output_tokens": 2048, "reasoning_output_tokens": 990, "total_tokens": 114936 }, "turn_token_usage": { "input_tokens": 8959500, "cached_input_tokens": 8646528, "cache_write_input_tokens": 0, "output_tokens": 51341, "reasoning_output_tokens": 32654, "total_tokens": 9010841 }, "thread_token_usage": { "input_tokens": 8959500, "cached_input_tokens": 8646528, "cache_write_input_tokens": 0, "output_tokens": 51341, "reasoning_output_tokens": 32654, "total_tokens": 9010841 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-5866d9db-75df-4004-893c-c03a567e8699", "changes": { "/app/solution/README.md": { "type": "add", "content": "# Music discovery two-tower\n\nSelected public NDCG@10: **0.022637**, versus production **0.021280**\n(**+6.4% relative**, 4,263 scored users). `submission.parquet` is the\ntest-cutoff ranking. The final fit starts from the production checkpoint and\nrefits the selected objective on **train + val** observed history.\n\nThe original 24 uniform / 8 corrected in-batch negatives give each target few\ncompetitive comparisons. Simply adding aggressively mined negatives was harmful:\nit changed the negative distribution and strongly suppressed useful popular\ntracks. The selected loss instead shares **4,096 uniformly sampled catalog\nitems** across a loss slice, masks training-prefix positives, active likes and\nthe target, and uses sampled softmax. The sampled negative denominator is scaled\nby catalog size / pool size. Log-sum-exp emphasizes confusable negatives without\na top-k proposal. Shared item encoding and matrix multiplication avoid a large\nper-target negative-vector tensor; the softmax training pass took about 141\nseconds, versus 265 seconds for the production pass on this CPU.\n\nThe selected recipe also uses the trainer's existing discovery-graded weighting:\nordinary/repeated targets 0.1, novel organic strong listens 1, and qualifying\norganic-like targets 7. Every selected adjacent target retains nonzero weight.\nThe GRU, both towers, 192-dimensional raw dot product, chronological partitions,\ncutoffs, candidate masks, and request-time serving architecture are unchanged.\nValidation-stage fitting reads only train events. Validation outcomes are used\nonly for model selection; final refitting observes train and val events.\n\n| Public experiment | NDCG@10 |\n|---|---:|\n| Production, one BCE pass | 0.021280 |\n| Aggressive mined-negative BCE, one pass | 0.002453 |\n| Production + second BCE pass | 0.019878 |\n| Production + hard pairwise auxiliary pass | 0.020386 |\n| Production + shared-softmax pass | 0.021570 |\n| Production + discovery-graded shared-softmax pass, selected | **0.022637** |\n| A second graded shared-softmax pass | 0.020540 |\n\nThe supplied environment's Python is `/opt/task-venv/bin/python`. The retained\n`production_ckpt/best.pt` was generated from the unmodified production trainer:\n\n```sh\n/opt/task-venv/bin/python /task/current_model/train_current_model.py \\\n --stage validation --public-dir /task \\\n --output /app/solution/production_validation.parquet \\\n --prepared-cache /app/solution/validation_cache.npz \\\n --checkpoint-dir /app/solution/production_ckpt\n```\n\nFinal submission command (`--epochs 2` means one new pass after checkpoint epoch 1):\n\n```sh\n/opt/task-venv/bin/python /app/solution/train_current_model.py \\\n --stage submission --public-dir /task \\\n --output /app/solution/submission.parquet \\\n --prepared-cache /app/solution/submission_cache.npz \\\n --checkpoint-dir /app/solution/final_ckpt \\\n --resume-checkpoint /app/solution/production_ckpt/best.pt \\\n --epochs 2 --hard-negatives 0 --training-objective shared-softmax \\\n --shared-pool-size 4096 --learning-rate 0.0005 \\\n --target-weighting discovery-graded --organic-like-target-weight 7 \\\n --ordinary-target-weight 0.1 --threads 8 --loss-microbatch-size 4096\n/opt/task-venv/bin/python /app/solution/validate_submission.py\n```\n\n`test_shared_softmax.py` verifies equality to exact filtered full-catalog\ncross-entropy when the pool covers the catalog, and finite nonzero gradients.\n`test_mining.py` checks the experimental miner's filtering, uniqueness,\ndeterminism and top-k selection. `validate_submission.py` independently checks\nall required schema, coverage, rank, uniqueness and test-eligibility conditions.\nRanking writes are atomic, preserving the previous valid file until completion.\n" } }, "status": "completed", "stdout": "Success. Updated the following files:\nA /app/solution/README.md\n", "stderr": "" } } ## Step 114 · agent · 2026-09-23T06:09:04.052Z ### Thinking unavailable — no text recorded ## Step 115 · agent · 2026-09-23T06:09:19.149Z ### Thinking unavailable — no text recorded The final refit is training on the combined history: 1,067,606 selected targets and 47,773 qualifying first-encounter organic-like targets. The README now records the diagnosis, public scores, and exact submission command. ### sleep · 2026-09-23T06:09:20.935Z { "duration_ms": 35000 } ### Tool output · 2026-09-23T06:09:56.017Z Wall time: 35.0289 seconds Sleep completed. ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "session_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "root_turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "response_id": "resp_03ec3525bea3ac86016ab36cec8e7487d2bb5846f587eb8d93", "usage": { "input_tokens": 115290, "cached_input_tokens": 112768, "cache_write_input_tokens": 0, "output_tokens": 949, "reasoning_output_tokens": 873, "total_tokens": 116239 }, "turn_token_usage": { "input_tokens": 9074790, "cached_input_tokens": 8759296, "cache_write_input_tokens": 0, "output_tokens": 52290, "reasoning_output_tokens": 33527, "total_tokens": 9127080 }, "thread_token_usage": { "input_tokens": 9074790, "cached_input_tokens": 8759296, "cache_write_input_tokens": 0, "output_tokens": 52290, "reasoning_output_tokens": 33527, "total_tokens": 9127080 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "Extension", "kind": "clock.sleep", "id": "call_Xl6XCeRedj2hJl3u5UOq12qI", "durationMs": 35000 } } ### exec · 2026-09-23T06:09:59.095Z { "code": "text(await tools.exec_command({cmd:\"tail -n 5 /app/solution/final.log\",max_output_tokens:1000}));\ntext(await tools.clock__curr_time({}));\n" } ### Tool output · 2026-09-23T06:09:59.224Z Script completed Wall time 0.1 seconds Output: {"chunk_id":"941c4f","wall_time_seconds":0.00000238,"exit_code":0,"original_token_count":108,"output":"resumed validation-objective training: epoch=1 loss=9.271396\nepoch=2/2 batches=50 targets=196,561 batch_bce=9.084432 running_bce=9.079491 elapsed=26.5s\nepoch=2/2 batches=100 targets=394,078 batch_bce=9.126623 running_bce=9.093719 elapsed=52.9s\nepoch=2/2 batches=150 targets=597,467 batch_bce=8.940991 running_bce=9.057428 elapsed=79.4s\nepoch=2/2 batches=200 targets=790,657 batch_bce=8.855006 running_bce=9.044212 elapsed=105.6s\n"} {"current_time":"2026-09-23 06:09:59 UTC"} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "session_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "root_turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "response_id": "resp_03ec3525bea3ac86016ab36d34376487d2a1a5c330df72b31c", "usage": { "input_tokens": 116262, "cached_input_tokens": 115072, "cache_write_input_tokens": 0, "output_tokens": 47, "reasoning_output_tokens": 0, "total_tokens": 116309 }, "turn_token_usage": { "input_tokens": 9191052, "cached_input_tokens": 8874368, "cache_write_input_tokens": 0, "output_tokens": 52337, "reasoning_output_tokens": 33527, "total_tokens": 9243389 }, "thread_token_usage": { "input_tokens": 9191052, "cached_input_tokens": 8874368, "cache_write_input_tokens": 0, "output_tokens": 52337, "reasoning_output_tokens": 33527, "total_tokens": 9243389 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-09d12026-e9c7-44e0-a226-e84546248d92", "process_id": "74350", "command": [ "/bin/sh", "-lc", "tail -n 5 /app/solution/final.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "read", "cmd": "tail -n 5 /app/solution/final.log", "name": "final.log", "path": "/app/solution/final.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "resumed validation-objective training: epoch=1 loss=9.271396\nepoch=2/2 batches=50 targets=196,561 batch_bce=9.084432 running_bce=9.079491 elapsed=26.5s\nepoch=2/2 batches=100 targets=394,078 batch_bce=9.126623 running_bce=9.093719 elapsed=52.9s\nepoch=2/2 batches=150 targets=597,467 batch_bce=8.940991 running_bce=9.057428 elapsed=79.4s\nepoch=2/2 batches=200 targets=790,657 batch_bce=8.855006 running_bce=9.044212 elapsed=105.6s\n", "stderr": "", "aggregated_output": "resumed validation-objective training: epoch=1 loss=9.271396\nepoch=2/2 batches=50 targets=196,561 batch_bce=9.084432 running_bce=9.079491 elapsed=26.5s\nepoch=2/2 batches=100 targets=394,078 batch_bce=9.126623 running_bce=9.093719 elapsed=52.9s\nepoch=2/2 batches=150 targets=597,467 batch_bce=8.940991 running_bce=9.057428 elapsed=79.4s\nepoch=2/2 batches=200 targets=790,657 batch_bce=8.855006 running_bce=9.044212 elapsed=105.6s\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 2380 }, "formatted_output": "resumed validation-objective training: epoch=1 loss=9.271396\nepoch=2/2 batches=50 targets=196,561 batch_bce=9.084432 running_bce=9.079491 elapsed=26.5s\nepoch=2/2 batches=100 targets=394,078 batch_bce=9.126623 running_bce=9.093719 elapsed=52.9s\nepoch=2/2 batches=150 targets=597,467 batch_bce=8.940991 running_bce=9.057428 elapsed=79.4s\nepoch=2/2 batches=200 targets=790,657 batch_bce=8.855006 running_bce=9.044212 elapsed=105.6s\n" } } ## Step 116 · agent · 2026-09-23T06:10:20.206Z ### Thinking unavailable — no text recorded ## Step 117 · agent · 2026-09-23T06:10:37.211Z ### Thinking unavailable — no text recorded ### sleep · 2026-09-23T06:10:37.345Z { "duration_ms": 35000 } ### Tool output · 2026-09-23T06:11:12.426Z Wall time: 35.0181 seconds Sleep completed. ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "session_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "root_turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "response_id": "resp_03ec3525bea3ac86016ab36d37a4c087d2a9f72ddb091e1a23", "usage": { "input_tokens": 116548, "cached_input_tokens": 116096, "cache_write_input_tokens": 0, "output_tokens": 970, "reasoning_output_tokens": 949, "total_tokens": 117518 }, "turn_token_usage": { "input_tokens": 9307600, "cached_input_tokens": 8990464, "cache_write_input_tokens": 0, "output_tokens": 53307, "reasoning_output_tokens": 34476, "total_tokens": 9360907 }, "thread_token_usage": { "input_tokens": 9307600, "cached_input_tokens": 8990464, "cache_write_input_tokens": 0, "output_tokens": 53307, "reasoning_output_tokens": 34476, "total_tokens": 9360907 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "Extension", "kind": "clock.sleep", "id": "call_w9nJEWaMeEfZDDhQFJtpOXSR", "durationMs": 35000 } } ### exec · 2026-09-23T06:11:18.433Z { "code": "text(await tools.exec_command({cmd:\"tail -n 7 /app/solution/final.log\",max_output_tokens:1000}));\ntext(await tools.clock__curr_time({}));\n" } ### Tool output · 2026-09-23T06:11:18.585Z Script completed Wall time 0.1 seconds Output: {"chunk_id":"c9740f","wall_time_seconds":0.00000649,"exit_code":0,"original_token_count":101,"output":"saved epoch checkpoint: /app/solution/final_ckpt/epoch-02.pt\nepoch=2/2 train_loss=8.99968 validation_loss=8.99810 pair_accuracy=0.841 train_targets=1,067,606 validation_targets=131,997 train_seconds=143.4 validation_seconds=12.7\nrestored best validation-objective checkpoint: epoch=2 loss=8.998097\nencoded users=1,600/8,456\nencoded users=3,200/8,456\nencoded users=4,800/8,456\nencoded users=6,400/8,456\n"} {"current_time":"2026-09-23 06:11:18 UTC"} ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-60a49b85-4aa5-4325-aa35-dd80927cfe61", "process_id": "55468", "command": [ "/bin/sh", "-lc", "tail -n 7 /app/solution/final.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "read", "cmd": "tail -n 7 /app/solution/final.log", "name": "final.log", "path": "/app/solution/final.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "saved epoch checkpoint: /app/solution/final_ckpt/epoch-02.pt\nepoch=2/2 train_loss=8.99968 validation_loss=8.99810 pair_accuracy=0.841 train_targets=1,067,606 validation_targets=131,997 train_seconds=143.4 validation_seconds=12.7\nrestored best validation-objective checkpoint: epoch=2 loss=8.998097\nencoded users=1,600/8,456\nencoded users=3,200/8,456\nencoded users=4,800/8,456\nencoded users=6,400/8,456\n", "stderr": "", "aggregated_output": "saved epoch checkpoint: /app/solution/final_ckpt/epoch-02.pt\nepoch=2/2 train_loss=8.99968 validation_loss=8.99810 pair_accuracy=0.841 train_targets=1,067,606 validation_targets=131,997 train_seconds=143.4 validation_seconds=12.7\nrestored best validation-objective checkpoint: epoch=2 loss=8.998097\nencoded users=1,600/8,456\nencoded users=3,200/8,456\nencoded users=4,800/8,456\nencoded users=6,400/8,456\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 6490 }, "formatted_output": "saved epoch checkpoint: /app/solution/final_ckpt/epoch-02.pt\nepoch=2/2 train_loss=8.99968 validation_loss=8.99810 pair_accuracy=0.841 train_targets=1,067,606 validation_targets=131,997 train_seconds=143.4 validation_seconds=12.7\nrestored best validation-objective checkpoint: epoch=2 loss=8.998097\nencoded users=1,600/8,456\nencoded users=3,200/8,456\nencoded users=4,800/8,456\nencoded users=6,400/8,456\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "session_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "root_turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "response_id": "resp_03ec3525bea3ac86016ab36d80b53087d2877cd4f4cfed5a90", "usage": { "input_tokens": 117541, "cached_input_tokens": 116352, "cache_write_input_tokens": 0, "output_tokens": 47, "reasoning_output_tokens": 0, "total_tokens": 117588 }, "turn_token_usage": { "input_tokens": 9425141, "cached_input_tokens": 9106816, "cache_write_input_tokens": 0, "output_tokens": 53354, "reasoning_output_tokens": 34476, "total_tokens": 9478495 }, "thread_token_usage": { "input_tokens": 9425141, "cached_input_tokens": 9106816, "cache_write_input_tokens": 0, "output_tokens": 53354, "reasoning_output_tokens": 34476, "total_tokens": 9478495 } } ## Step 118 · agent · 2026-09-23T06:11:39.009Z ### Thinking unavailable — no text recorded The refit finished successfully and selected the newly trained checkpoint. It is now ranking all 8,456 users against the test-cutoff candidate sets. Next I’ll check the final file’s schema and eligibility. ### sleep · 2026-09-23T06:11:40.299Z { "duration_ms": 30000 } ### Tool output · 2026-09-23T06:12:10.322Z Wall time: 30.0036 seconds Sleep completed. ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "session_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "root_turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "response_id": "resp_03ec3525bea3ac86016ab36d86d01887d2b146f544900dd3a4", "usage": { "input_tokens": 117795, "cached_input_tokens": 117376, "cache_write_input_tokens": 0, "output_tokens": 586, "reasoning_output_tokens": 516, "total_tokens": 118381 }, "turn_token_usage": { "input_tokens": 9542936, "cached_input_tokens": 9224192, "cache_write_input_tokens": 0, "output_tokens": 53940, "reasoning_output_tokens": 34992, "total_tokens": 9596876 }, "thread_token_usage": { "input_tokens": 9542936, "cached_input_tokens": 9224192, "cache_write_input_tokens": 0, "output_tokens": 53940, "reasoning_output_tokens": 34992, "total_tokens": 9596876 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-2e5e15c6-5d98-428f-9de1-24148d8720a3", "process_id": "85543", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python /app/solution/train_current_model.py --stage submission --public-dir /task --output /app/solution/submission.parquet --prepared-cache /app/solution/submission_cache.npz --checkpoint-dir /app/solution/final_ckpt --resume-checkpoint /app/solution/production_ckpt/best.pt --epochs 2 --hard-negatives 0 --training-objective shared-softmax --shared-pool-size 4096 --learning-rate 0.0005 --target-weighting discovery-graded --organic-like-target-weight 7 --ordinary-target-weight 0.1 --threads 8 --loss-microbatch-size 4096 > /app/solution/final.json 2> /app/solution/final.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 --output /app/solution/submission.parquet --prepared-cache /app/solution/submission_cache.npz --checkpoint-dir /app/solution/final_ckpt --resume-checkpoint /app/solution/production_ckpt/best.pt --epochs 2 --hard-negatives 0 --training-objective shared-softmax --shared-pool-size 4096 --learning-rate 0.0005 --target-weighting discovery-graded --organic-like-target-weight 7 --ordinary-target-weight 0.1 --threads 8 --loss-microbatch-size 4096 > /app/solution/final.json 2> /app/solution/final.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 272, "nanos": 741035955 }, "formatted_output": "" } } ### Harness context { "type": "codex_item_completed", "item": { "type": "Extension", "kind": "clock.sleep", "id": "call_vM0ruQbATgnJAQspIPrL7yzY", "durationMs": 30000 } } ### exec · 2026-09-23T06:12:16.743Z { "code": "text(await tools.exec_command({cmd:\"tail -n 5 /app/solution/final.log && ls -lh /app/solution/submission.parquet && /opt/task-venv/bin/python /app/solution/validate_submission.py > /app/solution/submission_validation.txt && cat /app/solution/submission_validation.txt\",yield_time_ms:1000,max_output_tokens:1500}));\ntext(await tools.clock__curr_time({}));\n" } ### Tool output · 2026-09-23T06:12:17.913Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"f26dbe","wall_time_seconds":1.000972459,"session_id":11216,"original_token_count":51,"output":"ranked 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-rw-r--r-- 1 agent agent 1.3M Sep 23 06:12 /app/solution/submission.parquet\n"} {"current_time":"2026-09-23 06:12:17 UTC"} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "session_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "root_turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "response_id": "resp_03ec3525bea3ac86016ab36dba7ad087d2928d31a0cd736a1f", "usage": { "input_tokens": 118404, "cached_input_tokens": 117632, "cache_write_input_tokens": 0, "output_tokens": 101, "reasoning_output_tokens": 0, "total_tokens": 118505 }, "turn_token_usage": { "input_tokens": 9661340, "cached_input_tokens": 9341824, "cache_write_input_tokens": 0, "output_tokens": 54041, "reasoning_output_tokens": 34992, "total_tokens": 9715381 }, "thread_token_usage": { "input_tokens": 9661340, "cached_input_tokens": 9341824, "cache_write_input_tokens": 0, "output_tokens": 54041, "reasoning_output_tokens": 34992, "total_tokens": 9715381 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-24306b99-0178-4388-9951-3f536745fc5c", "process_id": "11216", "command": [ "/bin/sh", "-lc", "tail -n 5 /app/solution/final.log && ls -lh /app/solution/submission.parquet && /opt/task-venv/bin/python /app/solution/validate_submission.py > /app/solution/submission_validation.txt && cat /app/solution/submission_validation.txt" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -n 5 /app/solution/final.log && ls -lh /app/solution/submission.parquet && /opt/task-venv/bin/python /app/solution/validate_submission.py > /app/solution/submission_validation.txt && cat /app/solution/submission_validation.txt" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "Validated 845,600 rows: exactly 100 distinct eligible ranked tracks for 8,456 users.\n", "stderr": "", "aggregated_output": "Validated 845,600 rows: exactly 100 distinct eligible ranked tracks for 8,456 users.\n", "exit_code": 0, "duration": { "secs": 1, "nanos": 707099086 }, "formatted_output": "Validated 845,600 rows: exactly 100 distinct eligible ranked tracks for 8,456 users.\n" } } ### exec · 2026-09-23T06:12:29.947Z { "code": "text(await tools.write_stdin({session_id:85543,chars:\"\",yield_time_ms:1000,max_output_tokens:1000}));\ntext(await tools.write_stdin({session_id:11216,chars:\"\",yield_time_ms:1000,max_output_tokens:1000}));\ntext(await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\nimport json\\nfrom pathlib import Path\\np=Path('/app/solution')\\na=json.loads((p/'final.json').read_text())\\nprint(json.dumps({k:a[k] for k in ['stage','rows','users','runtime_seconds','embedding_diagnostics']},indent=2))\\nprint('Selected checkpoint epoch:', min(a['training_history'],key=lambda r:r['validation_loss'])['epoch'])\\nprint('Local implementation:',(p/'train_current_model.py').is_file(),(p/'two_tower_model.py').is_file())\\nprint('Regular submission:',(p/'submission.parquet').is_file() and not (p/'submission.parquet').is_symlink())\\nPY\",yield_time_ms:1000,max_output_tokens:1500}));\n" } ### Tool output · 2026-09-23T06:12:30.086Z Script completed Wall time 0.1 seconds Output: {"chunk_id":"8245d4","wall_time_seconds":0.00000164,"exit_code":0,"original_token_count":0,"output":""} {"chunk_id":"06754e","wall_time_seconds":0.00000154,"exit_code":0,"original_token_count":22,"output":"Validated 845,600 rows: exactly 100 distinct eligible ranked tracks for 8,456 users.\n"} {"chunk_id":"7c973e","wall_time_seconds":0.00000249,"exit_code":0,"original_token_count":96,"output":"{\n \"stage\": \"submission\",\n \"rows\": 845600,\n \"users\": 8456,\n \"runtime_seconds\": 268.593,\n \"embedding_diagnostics\": {\n \"query_norm_mean\": 1.8813598155975342,\n \"query_norm_std\": 0.7769436240196228,\n \"item_norm_mean\": 2.642338991165161,\n \"item_norm_std\": 1.0539997816085815\n }\n}\nSelected checkpoint epoch: 2.0\nLocal implementation: True True\nRegular submission: True\n"} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "session_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "root_turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "response_id": "resp_03ec3525bea3ac86016ab36dc2216887d28da55d0c616b42c8", "usage": { "input_tokens": 118671, "cached_input_tokens": 118272, "cache_write_input_tokens": 0, "output_tokens": 253, "reasoning_output_tokens": 0, "total_tokens": 118924 }, "turn_token_usage": { "input_tokens": 9780011, "cached_input_tokens": 9460096, "cache_write_input_tokens": 0, "output_tokens": 54294, "reasoning_output_tokens": 34992, "total_tokens": 9834305 }, "thread_token_usage": { "input_tokens": 9780011, "cached_input_tokens": 9460096, "cache_write_input_tokens": 0, "output_tokens": 54294, "reasoning_output_tokens": 34992, "total_tokens": 9834305 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-9fa56d2a-ae95-42e9-bdb0-fda96ad51c3e", "process_id": "85302", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\nimport json\nfrom pathlib import Path\np=Path('/app/solution')\na=json.loads((p/'final.json').read_text())\nprint(json.dumps({k:a[k] for k in ['stage','rows','users','runtime_seconds','embedding_diagnostics']},indent=2))\nprint('Selected checkpoint epoch:', min(a['training_history'],key=lambda r:r['validation_loss'])['epoch'])\nprint('Local implementation:',(p/'train_current_model.py').is_file(),(p/'two_tower_model.py').is_file())\nprint('Regular submission:',(p/'submission.parquet').is_file() and not (p/'submission.parquet').is_symlink())\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport json\nfrom pathlib import Path\np=Path('/app/solution')\na=json.loads((p/'final.json').read_text())\nprint(json.dumps({k:a[k] for k in ['stage','rows','users','runtime_seconds','embedding_diagnostics']},indent=2))\nprint('Selected checkpoint epoch:', min(a['training_history'],key=lambda r:r['validation_loss'])['epoch'])\nprint('Local implementation:',(p/'train_current_model.py').is_file(),(p/'two_tower_model.py').is_file())\nprint('Regular submission:',(p/'submission.parquet').is_file() and not (p/'submission.parquet').is_symlink())\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{\n \"stage\": \"submission\",\n \"rows\": 845600,\n \"users\": 8456,\n \"runtime_seconds\": 268.593,\n \"embedding_diagnostics\": {\n \"query_norm_mean\": 1.8813598155975342,\n \"query_norm_std\": 0.7769436240196228,\n \"item_norm_mean\": 2.642338991165161,\n \"item_norm_std\": 1.0539997816085815\n }\n}\nSelected checkpoint epoch: 2.0\nLocal implementation: True True\nRegular submission: True\n", "stderr": "", "aggregated_output": "{\n \"stage\": \"submission\",\n \"rows\": 845600,\n \"users\": 8456,\n \"runtime_seconds\": 268.593,\n \"embedding_diagnostics\": {\n \"query_norm_mean\": 1.8813598155975342,\n \"query_norm_std\": 0.7769436240196228,\n \"item_norm_mean\": 2.642338991165161,\n \"item_norm_std\": 1.0539997816085815\n }\n}\nSelected checkpoint epoch: 2.0\nLocal implementation: True True\nRegular submission: True\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 2490 }, "formatted_output": "{\n \"stage\": \"submission\",\n \"rows\": 845600,\n \"users\": 8456,\n \"runtime_seconds\": 268.593,\n \"embedding_diagnostics\": {\n \"query_norm_mean\": 1.8813598155975342,\n \"query_norm_std\": 0.7769436240196228,\n \"item_norm_mean\": 2.642338991165161,\n \"item_norm_std\": 1.0539997816085815\n }\n}\nSelected checkpoint epoch: 2.0\nLocal implementation: True True\nRegular submission: True\n" } } ## Step 119 · agent · 2026-09-23T06:12:38.455Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T06:12:42.333Z { "code": "text(await tools.apply_patch(\"*** Begin Patch\\n*** Update File: /app/solution/README.md\\n@@\\n test-cutoff ranking. The final fit starts from the production checkpoint and\\n refits the selected objective on **train + val** observed history.\\n+The completed refit and ranking took 268.6 seconds. Independent validation\\n+confirmed **845,600 rows**, exactly **100 distinct eligible tracks for each of\\n+8,456 target users**, complete ranks 1–100, integer fields, and a regular file.\\n*** End Patch\"));\n" } ### Tool output · 2026-09-23T06:12:42.466Z Script completed Wall time 0.0 seconds Output: {} ### Harness context { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-25cdf5f9-f319-4828-a6bc-0f68f0f9f05e", "changes": { "/app/solution/README.md": { "type": "update", "unified_diff": "@@ -6,2 +6,5 @@\n refits the selected objective on **train + val** observed history.\n+The completed refit and ranking took 268.6 seconds. Independent validation\n+confirmed **845,600 rows**, exactly **100 distinct eligible tracks for each of\n+8,456 target users**, complete ranks 1–100, integer fields, and a regular file.\n \n", "move_path": null } }, "status": "completed", "stdout": "Success. Updated the following files:\nM /app/solution/README.md\n", "stderr": "" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "session_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "root_turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "response_id": "resp_03ec3525bea3ac86016ab36dce74e887d2bd9344d7a4ea4e3b", "usage": { "input_tokens": 119206, "cached_input_tokens": 118528, "cache_write_input_tokens": 0, "output_tokens": 298, "reasoning_output_tokens": 170, "total_tokens": 119504 }, "turn_token_usage": { "input_tokens": 9899217, "cached_input_tokens": 9578624, "cache_write_input_tokens": 0, "output_tokens": 54592, "reasoning_output_tokens": 35162, "total_tokens": 9953809 }, "thread_token_usage": { "input_tokens": 9899217, "cached_input_tokens": 9578624, "cache_write_input_tokens": 0, "output_tokens": 54592, "reasoning_output_tokens": 35162, "total_tokens": 9953809 } } ## Step 120 · agent · 2026-09-23T06:12:47.302Z Created [submission.parquet](/app/solution/submission.parquet). Public NDCG@10 improved **0.021280 → 0.022637 (+6.4%)** using shared-pool sampled softmax and discovery-graded weights. Refit on train + val. Validated exactly 100 distinct eligible tracks for all 8,456 users. [Implementation](/app/solution/train_current_model.py) · [Experiment record](/app/solution/README.md) ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "session_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae", "root_turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90", "response_id": "resp_03ec3525bea3ac86016ab36ddad69887d29c8b1faa56d2fc1c", "usage": { "input_tokens": 119528, "cached_input_tokens": 119040, "cache_write_input_tokens": 0, "output_tokens": 104, "reasoning_output_tokens": 0, "total_tokens": 119632 }, "turn_token_usage": { "input_tokens": 10018745, "cached_input_tokens": 9697664, "cache_write_input_tokens": 0, "output_tokens": 54696, "reasoning_output_tokens": 35162, "total_tokens": 10073441 }, "thread_token_usage": { "input_tokens": 10018745, "cached_input_tokens": 9697664, "cache_write_input_tokens": 0, "output_tokens": 54696, "reasoning_output_tokens": 35162, "total_tokens": 10073441 } }