yambda-discovery-ranking-two-tower-case-1 / case1-astra-high-cli-0-153-4-r03-20260923T050949Z Recorded model: gpt-6-astra ## Step 1 · system · 2026-09-23T05:19:49.335Z 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 · 2026-09-23T05:19:50.632Z ## 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 · 2026-09-23T05:19:50.632Z 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 · 2026-09-23T05:19:50.632Z 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 · 2026-09-23T05:19:50.632Z 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-b24d-7903-b491-cb991cb6dff5", "root_turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "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 · 2026-09-23T05:19:50.650Z # 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 · 2026-09-23T05:19:53.150Z I’ll inspect the trainer’s negative sampling, reproduce the public baseline, and test a targeted objective change before refitting for the final submission. ### exec · 2026-09-23T05:19:54.388Z { "code": "text(await tools.exec_command({cmd:\"ls -la /app && ls -la /task/current_model && cat /task/current_model/train_current_model.py\",max_output_tokens:22000}));\n" } ### Tool output · 2026-09-23T05:19:54.462Z Script completed Wall time 0.1 seconds Output: Warning: truncated output (original token count: 20869) Total output lines: 1 {"chunk_id":"a02bb2","wall_time_seconds":0.000003261,"exit_code":0,"original_token_count":20109,"output":"total 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 b…10869 tokens truncated… 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"} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "session_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "root_turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "response_id": "resp_021598e9bafc9da7016ab36176d1dc87d29c9f4a762d73e2af", "usage": { "input_tokens": 15969, "cached_input_tokens": 12160, "cache_write_input_tokens": 0, "output_tokens": 84, "reasoning_output_tokens": 0, "total_tokens": 16053 }, "turn_token_usage": { "input_tokens": 15969, "cached_input_tokens": 12160, "cache_write_input_tokens": 0, "output_tokens": 84, "reasoning_output_tokens": 0, "total_tokens": 16053 }, "thread_token_usage": { "input_tokens": 15969, "cached_input_tokens": 12160, "cache_write_input_tokens": 0, "output_tokens": 84, "reasoning_output_tokens": 0, "total_tokens": 16053 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-bdd6dac1-5de2-4592-9e18-169ff67dbe1f", "process_id": "26680", "command": [ "/bin/sh", "-lc", "ls -la /app && ls -la /task/current_model && cat /task/current_model/train_current_model.py" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "list_files", "cmd": "ls -la /app", "path": "app" }, { "type": "list_files", "cmd": "ls -la /task/current_model", "path": "current_model" }, { "type": "read", "cmd": "cat /task/current_model/train_current_model.py", "name": "train_current_model.py", "path": "/task/current_model/train_current_model.py" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "total 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": "total 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": 3261 }, "formatted_output": "Warning: truncated output (original token count: 20109)\nTotal output lines: 2173\n\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 minl…10109 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 · 2026-09-23T05:19:58.971Z { "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|loss' /task/current_model/train_current_model.py\",max_output_tokens:8000}));\ntext(await tools.exec_command({cmd:\"ls -lh /task/data && cat /task/evaluate_public.py && cat /task/current_model/two_tower_model.py\",max_output_tokens:14000}));\n" } ### Tool output · 2026-09-23T05:19:59.054Z Script completed Wall time 0.1 seconds Output: {"chunk_id":"bcb439","wall_time_seconds":0.0000035,"exit_code":0,"original_token_count":2136,"output":"6:loss. The fast default retains each user's most recent 128 training targets and\n9:computes all of its timestep losses and carries the detached GRU hidden state\n14:and 24 uniform plus eight propensity-corrected unique in-batch negatives. Every\n41:class Config:\n55: loss_microbatch_size: int = 65_536\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,\n1132: \"loss_sum\": 0.0,\n1134: \"negative_sum\": 0.0,\n1138: for begin in range(0, target_count, config.loss_microbatch_size):\n1139: end = min(begin + config.loss_microbatch_size, target_count)\n1162: positive_vectors, negative_vectors = candidate_vectors(\n1165: negatives[begin:end],\n1168: negative_logits = torch.einsum(\n1169: \"bd,bkd->bk\", query_vectors, negative_vectors\n1171: positive_loss = F.softplus(-positive_logits)\n1172: negative_loss = corrected_negative_loss(\n1173: negative_logits, negative_weights[begin:end], config\n1178: loss_sum = (\n1179: selected_weights * (positive_loss + negative_loss) / 2.0\n1181: if not bool(torch.isfinite(loss_sum)):\n1182: raise RuntimeError(\"dense next-item training produced non-finite loss\")\n1184: (loss_sum / total_weight).backward()\n1188: statistics[\"loss_sum\"] += float(loss_sum.detach().cpu())\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\n1228: total_loss = 0.0\n1232: total_negative = 0.0\n1293: negatives, negative_weights = draw_training_negatives(\n1314: negatives,\n1315: negative_weights,\n1327: total_loss += statistics[\"loss_sum\"]\n1331: total_negative += statistics[\"negative_sum\"]\n1337: batch_bce = statistics[\"loss_sum\"] / max(\n1340: running_bce = total_loss / max(total_weight, 1.0e-12)\n1351: \"loss\": total_loss / max(total_weight, 1.0e-12),\n1353: \"negative_logit\": total_negative / max(total_targets * negative_count, 1),\n1355: / max(total_targets * negative_count, 1),\n1364:def evaluate_validation_bce(\n1388: total_loss = 0.0\n1449: negatives, negative_weights = draw_training_negatives(\n1469: negatives,\n1470: negative_weights,\n1475: total_loss += statistics[\"loss_sum\"]\n1481: \"validation_loss\": total_loss / max(total_weight, 1.0e-12),\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\"\n1571: best_loss = float(\"inf\")\n1601: \"negative_filter_scope\", \"last-100\"\n1629: stored_filter_scope != config.negative_filter_scope\n1657: prior.pop(\"validation_loss\", None)\n1660: f\"{stored_filter_scope}->{config.negative_filter_scope}, \"\n1666: if \"validation_loss\" not in prior:\n1682: best_loss = float(prior[\"validation_loss\"])\n1687: f\"loss={best_loss:.6f}\"\n1731: diagnostics[\"validation_loss\"]\n1732: < best_loss - config.early_stopping_min_delta\n1735: best_loss = diagnostics[\"validation_loss\"]\n1754: f\"epoch={epoch}/{config.epochs} train_loss={diagnostics['loss']:.5f} \"\n1755: f\"validation_loss={diagnostics['validation_loss']:.5f} \"\n1766: f\"best_epoch={best_epoch} best_validation_loss={best_loss:.6f}\"\n1776: f\"loss={best_loss:.6f}\"\n1782:def inference_queries(\n1861:def rank_candidates(\n1912:def write_ranking(frame: pd.DataFrame, output: Path) -> None:\n1922:def main() -> int:\n1939: \"--loss-microbatch-size\",\n1941: default=Config.loss_microbatch_size,\n1944: \"--negative-filter-scope\",\n1946: default=Config.negative_filter_scope,\n2012: or args.loss_microbatch_size < 1\n2033: loss_microbatch_size=args.loss_microbatch_size,\n2034: negative_filter_scope=args.negative_filter_scope,\n2126: \"sequence_encoder\": \"one-layer-GRU-dense-timestep-loss\",\n2129: \"loss_execution\": \"gradient-accumulated-microbatches\",\n2136: \"negative_sampling\": (\n2139: \"false_negative_filter\": config.negative_filter_scope,\n"} {"chunk_id":"474a14","wall_time_seconds":0.00000309,"exit_code":0,"original_token_count":5705,"output":"total 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 05:15 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 05:15 validation_preference_state.parquet\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\"\"\"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"} ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-438651cf-5aab-41e3-acee-226647932b8b", "process_id": "55200", "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|loss' /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|loss' /task/current_model/train_current_model.py" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "6:loss. The fast default retains each user's most recent 128 training targets and\n9:computes all of its timestep losses and carries the detached GRU hidden state\n14:and 24 uniform plus eight propensity-corrected unique in-batch negatives. Every\n41:class Config:\n55: loss_microbatch_size: int = 65_536\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,\n1132: \"loss_sum\": 0.0,\n1134: \"negative_sum\": 0.0,\n1138: for begin in range(0, target_count, config.loss_microbatch_size):\n1139: end = min(begin + config.loss_microbatch_size, target_count)\n1162: positive_vectors, negative_vectors = candidate_vectors(\n1165: negatives[begin:end],\n1168: negative_logits = torch.einsum(\n1169: \"bd,bkd->bk\", query_vectors, negative_vectors\n1171: positive_loss = F.softplus(-positive_logits)\n1172: negative_loss = corrected_negative_loss(\n1173: negative_logits, negative_weights[begin:end], config\n1178: loss_sum = (\n1179: selected_weights * (positive_loss + negative_loss) / 2.0\n1181: if not bool(torch.isfinite(loss_sum)):\n1182: raise RuntimeError(\"dense next-item training produced non-finite loss\")\n1184: (loss_sum / total_weight).backward()\n1188: statistics[\"loss_sum\"] += float(loss_sum.detach().cpu())\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\n1228: total_loss = 0.0\n1232: total_negative = 0.0\n1293: negatives, negative_weights = draw_training_negatives(\n1314: negatives,\n1315: negative_weights,\n1327: total_loss += statistics[\"loss_sum\"]\n1331: total_negative += statistics[\"negative_sum\"]\n1337: batch_bce = statistics[\"loss_sum\"] / max(\n1340: running_bce = total_loss / max(total_weight, 1.0e-12)\n1351: \"loss\": total_loss / max(total_weight, 1.0e-12),\n1353: \"negative_logit\": total_negative / max(total_targets * negative_count, 1),\n1355: / max(total_targets * negative_count, 1),\n1364:def evaluate_validation_bce(\n1388: total_loss = 0.0\n1449: negatives, negative_weights = draw_training_negatives(\n1469: negatives,\n1470: negative_weights,\n1475: total_loss += statistics[\"loss_sum\"]\n1481: \"validation_loss\": total_loss / max(total_weight, 1.0e-12),\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\"\n1571: best_loss = float(\"inf\")\n1601: \"negative_filter_scope\", \"last-100\"\n1629: stored_filter_scope != config.negative_filter_scope\n1657: prior.pop(\"validation_loss\", None)\n1660: f\"{stored_filter_scope}->{config.negative_filter_scope}, \"\n1666: if \"validation_loss\" not in prior:\n1682: best_loss = float(prior[\"validation_loss\"])\n1687: f\"loss={best_loss:.6f}\"\n1731: diagnostics[\"validation_loss\"]\n1732: < best_loss - config.early_stopping_min_delta\n1735: best_loss = diagnostics[\"validation_loss\"]\n1754: f\"epoch={epoch}/{config.epochs} train_loss={diagnostics['loss']:.5f} \"\n1755: f\"validation_loss={diagnostics['validation_loss']:.5f} \"\n1766: f\"best_epoch={best_epoch} best_validation_loss={best_loss:.6f}\"\n1776: f\"loss={best_loss:.6f}\"\n1782:def inference_queries(\n1861:def rank_candidates(\n1912:def write_ranking(frame: pd.DataFrame, output: Path) -> None:\n1922:def main() -> int:\n1939: \"--loss-microbatch-size\",\n1941: default=Config.loss_microbatch_size,\n1944: \"--negative-filter-scope\",\n1946: default=Config.negative_filter_scope,\n2012: or args.loss_microbatch_size < 1\n2033: loss_microbatch_size=args.loss_microbatch_size,\n2034: negative_filter_scope=args.negative_filter_scope,\n2126: \"sequence_encoder\": \"one-layer-GRU-dense-timestep-loss\",\n2129: \"loss_execution\": \"gradient-accumulated-microbatches\",\n2136: \"negative_sampling\": (\n2139: \"false_negative_filter\": config.negative_filter_scope,\n", "stderr": "", "aggregated_output": "6:loss. The fast default retains each user's most recent 128 training targets and\n9:computes all of its timestep losses and carries the detached GRU hidden state\n14:and 24 uniform plus eight propensity-corrected unique in-batch negatives. Every\n41:class Config:\n55: loss_microbatch_size: int = 65_536\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,\n1132: \"loss_sum\": 0.0,\n1134: \"negative_sum\": 0.0,\n1138: for begin in range(0, target_count, config.loss_microbatch_size):\n1139: end = min(begin + config.loss_microbatch_size, target_count)\n1162: positive_vectors, negative_vectors = candidate_vectors(\n1165: negatives[begin:end],\n1168: negative_logits = torch.einsum(\n1169: \"bd,bkd->bk\", query_vectors, negative_vectors\n1171: positive_loss = F.softplus(-positive_logits)\n1172: negative_loss = corrected_negative_loss(\n1173: negative_logits, negative_weights[begin:end], config\n1178: loss_sum = (\n1179: selected_weights * (positive_loss + negative_loss) / 2.0\n1181: if not bool(torch.isfinite(loss_sum)):\n1182: raise RuntimeError(\"dense next-item training produced non-finite loss\")\n1184: (loss_sum / total_weight).backward()\n1188: statistics[\"loss_sum\"] += float(loss_sum.detach().cpu())\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\n1228: total_loss = 0.0\n1232: total_negative = 0.0\n1293: negatives, negative_weights = draw_training_negatives(\n1314: negatives,\n1315: negative_weights,\n1327: total_loss += statistics[\"loss_sum\"]\n1331: total_negative += statistics[\"negative_sum\"]\n1337: batch_bce = statistics[\"loss_sum\"] / max(\n1340: running_bce = total_loss / max(total_weight, 1.0e-12)\n1351: \"loss\": total_loss / max(total_weight, 1.0e-12),\n1353: \"negative_logit\": total_negative / max(total_targets * negative_count, 1),\n1355: / max(total_targets * negative_count, 1),\n1364:def evaluate_validation_bce(\n1388: total_loss = 0.0\n1449: negatives, negative_weights = draw_training_negatives(\n1469: negatives,\n1470: negative_weights,\n1475: total_loss += statistics[\"loss_sum\"]\n1481: \"validation_loss\": total_loss / max(total_weight, 1.0e-12),\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\"\n1571: best_loss = float(\"inf\")\n1601: \"negative_filter_scope\", \"last-100\"\n1629: stored_filter_scope != config.negative_filter_scope\n1657: prior.pop(\"validation_loss\", None)\n1660: f\"{stored_filter_scope}->{config.negative_filter_scope}, \"\n1666: if \"validation_loss\" not in prior:\n1682: best_loss = float(prior[\"validation_loss\"])\n1687: f\"loss={best_loss:.6f}\"\n1731: diagnostics[\"validation_loss\"]\n1732: < best_loss - config.early_stopping_min_delta\n1735: best_loss = diagnostics[\"validation_loss\"]\n1754: f\"epoch={epoch}/{config.epochs} train_loss={diagnostics['loss']:.5f} \"\n1755: f\"validation_loss={diagnostics['validation_loss']:.5f} \"\n1766: f\"best_epoch={best_epoch} best_validation_loss={best_loss:.6f}\"\n1776: f\"loss={best_loss:.6f}\"\n1782:def inference_queries(\n1861:def rank_candidates(\n1912:def write_ranking(frame: pd.DataFrame, output: Path) -> None:\n1922:def main() -> int:\n1939: \"--loss-microbatch-size\",\n1941: default=Config.loss_microbatch_size,\n1944: \"--negative-filter-scope\",\n1946: default=Config.negative_filter_scope,\n2012: or args.loss_microbatch_size < 1\n2033: loss_microbatch_size=args.loss_microbatch_size,\n2034: negative_filter_scope=args.negative_filter_scope,\n2126: \"sequence_encoder\": \"one-layer-GRU-dense-timestep-loss\",\n2129: \"loss_execution\": \"gradient-accumulated-microbatches\",\n2136: \"negative_sampling\": (\n2139: \"false_negative_filter\": config.negative_filter_scope,\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 3500 }, "formatted_output": "6:loss. The fast default retains each user's most recent 128 training targets and\n9:computes all of its timestep losses and carries the detached GRU hidden state\n14:and 24 uniform plus eight propensity-corrected unique in-batch negatives. Every\n41:class Config:\n55: loss_microbatch_size: int = 65_536\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,\n1132: \"loss_sum\": 0.0,\n1134: \"negative_sum\": 0.0,\n1138: for begin in range(0, target_count, config.loss_microbatch_size):\n1139: end = min(begin + config.loss_microbatch_size, target_count)\n1162: positive_vectors, negative_vectors = candidate_vectors(\n1165: negatives[begin:end],\n1168: negative_logits = torch.einsum(\n1169: \"bd,bkd->bk\", query_vectors, negative_vectors\n1171: positive_loss = F.softplus(-positive_logits)\n1172: negative_loss = corrected_negative_loss(\n1173: negative_logits, negative_weights[begin:end], config\n1178: loss_sum = (\n1179: selected_weights * (positive_loss + negative_loss) / 2.0\n1181: if not bool(torch.isfinite(loss_sum)):\n1182: raise RuntimeError(\"dense next-item training produced non-finite loss\")\n1184: (loss_sum / total_weight).backward()\n1188: statistics[\"loss_sum\"] += float(loss_sum.detach().cpu())\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\n1228: total_loss = 0.0\n1232: total_negative = 0.0\n1293: negatives, negative_weights = draw_training_negatives(\n1314: negatives,\n1315: negative_weights,\n1327: total_loss += statistics[\"loss_sum\"]\n1331: total_negative += statistics[\"negative_sum\"]\n1337: batch_bce = statistics[\"loss_sum\"] / max(\n1340: running_bce = total_loss / max(total_weight, 1.0e-12)\n1351: \"loss\": total_loss / max(total_weight, 1.0e-12),\n1353: \"negative_logit\": total_negative / max(total_targets * negative_count, 1),\n1355: / max(total_targets * negative_count, 1),\n1364:def evaluate_validation_bce(\n1388: total_loss = 0.0\n1449: negatives, negative_weights = draw_training_negatives(\n1469: negatives,\n1470: negative_weights,\n1475: total_loss += statistics[\"loss_sum\"]\n1481: \"validation_loss\": total_loss / max(total_weight, 1.0e-12),\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\"\n1571: best_loss = float(\"inf\")\n1601: \"negative_filter_scope\", \"last-100\"\n1629: stored_filter_scope != config.negative_filter_scope\n1657: prior.pop(\"validation_loss\", None)\n1660: f\"{stored_filter_scope}->{config.negative_filter_scope}, \"\n1666: if \"validation_loss\" not in prior:\n1682: best_loss = float(prior[\"validation_loss\"])\n1687: f\"loss={best_loss:.6f}\"\n1731: diagnostics[\"validation_loss\"]\n1732: < best_loss - config.early_stopping_min_delta\n1735: best_loss = diagnostics[\"validation_loss\"]\n1754: f\"epoch={epoch}/{config.epochs} train_loss={diagnostics['loss']:.5f} \"\n1755: f\"validation_loss={diagnostics['validation_loss']:.5f} \"\n1766: f\"best_epoch={best_epoch} best_validation_loss={best_loss:.6f}\"\n1776: f\"loss={best_loss:.6f}\"\n1782:def inference_queries(\n1861:def rank_candidates(\n1912:def write_ranking(frame: pd.DataFrame, output: Path) -> None:\n1922:def main() -> int:\n1939: \"--loss-microbatch-size\",\n1941: default=Config.loss_microbatch_size,\n1944: \"--negative-filter-scope\",\n1946: default=Config.negative_filter_scope,\n2012: or args.loss_microbatch_size < 1\n2033: loss_microbatch_size=args.loss_microbatch_size,\n2034: negative_filter_scope=args.negative_filter_scope,\n2126: \"sequence_encoder\": \"one-layer-GRU-dense-timestep-loss\",\n2129: \"loss_execution\": \"gradient-accumulated-microbatches\",\n2136: \"negative_sampling\": (\n2139: \"false_negative_filter\": config.negative_filter_scope,\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "session_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "root_turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "response_id": "resp_021598e9bafc9da7016ab3617ab6b087d2884c5230592e2801", "usage": { "input_tokens": 25898, "cached_input_tokens": 15744, "cache_write_input_tokens": 0, "output_tokens": 114, "reasoning_output_tokens": 0, "total_tokens": 26012 }, "turn_token_usage": { "input_tokens": 41867, "cached_input_tokens": 27904, "cache_write_input_tokens": 0, "output_tokens": 198, "reasoning_output_tokens": 0, "total_tokens": 42065 }, "thread_token_usage": { "input_tokens": 41867, "cached_input_tokens": 27904, "cache_write_input_tokens": 0, "output_tokens": 198, "reasoning_output_tokens": 0, "total_tokens": 42065 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-c15dbde0-2f93-489f-ac3e-aeb5da5f26cf", "process_id": "80732", "command": [ "/bin/sh", "-lc", "ls -lh /task/data && cat /task/evaluate_public.py && cat /task/current_model/two_tower_model.py" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "list_files", "cmd": "ls -lh /task/data", "path": "data" }, { "type": "read", "cmd": "cat /task/evaluate_public.py", "name": "evaluate_public.py", "path": "/task/evaluate_public.py" }, { "type": "read", "cmd": "cat /task/current_model/two_tower_model.py", "name": "two_tower_model.py", "path": "/task/current_model/two_tower_model.py" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "total 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 05:15 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 05:15 validation_preference_state.parquet\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\"\"\"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", "stderr": "", "aggregated_output": "total 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 05:15 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 05:15 validation_preference_state.parquet\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\"\"\"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", "exit_code": 0, "duration": { "secs": 0, "nanos": 3090 }, "formatted_output": "total 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 05:15 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 05:15 validation_preference_state.parquet\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\"\"\"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" } } ### exec · 2026-09-23T05:20:07.552Z { "code": "text(await tools.exec_command({cmd:\"OPENBLAS_NUM_THREADS=1 OMP_NUM_THREADS=4 PYTHONDONTWRITEBYTECODE=1 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/baseline_ckpt --threads 4 > /app/solution/baseline.json 2> /app/solution/baseline.log\",yield_time_ms:1000}));\ntext(await tools.exec_command({cmd:\"sed -n '534,765p' /app/solution/train_current_model.py && sed -n '813,867p' /app/solution/train_current_model.py && sed -n '1111,1363p' /app/solution/train_current_model.py\",max_output_tokens:10500}));\n" } ### Tool output · 2026-09-23T05:20:07.795Z Script completed Wall time 0.2 seconds Output: {"chunk_id":"ac1982","wall_time_seconds":0.035942137,"exit_code":1,"original_token_count":0,"output":""} {"chunk_id":"900bca","wall_time_seconds":0.00000241,"exit_code":0,"original_token_count":4912,"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(\n model: base.SequentialTwoTower,\n items: torch.Tensor,\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(\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-b23f-76c2-905d-9e8b0e97f817", "turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "session_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "root_turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "response_id": "resp_021598e9bafc9da7016ab3617f4c5887d2b1556124da14533c", "usage": { "input_tokens": 34619, "cached_input_tokens": 25728, "cache_write_input_tokens": 0, "output_tokens": 195, "reasoning_output_tokens": 0, "total_tokens": 34814 }, "turn_token_usage": { "input_tokens": 76486, "cached_input_tokens": 53632, "cache_write_input_tokens": 0, "output_tokens": 393, "reasoning_output_tokens": 0, "total_tokens": 76879 }, "thread_token_usage": { "input_tokens": 76486, "cached_input_tokens": 53632, "cache_write_input_tokens": 0, "output_tokens": 393, "reasoning_output_tokens": 0, "total_tokens": 76879 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-9c406bae-7c67-4332-9080-f31c20b99d48", "process_id": "19312", "command": [ "/bin/sh", "-lc", "OPENBLAS_NUM_THREADS=1 OMP_NUM_THREADS=4 PYTHONDONTWRITEBYTECODE=1 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/baseline_ckpt --threads 4 > /app/solution/baseline.json 2> /app/solution/baseline.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "OPENBLAS_NUM_THREADS=1 OMP_NUM_THREADS=4 PYTHONDONTWRITEBYTECODE=1 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/baseline_ckpt --threads 4 > /app/solution/baseline.json 2> /app/solution/baseline.log" } ], "source": "unified_exec_startup", "status": "failed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 1, "duration": { "secs": 0, "nanos": 35945077 }, "formatted_output": "" } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-2ccf7181-f74f-4a0c-96e4-8636c940dcb8", "process_id": "61455", "command": [ "/bin/sh", "-lc", "sed -n '534,765p' /app/solution/train_current_model.py && sed -n '813,867p' /app/solution/train_current_model.py && sed -n '1111,1363p' /app/solution/train_current_model.py" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "read", "cmd": "sed -n '534,765p' /app/solution/train_current_model.py", "name": "train_current_model.py", "path": "/app/solution/train_current_model.py" }, { "type": "read", "cmd": "sed -n '813,867p' /app/solution/train_current_model.py", "name": "train_current_model.py", "path": "/app/solution/train_current_model.py" }, { "type": "read", "cmd": "sed -n '1111,1363p' /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(\n model: base.SequentialTwoTower,\n items: torch.Tensor,\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(\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(\n model: base.SequentialTwoTower,\n items: torch.Tensor,\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(\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": 2410 }, "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(\n model: base.SequentialTwoTower,\n items: torch.Tensor,\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(\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 · 2026-09-23T05:20:10.721Z { "code": "text(await tools.exec_command({cmd:\"cat /app/solution/baseline.log && which python && ls /opt && env | rg 'VIRTUAL|PATH' && cat /sys/fs/cgroup/cpu.max && cat /sys/fs/cgroup/memory.max\",max_output_tokens:2000}));\n" } ### Tool output · 2026-09-23T05:20:10.783Z Script completed Wall time 0.0 seconds Output: {"chunk_id":"f50609","wall_time_seconds":0.00000755,"exit_code":0,"original_token_count":100,"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\nPATH=/opt/codex-home/packages/standalone/releases/0.153.4-x86_64-unknown-linux-musl/codex-path:/usr/local/bin:/usr/bin:/bin:/usr/local/games:/usr/games\n800000 100000\n8589934592\n"} ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-c93ec85c-e67a-461b-9cd7-9c099e6dee74", "process_id": "38574", "command": [ "/bin/sh", "-lc", "cat /app/solution/baseline.log && which python && ls /opt && env | rg 'VIRTUAL|PATH' && cat /sys/fs/cgroup/cpu.max && cat /sys/fs/cgroup/memory.max" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat /app/solution/baseline.log && which python && ls /opt && env | rg 'VIRTUAL|PATH' && cat /sys/fs/cgroup/cpu.max && cat /sys/fs/cgroup/memory.max" } ], "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\nPATH=/opt/codex-home/packages/standalone/releases/0.153.4-x86_64-unknown-linux-musl/codex-path:/usr/local/bin:/usr/bin:/bin:/usr/local/games:/usr/games\n800000 100000\n8589934592\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\nPATH=/opt/codex-home/packages/standalone/releases/0.153.4-x86_64-unknown-linux-musl/codex-path:/usr/local/bin:/usr/bin:/bin:/usr/local/games:/usr/games\n800000 100000\n8589934592\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 7550 }, "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\nPATH=/opt/codex-home/packages/standalone/releases/0.153.4-x86_64-unknown-linux-musl/codex-path:/usr/local/bin:/usr/bin:/bin:/usr/local/games:/usr/games\n800000 100000\n8589934592\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "session_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "root_turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "response_id": "resp_021598e9bafc9da7016ab36187f64487d29125a23618cce04c", "usage": { "input_tokens": 39808, "cached_input_tokens": 34432, "cache_write_input_tokens": 0, "output_tokens": 72, "reasoning_output_tokens": 0, "total_tokens": 39880 }, "turn_token_usage": { "input_tokens": 116294, "cached_input_tokens": 88064, "cache_write_input_tokens": 0, "output_tokens": 465, "reasoning_output_tokens": 0, "total_tokens": 116759 }, "thread_token_usage": { "input_tokens": 116294, "cached_input_tokens": 88064, "cache_write_input_tokens": 0, "output_tokens": 465, "reasoning_output_tokens": 0, "total_tokens": 116759 } } ### exec · 2026-09-23T05:20:17.503Z { "code": "text(await tools.exec_command({cmd:\"OPENBLAS_NUM_THREADS=1 OMP_NUM_THREADS=4 PYTHONDONTWRITEBYTECODE=1 /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/baseline_ckpt --threads 4 > /app/solution/baseline.json 2> /app/solution/baseline.log\",yield_time_ms:1000}));\ntext(await tools.exec_command({cmd:\"sed -n '867,1035p' /app/solution/train_current_model.py && sed -n '1521,1645p' /app/solution/train_current_model.py\",max_output_tokens:6000}));\n" } ### Tool output · 2026-09-23T05:20:18.710Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"8feb46","wall_time_seconds":1.00112132,"session_id":84315,"original_token_count":0,"output":""} {"chunk_id":"fd401e","wall_time_seconds":0.000004101,"exit_code":0,"original_token_count":2897,"output":"def 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(\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"} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "session_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "root_turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "response_id": "resp_021598e9bafc9da7016ab3618b018c87d29402545b008ab256", "usage": { "input_tokens": 40060, "cached_input_tokens": 39680, "cache_write_input_tokens": 0, "output_tokens": 185, "reasoning_output_tokens": 0, "total_tokens": 40245 }, "turn_token_usage": { "input_tokens": 156354, "cached_input_tokens": 127744, "cache_write_input_tokens": 0, "output_tokens": 650, "reasoning_output_tokens": 0, "total_tokens": 157004 }, "thread_token_usage": { "input_tokens": 156354, "cached_input_tokens": 127744, "cache_write_input_tokens": 0, "output_tokens": 650, "reasoning_output_tokens": 0, "total_tokens": 157004 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-df36728b-a417-4eeb-b874-a903babc41be", "process_id": "37810", "command": [ "/bin/sh", "-lc", "sed -n '867,1035p' /app/solution/train_current_model.py && sed -n '1521,1645p' /app/solution/train_current_model.py" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "read", "cmd": "sed -n '867,1035p' /app/solution/train_current_model.py", "name": "train_current_model.py", "path": "/app/solution/train_current_model.py" }, { "type": "read", "cmd": "sed -n '1521,1645p' /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 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(\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", "stderr": "", "aggregated_output": "def 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(\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", "exit_code": 0, "duration": { "secs": 0, "nanos": 4101 }, "formatted_output": "def 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(\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" } } ## Step 8 · agent · 2026-09-23T05:20:26.821Z ### Thinking unavailable — no text recorded The baseline samples 24 uniform negatives and eight in-batch negatives, then downweights frequent in-batch items. My working hypothesis is that this gives too little training weight to tracks that compete for the top ten. I’ll test model-scored hard negatives while retaining random negatives and the existing false-negative filters. ### exec · 2026-09-23T05:20:32.419Z { "code": "text(await tools.exec_command({cmd:\"tail -8 /app/solution/baseline.log && ps -eo pid,etime,%cpu,rss,args | rg 'train_current|python' && df -h /app/solution\",max_output_tokens:2000}));\ntext(await tools.exec_command({cmd:\"sed -n '1364,1488p' /app/solution/train_current_model.py && sed -n '1035,1111p' /app/solution/train_current_model.py\",max_output_tokens:4000}));\n" } ### Tool output · 2026-09-23T05:20:32.497Z Script completed Wall time 0.1 seconds Output: {"chunk_id":"60f372","wall_time_seconds":0.000001949,"exit_code":1,"original_token_count":35,"output":"loaded explicit events: rows=720,270 users=8,220\nprepared users=1,000 tokens=2,090,746 states=35,727 elapsed=4.8s\n/bin/sh: 1: ps: not found\n"} {"chunk_id":"d253a9","wall_time_seconds":0.00000316,"exit_code":0,"original_token_count":1847,"output":"def 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 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"} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "session_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "root_turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "response_id": "resp_021598e9bafc9da7016ab36192e3e487d28885a204216b74a5", "usage": { "input_tokens": 43083, "cached_input_tokens": 39936, "cache_write_input_tokens": 0, "output_tokens": 409, "reasoning_output_tokens": 218, "total_tokens": 43492 }, "turn_token_usage": { "input_tokens": 199437, "cached_input_tokens": 167680, "cache_write_input_tokens": 0, "output_tokens": 1059, "reasoning_output_tokens": 218, "total_tokens": 200496 }, "thread_token_usage": { "input_tokens": 199437, "cached_input_tokens": 167680, "cache_write_input_tokens": 0, "output_tokens": 1059, "reasoning_output_tokens": 218, "total_tokens": 200496 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-fb7e7faa-1aaf-4a60-8376-0a126fe81350", "process_id": "34371", "command": [ "/bin/sh", "-lc", "tail -8 /app/solution/baseline.log && ps -eo pid,etime,%cpu,rss,args | rg 'train_current|python' && df -h /app/solution" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -8 /app/solution/baseline.log && ps -eo pid,etime,%cpu,rss,args | rg 'train_current|python' && df -h /app/solution" } ], "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=4.8s\n/bin/sh: 1: ps: not found\n", "stderr": "", "aggregated_output": "loaded explicit events: rows=720,270 users=8,220\nprepared users=1,000 tokens=2,090,746 states=35,727 elapsed=4.8s\n/bin/sh: 1: ps: not found\n", "exit_code": 1, "duration": { "secs": 0, "nanos": 1949 }, "formatted_output": "loaded explicit events: rows=720,270 users=8,220\nprepared users=1,000 tokens=2,090,746 states=35,727 elapsed=4.8s\n/bin/sh: 1: ps: not found\n" } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-a2ad3453-a505-4b8e-92d9-fe5b6c3a1bcb", "process_id": "25246", "command": [ "/bin/sh", "-lc", "sed -n '1364,1488p' /app/solution/train_current_model.py && sed -n '1035,1111p' /app/solution/train_current_model.py" ], "cwd": "file:///app", "parsed_cmd": [ { "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 '1035,1111p' /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 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 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", "stderr": "", "aggregated_output": "def 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 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", "exit_code": 0, "duration": { "secs": 0, "nanos": 3160 }, "formatted_output": "def 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 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" } } ## Step 9 · agent · 2026-09-23T05:20:39.682Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T05:21:27.797Z { "code": "text(await tools.exec_command({cmd:\"tail -6 /app/solution/baseline.log && df -h /app/solution\",max_output_tokens:1000}));\ntext(await tools.exec_command({cmd:\"cat > /app/solution/edit_objective.py <<'PY'\\nfrom pathlib import Path\\np=Path('/app/solution/train_current_model.py')\\ns=p.read_text()\\ns=s.replace(' uniform_negatives: int = 24',' hard_negatives: int = 8\\\\n hard_pool_size: int = 1024\\\\n hard_loss_weight: float = 0.5\\\\n ranking_loss: str = \\\"bce\\\"\\\\n uniform_negatives: int = 24')\\ns=s.replace(' backward: bool,\\\\n) -> tuple[dict[str, float]', ' backward: bool,\\\\n user_rows: np.ndarray | None = None,\\\\n known_positive_packed: np.ndarray | None = None,\\\\n generator: torch.Generator | None = None,\\\\n) -> tuple[dict[str, float]')\\ns=s.replace(' for begin in range(0, target_count, config.loss_microbatch_size):', ''' # One shared pool amortizes item encoding and uses a dense matrix multiply.\\n # Held-out BCE remains the original deterministic random-negative diagnostic.\\n pool = None\\n if backward and config.hard_negatives:\\n if known_positive_packed is None or user_rows is None:\\n raise ValueError(\\\"hard mining requires the complete training-prefix filter\\\")\\n pool = torch.unique(torch.cat([\\n positive_items.detach().cpu(),\\n torch.randint(len(prepared.item_ids), (config.hard_pool_size,), generator=generator),\\n ])).to(device)\\n with torch.no_grad():\\n was_training = model.training\\n model.eval()\\n pool_vectors = model.encode_items(pool)\\n model.train(was_training)\\n for begin in range(0, target_count, config.loss_microbatch_size):''')\\ns=s.replace(''' positive_vectors, negative_vectors = candidate_vectors(\\n model,\\n positive_items[begin:end],\\n negatives[begin:end],\\n )''',''' selected_negatives = negatives[begin:end]\\n if pool is not None:\\n with torch.no_grad():\\n mining_scores = query_vectors.detach() @ pool_vectors.T\\n pool_np = pool.cpu().numpy()\\n rows = user_rows[begin:end]\\n bits = known_positive_packed[rows[:, None], pool_np[None, :] >> 3]\\n invalid = ((bits >> (pool_np[None, :] & 7)) & 1).astype(bool)\\n # Explicit state can contain likes without a qualifying listen.\\n likes = prepared.liked_states[selected_states]\\n pool_columns = np.full(len(prepared.item_ids) + 1, -1, dtype=np.int32)\\n pool_columns[pool_np] = np.arange(len(pool_np))\\n like_columns = pool_columns[likes]\\n rr, cc = np.nonzero(like_columns >= 0)\\n invalid[rr, like_columns[rr, cc]] = True\\n mining_scores.masked_fill_(torch.from_numpy(invalid).to(device), -torch.inf)\\n # Current targets and existing random negatives are never mined.\\n excluded = torch.cat([positive_items[begin:end, None], selected_negatives], dim=1)\\n excluded_columns = pool_columns[excluded.cpu().numpy()]\\n rr, cc = np.nonzero(excluded_columns >= 0)\\n mining_scores[torch.from_numpy(rr).to(device),\\n torch.from_numpy(excluded_columns[rr, cc]).to(device)] = -torch.inf\\n values, columns = mining_scores.topk(config.hard_negatives, dim=1)\\n if not torch.isfinite(values).all():\\n raise RuntimeError(\\\"not enough valid hard negatives\\\")\\n selected_negatives = torch.cat([selected_negatives, pool[columns]], dim=1)\\n positive_vectors, negative_vectors = candidate_vectors(\\n model, positive_items[begin:end], selected_negatives,\\n )''')\\ns=s.replace(''' negative_loss = corrected_negative_loss(\\n negative_logits, negative_weights[begin:end], config\\n )''',''' random_count = config.uniform_negatives + config.inbatch_negatives\\n negative_loss = corrected_negative_loss(\\n negative_logits[:, :random_count], negative_weights[begin:end], config\\n )\\n if pool is not None:\\n hard_loss = F.softplus(negative_logits[:, random_count:]).mean(dim=1)\\n negative_loss = ((1.0 - config.hard_loss_weight) * negative_loss\\n + config.hard_loss_weight * hard_loss)\\n if config.ranking_loss == \\\"softmax\\\" and backward:\\n # Relative ranking loss concentrates gradient on confusing candidates.\\n joint = torch.cat([positive_logits[:, None], negative_logits], dim=1)\\n positive_loss = 2.0 * (torch.logsumexp(joint, dim=1) - positive_logits)\\n negative_loss = torch.zeros_like(positive_loss)''')\\ns=s.replace(''' backward=True,\\n )''',''' backward=True,\\n user_rows=flat_user_rows,\\n known_positive_packed=known_positive_packed,\\n generator=generator,\\n )''')\\ns=s.replace(''' parser.add_argument(\\\"--epochs\\\", type=int, default=Config.epochs)''',''' 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-loss-weight\\\", type=float, default=Config.hard_loss_weight)\\n parser.add_argument(\\\"--ranking-loss\\\", choices=(\\\"bce\\\", \\\"softmax\\\"), default=Config.ranking_loss)\\n parser.add_argument(\\\"--epochs\\\", type=int, default=Config.epochs)''')\\ns=s.replace(''' epochs=args.epochs,\\n learning_rate''',''' epochs=args.epochs,\\n hard_negatives=args.hard_negatives,\\n hard_pool_size=args.hard_pool_size,\\n hard_loss_weight=args.hard_loss_weight,\\n ranking_loss=args.ranking_loss,\\n learning_rate''')\\ns=s.replace(''' stored_filter_scope != config.negative_filter_scope''',''' any(stored_config.get(name, default) != getattr(config, name)\\n for name, default in ((\\\"hard_negatives\\\", 0), (\\\"hard_pool_size\\\", 1024),\\n (\\\"hard_loss_weight\\\", 0.5), (\\\"ranking_loss\\\", \\\"bce\\\")))\\n or stored_filter_scope != config.negative_filter_scope''')\\ns=s.replace(''' if batches % 250 == 0:''',''' if batches % 25 == 0:''')\\np.write_text(s)\\nPY\\n/opt/task-venv/bin/python /app/solution/edit_objective.py\\nPYTHONDONTWRITEBYTECODE=1 /opt/task-venv/bin/python -c \\\"import ast; ast.parse(open('/app/solution/train_current_model.py').read())\\\"\",\"max_output_tokens\":1000}));\n" } ### Tool output · 2026-09-23T05:21:28.107Z Script completed Wall time 0.3 seconds Output: {"chunk_id":"645e51","wall_time_seconds":0.000023471,"exit_code":0,"original_token_count":156,"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\nFilesystem Size Used Avail Use% Mounted on\noverlay 96G 15G 82G 15% /\n"} {"chunk_id":"92e684","wall_time_seconds":0.080910515,"exit_code":0,"original_token_count":162,"output":"Traceback (most recent call last):\n File \"/app/solution/edit_objective.py\", line 93, in \n p.write_text(s)\n File \"/usr/local/lib/python3.12/pathlib.py\", line 1047, in write_text\n with self.open(mode='w', encoding=encoding, errors=errors, newline=newline) as f:\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/usr/local/lib/python3.12/pathlib.py\", line 1013, in open\n return io.open(self, mode, buffering, encoding, errors, newline)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nPermissionError: [Errno 13] Permission denied: '/app/solution/train_current_model.py'\n"} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "session_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "root_turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "response_id": "resp_021598e9bafc9da7016ab361a0b15c87d2b6ca1c674865433e", "usage": { "input_tokens": 45424, "cached_input_tokens": 42880, "cache_write_input_tokens": 0, "output_tokens": 1806, "reasoning_output_tokens": 195, "total_tokens": 47230 }, "turn_token_usage": { "input_tokens": 244861, "cached_input_tokens": 210560, "cache_write_input_tokens": 0, "output_tokens": 2865, "reasoning_output_tokens": 413, "total_tokens": 247726 }, "thread_token_usage": { "input_tokens": 244861, "cached_input_tokens": 210560, "cache_write_input_tokens": 0, "output_tokens": 2865, "reasoning_output_tokens": 413, "total_tokens": 247726 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-b3e01b33-f536-4625-aa4a-9df562cb8f41", "process_id": "76672", "command": [ "/bin/sh", "-lc", "tail -6 /app/solution/baseline.log && df -h /app/solution" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -6 /app/solution/baseline.log && df -h /app/solution" } ], "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\nFilesystem Size Used Avail Use% Mounted on\noverlay 96G 15G 82G 15% /\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\nFilesystem Size Used Avail Use% Mounted on\noverlay 96G 15G 82G 15% /\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 23471 }, "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\nFilesystem Size Used Avail Use% Mounted on\noverlay 96G 15G 82G 15% /\n" } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-1693024d-a520-4320-8f6e-5e2f94dff7bf", "process_id": "35201", "command": [ "/bin/sh", "-lc", "cat > /app/solution/edit_objective.py <<'PY'\nfrom pathlib import Path\np=Path('/app/solution/train_current_model.py')\ns=p.read_text()\ns=s.replace(' uniform_negatives: int = 24',' hard_negatives: int = 8\\n hard_pool_size: int = 1024\\n hard_loss_weight: float = 0.5\\n ranking_loss: str = \"bce\"\\n uniform_negatives: int = 24')\ns=s.replace(' backward: bool,\\n) -> tuple[dict[str, float]', ' backward: bool,\\n user_rows: np.ndarray | None = None,\\n known_positive_packed: np.ndarray | None = None,\\n generator: torch.Generator | None = None,\\n) -> tuple[dict[str, float]')\ns=s.replace(' for begin in range(0, target_count, config.loss_microbatch_size):', ''' # One shared pool amortizes item encoding and uses a dense matrix multiply.\n # Held-out BCE remains the original deterministic random-negative diagnostic.\n pool = None\n if backward and config.hard_negatives:\n if known_positive_packed is None or user_rows is None:\n raise ValueError(\"hard mining requires the complete training-prefix filter\")\n pool = torch.unique(torch.cat([\n positive_items.detach().cpu(),\n torch.randint(len(prepared.item_ids), (config.hard_pool_size,), generator=generator),\n ])).to(device)\n with torch.no_grad():\n was_training = model.training\n model.eval()\n pool_vectors = model.encode_items(pool)\n model.train(was_training)\n for begin in range(0, target_count, config.loss_microbatch_size):''')\ns=s.replace(''' positive_vectors, negative_vectors = candidate_vectors(\n model,\n positive_items[begin:end],\n negatives[begin:end],\n )''',''' selected_negatives = negatives[begin:end]\n if pool is not None:\n with torch.no_grad():\n mining_scores = query_vectors.detach() @ pool_vectors.T\n pool_np = pool.cpu().numpy()\n rows = user_rows[begin:end]\n bits = known_positive_packed[rows[:, None], pool_np[None, :] >> 3]\n invalid = ((bits >> (pool_np[None, :] & 7)) & 1).astype(bool)\n # Explicit state can contain likes without a qualifying listen.\n likes = prepared.liked_states[selected_states]\n pool_columns = np.full(len(prepared.item_ids) + 1, -1, dtype=np.int32)\n pool_columns[pool_np] = np.arange(len(pool_np))\n like_columns = pool_columns[likes]\n rr, cc = np.nonzero(like_columns >= 0)\n invalid[rr, like_columns[rr, cc]] = True\n mining_scores.masked_fill_(torch.from_numpy(invalid).to(device), -torch.inf)\n # Current targets and existing random negatives are never mined.\n excluded = torch.cat([positive_items[begin:end, None], selected_negatives], dim=1)\n excluded_columns = pool_columns[excluded.cpu().numpy()]\n rr, cc = np.nonzero(excluded_columns >= 0)\n mining_scores[torch.from_numpy(rr).to(device),\n torch.from_numpy(excluded_columns[rr, cc]).to(device)] = -torch.inf\n values, columns = mining_scores.topk(config.hard_negatives, dim=1)\n if not torch.isfinite(values).all():\n raise RuntimeError(\"not enough valid hard negatives\")\n selected_negatives = torch.cat([selected_negatives, pool[columns]], dim=1)\n positive_vectors, negative_vectors = candidate_vectors(\n model, positive_items[begin:end], selected_negatives,\n )''')\ns=s.replace(''' negative_loss = corrected_negative_loss(\n negative_logits, negative_weights[begin:end], config\n )''',''' random_count = config.uniform_negatives + config.inbatch_negatives\n negative_loss = corrected_negative_loss(\n negative_logits[:, :random_count], negative_weights[begin:end], config\n )\n if pool is not None:\n hard_loss = F.softplus(negative_logits[:, random_count:]).mean(dim=1)\n negative_loss = ((1.0 - config.hard_loss_weight) * negative_loss\n + config.hard_loss_weight * hard_loss)\n if config.ranking_loss == \"softmax\" and backward:\n # Relative ranking loss concentrates gradient on confusing candidates.\n joint = torch.cat([positive_logits[:, None], negative_logits], dim=1)\n positive_loss = 2.0 * (torch.logsumexp(joint, dim=1) - positive_logits)\n negative_loss = torch.zeros_like(positive_loss)''')\ns=s.replace(''' backward=True,\n )''',''' backward=True,\n user_rows=flat_user_rows,\n known_positive_packed=known_positive_packed,\n generator=generator,\n )''')\ns=s.replace(''' parser.add_argument(\"--epochs\", type=int, default=Config.epochs)''',''' 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-loss-weight\", type=float, default=Config.hard_loss_weight)\n parser.add_argument(\"--ranking-loss\", choices=(\"bce\", \"softmax\"), default=Config.ranking_loss)\n parser.add_argument(\"--epochs\", type=int, default=Config.epochs)''')\ns=s.replace(''' epochs=args.epochs,\n learning_rate''',''' epochs=args.epochs,\n hard_negatives=args.hard_negatives,\n hard_pool_size=args.hard_pool_size,\n hard_loss_weight=args.hard_loss_weight,\n ranking_loss=args.ranking_loss,\n learning_rate''')\ns=s.replace(''' stored_filter_scope != config.negative_filter_scope''',''' any(stored_config.get(name, default) != getattr(config, name)\n for name, default in ((\"hard_negatives\", 0), (\"hard_pool_size\", 1024),\n (\"hard_loss_weight\", 0.5), (\"ranking_loss\", \"bce\")))\n or stored_filter_scope != config.negative_filter_scope''')\ns=s.replace(''' if batches % 250 == 0:''',''' if batches % 25 == 0:''')\np.write_text(s)\nPY\n/opt/task-venv/bin/python /app/solution/edit_objective.py\nPYTHONDONTWRITEBYTECODE=1 /opt/task-venv/bin/python -c \"import ast; ast.parse(open('/app/solution/train_current_model.py').read())\"" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat > /app/solution/edit_objective.py <<'PY'\nfrom pathlib import Path\np=Path('/app/solution/train_current_model.py')\ns=p.read_text()\ns=s.replace(' uniform_negatives: int = 24',' hard_negatives: int = 8\\n hard_pool_size: int = 1024\\n hard_loss_weight: float = 0.5\\n ranking_loss: str = \"bce\"\\n uniform_negatives: int = 24')\ns=s.replace(' backward: bool,\\n) -> tuple[dict[str, float]', ' backward: bool,\\n user_rows: np.ndarray | None = None,\\n known_positive_packed: np.ndarray | None = None,\\n generator: torch.Generator | None = None,\\n) -> tuple[dict[str, float]')\ns=s.replace(' for begin in range(0, target_count, config.loss_microbatch_size):', ''' # One shared pool amortizes item encoding and uses a dense matrix multiply.\n # Held-out BCE remains the original deterministic random-negative diagnostic.\n pool = None\n if backward and config.hard_negatives:\n if known_positive_packed is None or user_rows is None:\n raise ValueError(\"hard mining requires the complete training-prefix filter\")\n pool = torch.unique(torch.cat([\n positive_items.detach().cpu(),\n torch.randint(len(prepared.item_ids), (config.hard_pool_size,), generator=generator),\n ])).to(device)\n with torch.no_grad():\n was_training = model.training\n model.eval()\n pool_vectors = model.encode_items(pool)\n model.train(was_training)\n for begin in range(0, target_count, config.loss_microbatch_size):''')\ns=s.replace(''' positive_vectors, negative_vectors = candidate_vectors(\n model,\n positive_items[begin:end],\n negatives[begin:end],\n )''',''' selected_negatives = negatives[begin:end]\n if pool is not None:\n with torch.no_grad():\n mining_scores = query_vectors.detach() @ pool_vectors.T\n pool_np = pool.cpu().numpy()\n rows = user_rows[begin:end]\n bits = known_positive_packed[rows[:, None], pool_np[None, :] >> 3]\n invalid = ((bits >> (pool_np[None, :] & 7)) & 1).astype(bool)\n # Explicit state can contain likes without a qualifying listen.\n likes = prepared.liked_states[selected_states]\n pool_columns = np.full(len(prepared.item_ids) + 1, -1, dtype=np.int32)\n pool_columns[pool_np] = np.arange(len(pool_np))\n like_columns = pool_columns[likes]\n rr, cc = np.nonzero(like_columns >= 0)\n invalid[rr, like_columns[rr, cc]] = True\n mining_scores.masked_fill_(torch.from_numpy(invalid).to(device), -torch.inf)\n # Current targets and existing random negatives are never mined.\n excluded = torch.cat([positive_items[begin:end, None], selected_negatives], dim=1)\n excluded_columns = pool_columns[excluded.cpu().numpy()]\n rr, cc = np.nonzero(excluded_columns >= 0)\n mining_scores[torch.from_numpy(rr).to(device),\n torch.from_numpy(excluded_columns[rr, cc]).to(device)] = -torch.inf\n values, columns = mining_scores.topk(config.hard_negatives, dim=1)\n if not torch.isfinite(values).all():\n raise RuntimeError(\"not enough valid hard negatives\")\n selected_negatives = torch.cat([selected_negatives, pool[columns]], dim=1)\n positive_vectors, negative_vectors = candidate_vectors(\n model, positive_items[begin:end], selected_negatives,\n )''')\ns=s.replace(''' negative_loss = corrected_negative_loss(\n negative_logits, negative_weights[begin:end], config\n )''',''' random_count = config.uniform_negatives + config.inbatch_negatives\n negative_loss = corrected_negative_loss(\n negative_logits[:, :random_count], negative_weights[begin:end], config\n )\n if pool is not None:\n hard_loss = F.softplus(negative_logits[:, random_count:]).mean(dim=1)\n negative_loss = ((1.0 - config.hard_loss_weight) * negative_loss\n + config.hard_loss_weight * hard_loss)\n if config.ranking_loss == \"softmax\" and backward:\n # Relative ranking loss concentrates gradient on confusing candidates.\n joint = torch.cat([positive_logits[:, None], negative_logits], dim=1)\n positive_loss = 2.0 * (torch.logsumexp(joint, dim=1) - positive_logits)\n negative_loss = torch.zeros_like(positive_loss)''')\ns=s.replace(''' backward=True,\n )''',''' backward=True,\n user_rows=flat_user_rows,\n known_positive_packed=known_positive_packed,\n generator=generator,\n )''')\ns=s.replace(''' parser.add_argument(\"--epochs\", type=int, default=Config.epochs)''',''' 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-loss-weight\", type=float, default=Config.hard_loss_weight)\n parser.add_argument(\"--ranking-loss\", choices=(\"bce\", \"softmax\"), default=Config.ranking_loss)\n parser.add_argument(\"--epochs\", type=int, default=Config.epochs)''')\ns=s.replace(''' epochs=args.epochs,\n learning_rate''',''' epochs=args.epochs,\n hard_negatives=args.hard_negatives,\n hard_pool_size=args.hard_pool_size,\n hard_loss_weight=args.hard_loss_weight,\n ranking_loss=args.ranking_loss,\n learning_rate''')\ns=s.replace(''' stored_filter_scope != config.negative_filter_scope''',''' any(stored_config.get(name, default) != getattr(config, name)\n for name, default in ((\"hard_negatives\", 0), (\"hard_pool_size\", 1024),\n (\"hard_loss_weight\", 0.5), (\"ranking_loss\", \"bce\")))\n or stored_filter_scope != config.negative_filter_scope''')\ns=s.replace(''' if batches % 250 == 0:''',''' if batches % 25 == 0:''')\np.write_text(s)\nPY\n/opt/task-venv/bin/python /app/solution/edit_objective.py\nPYTHONDONTWRITEBYTECODE=1 /opt/task-venv/bin/python -c \"import ast; ast.parse(open('/app/solution/train_current_model.py').read())\"" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 0, "nanos": 80863062 }, "formatted_output": "" } } ## Step 10 · agent · 2026-09-23T05:21:31.287Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T05:21:34.768Z { "code": "text(await tools.exec_command({cmd:\"chmod u+w /app/solution/train_current_model.py /app/solution/two_tower_model.py && /opt/task-venv/bin/python /app/solution/edit_objective.py && PYTHONDONTWRITEBYTECODE=1 /opt/task-venv/bin/python -c \\\"import ast; ast.parse(open('/app/solution/train_current_model.py').read())\\\" && tail -3 /app/solution/baseline.log\",max_output_tokens:1000}));\ntext(await tools.clock__curr_time({}));\n" } ### Tool output · 2026-09-23T05:21:35.018Z Script completed Wall time 0.2 seconds Output: {"chunk_id":"0db5ee","wall_time_seconds":0.067431451,"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"} {"current_time":"2026-09-23 05:21:35 UTC"} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "session_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "root_turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "response_id": "resp_021598e9bafc9da7016ab361d8498c87d2b446b7e18280d08a", "usage": { "input_tokens": 47685, "cached_input_tokens": 45184, "cache_write_input_tokens": 0, "output_tokens": 142, "reasoning_output_tokens": 16, "total_tokens": 47827 }, "turn_token_usage": { "input_tokens": 292546, "cached_input_tokens": 255744, "cache_write_input_tokens": 0, "output_tokens": 3007, "reasoning_output_tokens": 429, "total_tokens": 295553 }, "thread_token_usage": { "input_tokens": 292546, "cached_input_tokens": 255744, "cache_write_input_tokens": 0, "output_tokens": 3007, "reasoning_output_tokens": 429, "total_tokens": 295553 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-82c61df7-ba4b-41b2-acf8-94b3929c4053", "process_id": "81119", "command": [ "/bin/sh", "-lc", "chmod u+w /app/solution/train_current_model.py /app/solution/two_tower_model.py && /opt/task-venv/bin/python /app/solution/edit_objective.py && PYTHONDONTWRITEBYTECODE=1 /opt/task-venv/bin/python -c \"import ast; ast.parse(open('/app/solution/train_current_model.py').read())\" && tail -3 /app/solution/baseline.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "chmod u+w /app/solution/train_current_model.py /app/solution/two_tower_model.py && /opt/task-venv/bin/python /app/solution/edit_objective.py && PYTHONDONTWRITEBYTECODE=1 /opt/task-venv/bin/python -c \"import ast; ast.parse(open('/app/solution/train_current_model.py').read())\" && tail -3 /app/solution/baseline.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": 67415750 }, "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 11 · agent · 2026-09-23T05:21:42.918Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T05:21:48.938Z { "code": "text(await tools.exec_command({cmd:\"cat > /app/solution/check_resources.py <<'PY'\\nfrom pathlib import Path\\nfor p in Path('/proc').glob('[0-9]*'):\\n try:\\n cmd=(p/'cmdline').read_bytes().replace(b'\\\\0',b' ').decode()\\n if '/opt/task-venv/bin/python' in cmd and 'check_resources' not in cmd:\\n print(p.name,cmd[:160])\\n print('\\\\n'.join(x for x in (p/'status').read_text().splitlines() if x.startswith(('VmRSS','Threads'))))\\n except (OSError,UnicodeError): pass\\nprint(Path('/sys/fs/cgroup/memory.current').read_text())\\nPY\\n/opt/task-venv/bin/python /app/solution/check_resources.py && tail -5 /app/solution/baseline.log\",\"max_output_tokens\":1000}));\n" } ### Tool output · 2026-09-23T05:21:49.200Z Script completed Wall time 0.2 seconds Output: {"chunk_id":"987885","wall_time_seconds":0.008742529,"exit_code":0,"original_token_count":207,"output":"267 /bin/sh -c OPENBLAS_NUM_THREADS=1 OMP_NUM_THREADS=4 PYTHONDONTWRITEBYTECODE=1 /opt/task-venv/bin/python /task/current_model/train_current_model.py --stage valid\nVmRSS:\t 1800 kB\nThreads:\t1\n268 /opt/task-venv/bin/python /task/current_model/train_current_model.py --stage validation --public-dir /task --output /app/solution/production_validation.parquet \nVmRSS:\t 3539596 kB\nThreads:\t13\n4074864640\n\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-b23f-76c2-905d-9e8b0e97f817", "turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "session_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "root_turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "response_id": "resp_021598e9bafc9da7016ab361df322887d2aca704649f38e281", "usage": { "input_tokens": 47974, "cached_input_tokens": 47488, "cache_write_input_tokens": 0, "output_tokens": 412, "reasoning_output_tokens": 201, "total_tokens": 48386 }, "turn_token_usage": { "input_tokens": 340520, "cached_input_tokens": 303232, "cache_write_input_tokens": 0, "output_tokens": 3419, "reasoning_output_tokens": 630, "total_tokens": 343939 }, "thread_token_usage": { "input_tokens": 340520, "cached_input_tokens": 303232, "cache_write_input_tokens": 0, "output_tokens": 3419, "reasoning_output_tokens": 630, "total_tokens": 343939 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-b2744a0e-ce1c-49af-8743-e76e66e795db", "process_id": "40813", "command": [ "/bin/sh", "-lc", "cat > /app/solution/check_resources.py <<'PY'\nfrom pathlib import Path\nfor p in Path('/proc').glob('[0-9]*'):\n try:\n cmd=(p/'cmdline').read_bytes().replace(b'\\0',b' ').decode()\n if '/opt/task-venv/bin/python' in cmd and 'check_resources' not in cmd:\n print(p.name,cmd[:160])\n print('\\n'.join(x for x in (p/'status').read_text().splitlines() if x.startswith(('VmRSS','Threads'))))\n except (OSError,UnicodeError): pass\nprint(Path('/sys/fs/cgroup/memory.current').read_text())\nPY\n/opt/task-venv/bin/python /app/solution/check_resources.py && tail -5 /app/solution/baseline.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat > /app/solution/check_resources.py <<'PY'\nfrom pathlib import Path\nfor p in Path('/proc').glob('[0-9]*'):\n try:\n cmd=(p/'cmdline').read_bytes().replace(b'\\0',b' ').decode()\n if '/opt/task-venv/bin/python' in cmd and 'check_resources' not in cmd:\n print(p.name,cmd[:160])\n print('\\n'.join(x for x in (p/'status').read_text().splitlines() if x.startswith(('VmRSS','Threads'))))\n except (OSError,UnicodeError): pass\nprint(Path('/sys/fs/cgroup/memory.current').read_text())\nPY\n/opt/task-venv/bin/python /app/solution/check_resources.py && tail -5 /app/solution/baseline.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "267 /bin/sh -c OPENBLAS_NUM_THREADS=1 OMP_NUM_THREADS=4 PYTHONDONTWRITEBYTECODE=1 /opt/task-venv/bin/python /task/current_model/train_current_model.py --stage valid\nVmRSS:\t 1800 kB\nThreads:\t1\n268 /opt/task-venv/bin/python /task/current_model/train_current_model.py --stage validation --public-dir /task --output /app/solution/production_validation.parquet \nVmRSS:\t 3539596 kB\nThreads:\t13\n4074864640\n\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": "267 /bin/sh -c OPENBLAS_NUM_THREADS=1 OMP_NUM_THREADS=4 PYTHONDONTWRITEBYTECODE=1 /opt/task-venv/bin/python /task/current_model/train_current_model.py --stage valid\nVmRSS:\t 1800 kB\nThreads:\t1\n268 /opt/task-venv/bin/python /task/current_model/train_current_model.py --stage validation --public-dir /task --output /app/solution/production_validation.parquet \nVmRSS:\t 3539596 kB\nThreads:\t13\n4074864640\n\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": 8746099 }, "formatted_output": "267 /bin/sh -c OPENBLAS_NUM_THREADS=1 OMP_NUM_THREADS=4 PYTHONDONTWRITEBYTECODE=1 /opt/task-venv/bin/python /task/current_model/train_current_model.py --stage valid\nVmRSS:\t 1800 kB\nThreads:\t1\n268 /opt/task-venv/bin/python /task/current_model/train_current_model.py --stage validation --public-dir /task --output /app/solution/production_validation.parquet \nVmRSS:\t 3539596 kB\nThreads:\t13\n4074864640\n\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 · 2026-09-23T05:21:53.244Z ### Thinking unavailable — no text recorded The training-prefix filter already excludes known positive listens and active likes. I’ll preserve it in the hard-negative sampler. The experiment will score a shared pool of tracks, select eight high-scoring valid negatives per target, and blend their loss with the existing random-negative loss. The towers and serving score stay unchanged. ### exec · 2026-09-23T05:22:00.468Z { "code": "text(await tools.exec_command({cmd:\"OPENBLAS_NUM_THREADS=1 OMP_NUM_THREADS=4 PYTHONDONTWRITEBYTECODE=1 /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 4 --loss-microbatch-size 1024 > /app/solution/hard.json 2> /app/solution/hard.log\",yield_time_ms:1000}));\ntext(await tools.exec_command({cmd:\"tail -5 /app/solution/baseline.log && tail -5 /app/solution/hard.log\",max_output_tokens:1000}));\n" } ### Tool output · 2026-09-23T05:22:01.676Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"d1b9de","wall_time_seconds":1.001247251,"session_id":8117,"original_token_count":0,"output":""} {"chunk_id":"7092f7","wall_time_seconds":0.00000532,"exit_code":0,"original_token_count":106,"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\n"} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "session_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "root_turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "response_id": "resp_021598e9bafc9da7016ab361ee27e487d2ad01722e859f48ef", "usage": { "input_tokens": 48696, "cached_input_tokens": 47744, "cache_write_input_tokens": 0, "output_tokens": 280, "reasoning_output_tokens": 34, "total_tokens": 48976 }, "turn_token_usage": { "input_tokens": 389216, "cached_input_tokens": 350976, "cache_write_input_tokens": 0, "output_tokens": 3699, "reasoning_output_tokens": 664, "total_tokens": 392915 }, "thread_token_usage": { "input_tokens": 389216, "cached_input_tokens": 350976, "cache_write_input_tokens": 0, "output_tokens": 3699, "reasoning_output_tokens": 664, "total_tokens": 392915 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-ed96e16f-0be3-4f45-9d4a-cddf33346d9a", "process_id": "50492", "command": [ "/bin/sh", "-lc", "tail -5 /app/solution/baseline.log && tail -5 /app/solution/hard.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -5 /app/solution/baseline.log && tail -5 /app/solution/hard.log" } ], "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\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\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 5320 }, "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\n" } } ## Step 13 · agent · 2026-09-23T05:22:06.093Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T05:22:15.773Z { "code": "text(await tools.exec_command({cmd:\"cat > /app/solution/rank_checkpoint.py <<'PY'\\n\\\"\\\"\\\"Rank an existing two-tower checkpoint using the cutoff-specific public history.\\\"\\\"\\\"\\nimport argparse\\nfrom pathlib import Path\\nimport torch\\nimport train_current_model as train\\n\\np = argparse.ArgumentParser()\\np.add_argument('--checkpoint', type=Path, required=True)\\np.add_argument('--stage', choices=['validation', 'submission'], required=True)\\np.add_argument('--output', type=Path, required=True)\\np.add_argument('--threads', type=int, default=4)\\na = p.parse_args()\\ntorch.set_num_threads(a.threads)\\ntorch.set_num_interop_threads(1)\\npayload = torch.load(a.checkpoint, map_location='cpu', weights_only=False)\\nconfig = train.Config(**payload['config'])\\ndata, eligible = train.load_or_prepare(Path('/task'), a.stage, config, Path('/app/solution') / (a.stage + '_cache.npz'))\\nmodel = train.base.SequentialTwoTower(data.item_features, train.model_config(config))\\nmodel.load_state_dict(payload['model_state'])\\nmodel.eval()\\nframe, diag = train.rank_candidates(model, data, eligible, config, torch.device('cpu'), 64)\\ntrain.write_ranking(frame, a.output)\\nprint(diag)\\nPY\\n/opt/task-venv/bin/python /app/solution/check_resources.py && tail -5 /app/solution/hard.log\",max_output_tokens:1500}));\n" } ### Tool output · 2026-09-23T05:22:16.030Z Script completed Wall time 0.2 seconds Output: {"chunk_id":"cd6ecb","wall_time_seconds":0.071708722,"exit_code":0,"original_token_count":301,"output":"267 /bin/sh -c OPENBLAS_NUM_THREADS=1 OMP_NUM_THREADS=4 PYTHONDONTWRITEBYTECODE=1 /opt/task-venv/bin/python /task/current_model/train_current_model.py --stage valid\nVmRSS:\t 1800 kB\nThreads:\t1\n268 /opt/task-venv/bin/python /task/current_model/train_current_model.py --stage validation --public-dir /task --output /app/solution/production_validation.parquet \nVmRSS:\t 4660100 kB\nThreads:\t13\n360 /bin/sh -c OPENBLAS_NUM_THREADS=1 OMP_NUM_THREADS=4 PYTHONDONTWRITEBYTECODE=1 /opt/task-venv/bin/python /app/solution/train_current_model.py --stage validation \nVmRSS:\t 1560 kB\nThreads:\t1\n361 /opt/task-venv/bin/python /app/solution/train_current_model.py --stage validation --public-dir /task --output /app/solution/hard_validation.parquet --prepared-c\nVmRSS:\t 1821960 kB\nThreads:\t8\n6941253632\n\nloaded 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\n"} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "session_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "root_turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "response_id": "resp_021598e9bafc9da7016ab361f9d95887d2bdca501a57d7fc3c", "usage": { "input_tokens": 49200, "cached_input_tokens": 48512, "cache_write_input_tokens": 0, "output_tokens": 432, "reasoning_output_tokens": 100, "total_tokens": 49632 }, "turn_token_usage": { "input_tokens": 438416, "cached_input_tokens": 399488, "cache_write_input_tokens": 0, "output_tokens": 4131, "reasoning_output_tokens": 764, "total_tokens": 442547 }, "thread_token_usage": { "input_tokens": 438416, "cached_input_tokens": 399488, "cache_write_input_tokens": 0, "output_tokens": 4131, "reasoning_output_tokens": 764, "total_tokens": 442547 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-535559fa-49ca-4647-8a26-ba78d7f503ff", "process_id": "67684", "command": [ "/bin/sh", "-lc", "cat > /app/solution/rank_checkpoint.py <<'PY'\n\"\"\"Rank an existing two-tower checkpoint using the cutoff-specific public history.\"\"\"\nimport argparse\nfrom pathlib import Path\nimport torch\nimport train_current_model as train\n\np = argparse.ArgumentParser()\np.add_argument('--checkpoint', type=Path, required=True)\np.add_argument('--stage', choices=['validation', 'submission'], required=True)\np.add_argument('--output', type=Path, required=True)\np.add_argument('--threads', type=int, default=4)\na = p.parse_args()\ntorch.set_num_threads(a.threads)\ntorch.set_num_interop_threads(1)\npayload = torch.load(a.checkpoint, map_location='cpu', weights_only=False)\nconfig = train.Config(**payload['config'])\ndata, eligible = train.load_or_prepare(Path('/task'), a.stage, config, Path('/app/solution') / (a.stage + '_cache.npz'))\nmodel = train.base.SequentialTwoTower(data.item_features, train.model_config(config))\nmodel.load_state_dict(payload['model_state'])\nmodel.eval()\nframe, diag = train.rank_candidates(model, data, eligible, config, torch.device('cpu'), 64)\ntrain.write_ranking(frame, a.output)\nprint(diag)\nPY\n/opt/task-venv/bin/python /app/solution/check_resources.py && tail -5 /app/solution/hard.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat > /app/solution/rank_checkpoint.py <<'PY'\n\"\"\"Rank an existing two-tower checkpoint using the cutoff-specific public history.\"\"\"\nimport argparse\nfrom pathlib import Path\nimport torch\nimport train_current_model as train\n\np = argparse.ArgumentParser()\np.add_argument('--checkpoint', type=Path, required=True)\np.add_argument('--stage', choices=['validation', 'submission'], required=True)\np.add_argument('--output', type=Path, required=True)\np.add_argument('--threads', type=int, default=4)\na = p.parse_args()\ntorch.set_num_threads(a.threads)\ntorch.set_num_interop_threads(1)\npayload = torch.load(a.checkpoint, map_location='cpu', weights_only=False)\nconfig = train.Config(**payload['config'])\ndata, eligible = train.load_or_prepare(Path('/task'), a.stage, config, Path('/app/solution') / (a.stage + '_cache.npz'))\nmodel = train.base.SequentialTwoTower(data.item_features, train.model_config(config))\nmodel.load_state_dict(payload['model_state'])\nmodel.eval()\nframe, diag = train.rank_candidates(model, data, eligible, config, torch.device('cpu'), 64)\ntrain.write_ranking(frame, a.output)\nprint(diag)\nPY\n/opt/task-venv/bin/python /app/solution/check_resources.py && tail -5 /app/solution/hard.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "267 /bin/sh -c OPENBLAS_NUM_THREADS=1 OMP_NUM_THREADS=4 PYTHONDONTWRITEBYTECODE=1 /opt/task-venv/bin/python /task/current_model/train_current_model.py --stage valid\nVmRSS:\t 1800 kB\nThreads:\t1\n268 /opt/task-venv/bin/python /task/current_model/train_current_model.py --stage validation --public-dir /task --output /app/solution/production_validation.parquet \nVmRSS:\t 4660100 kB\nThreads:\t13\n360 /bin/sh -c OPENBLAS_NUM_THREADS=1 OMP_NUM_THREADS=4 PYTHONDONTWRITEBYTECODE=1 /opt/task-venv/bin/python /app/solution/train_current_model.py --stage validation \nVmRSS:\t 1560 kB\nThreads:\t1\n361 /opt/task-venv/bin/python /app/solution/train_current_model.py --stage validation --public-dir /task --output /app/solution/hard_validation.parquet --prepared-c\nVmRSS:\t 1821960 kB\nThreads:\t8\n6941253632\n\nloaded 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\n", "stderr": "", "aggregated_output": "267 /bin/sh -c OPENBLAS_NUM_THREADS=1 OMP_NUM_THREADS=4 PYTHONDONTWRITEBYTECODE=1 /opt/task-venv/bin/python /task/current_model/train_current_model.py --stage valid\nVmRSS:\t 1800 kB\nThreads:\t1\n268 /opt/task-venv/bin/python /task/current_model/train_current_model.py --stage validation --public-dir /task --output /app/solution/production_validation.parquet \nVmRSS:\t 4660100 kB\nThreads:\t13\n360 /bin/sh -c OPENBLAS_NUM_THREADS=1 OMP_NUM_THREADS=4 PYTHONDONTWRITEBYTECODE=1 /opt/task-venv/bin/python /app/solution/train_current_model.py --stage validation \nVmRSS:\t 1560 kB\nThreads:\t1\n361 /opt/task-venv/bin/python /app/solution/train_current_model.py --stage validation --public-dir /task --output /app/solution/hard_validation.parquet --prepared-c\nVmRSS:\t 1821960 kB\nThreads:\t8\n6941253632\n\nloaded 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\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 71420936 }, "formatted_output": "267 /bin/sh -c OPENBLAS_NUM_THREADS=1 OMP_NUM_THREADS=4 PYTHONDONTWRITEBYTECODE=1 /opt/task-venv/bin/python /task/current_model/train_current_model.py --stage valid\nVmRSS:\t 1800 kB\nThreads:\t1\n268 /opt/task-venv/bin/python /task/current_model/train_current_model.py --stage validation --public-dir /task --output /app/solution/production_validation.parquet \nVmRSS:\t 4660100 kB\nThreads:\t13\n360 /bin/sh -c OPENBLAS_NUM_THREADS=1 OMP_NUM_THREADS=4 PYTHONDONTWRITEBYTECODE=1 /opt/task-venv/bin/python /app/solution/train_current_model.py --stage validation \nVmRSS:\t 1560 kB\nThreads:\t1\n361 /opt/task-venv/bin/python /app/solution/train_current_model.py --stage validation --public-dir /task --output /app/solution/hard_validation.parquet --prepared-c\nVmRSS:\t 1821960 kB\nThreads:\t8\n6941253632\n\nloaded 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\n" } } ## Step 14 · agent · 2026-09-23T05:22:23.648Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T05:22:47.982Z { "code": "text(await tools.exec_command({cmd:\"cat > /app/solution/test_hard_negatives.py <<'PY'\\n\\\"\\\"\\\"Check mined-negative exclusions, uniqueness, and end-to-end gradients.\\\"\\\"\\\"\\nfrom types import SimpleNamespace\\nimport numpy as np\\nimport torch\\nimport train_current_model as t\\n\\ndef test_mining():\\n torch.set_num_threads(1)\\n t.seed_everything(5)\\n config = t.Config(embedding_dim=8, hidden_dim=8, user_hidden_1=16,\\n user_hidden_2=12, item_hidden_1=16, item_hidden_2=12,\\n uniform_negatives=3, inbatch_negatives=2, hard_negatives=4,\\n hard_pool_size=128, loss_microbatch_size=2)\\n data = SimpleNamespace(item_ids=np.arange(64),\\n liked_states=np.array([[6, 64], [7, 64]], dtype=np.uint16),\\n disliked_states=np.array([[64], [64]], dtype=np.uint16),\\n state_counts=np.array([[1, 0], [1, 0]]))\\n model = t.base.SequentialTwoTower(np.zeros((64, 4), np.float32), t.model_config(config))\\n history = np.zeros((2, 64), dtype=np.uint8)\\n history[0, [0, 1, 8, 9]] = 1\\n history[1, [2, 3, 10, 11]] = 1\\n packed = np.packbits(history, axis=1, bitorder='little')\\n random = torch.tensor([[12, 13, 14, 15, 16], [17, 18, 19, 20, 21]])\\n original = t.candidate_vectors\\n captured = []\\n def capture(model, positives, negatives):\\n captured.append(negatives.clone())\\n return original(model, positives, negatives)\\n t.candidate_vectors = capture\\n stats, grad = t.score_target_microbatches(model, data, config,\\n torch.randn(2, 8), np.array([0, 1]), np.zeros((2, 4), np.float32),\\n torch.tensor([4, 5]), random, torch.ones((2, 5)), np.ones(2, np.float32),\\n torch.device('cpu'), True, np.array([0, 1]), packed,\\n torch.Generator().manual_seed(9))\\n t.candidate_vectors = original\\n selected = captured[0].numpy()\\n for row in range(2):\\n assert len(np.unique(selected[row])) == 9\\n forbidden = set(np.flatnonzero(history[row])) | {4 + row, 6 + row}\\n assert not forbidden.intersection(selected[row])\\n assert np.array_equal(selected[row, :5], random[row].numpy())\\n assert np.isfinite(stats['loss_sum'])\\n assert torch.isfinite(grad).all() and grad.abs().sum() > 0\\n assert all(torch.isfinite(p.grad).all() for p in model.parameters() if p.grad is not None)\\n print('PASS: prefix positives, likes, target, duplicates excluded; finite nonzero gradients')\\n\\nif __name__ == '__main__':\\n test_mining()\\nPY\\nPYTHONDONTWRITEBYTECODE=1 /opt/task-venv/bin/python /app/solution/test_hard_negatives.py && tail -3 /app/solution/hard.log && tail -3 /app/solution/baseline.log\",yield_time_ms:1000,max_output_tokens:1000}));\n" } ### Tool output · 2026-09-23T05:22:49.174Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"622db1","wall_time_seconds":1.002446929,"session_id":86364,"original_token_count":0,"output":""} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "session_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "root_turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "response_id": "resp_021598e9bafc9da7016ab3620830f487d2b6096a555daaa1cd", "usage": { "input_tokens": 50068, "cached_input_tokens": 49024, "cache_write_input_tokens": 0, "output_tokens": 991, "reasoning_output_tokens": 169, "total_tokens": 51059 }, "turn_token_usage": { "input_tokens": 488484, "cached_input_tokens": 448512, "cache_write_input_tokens": 0, "output_tokens": 5122, "reasoning_output_tokens": 933, "total_tokens": 493606 }, "thread_token_usage": { "input_tokens": 488484, "cached_input_tokens": 448512, "cache_write_input_tokens": 0, "output_tokens": 5122, "reasoning_output_tokens": 933, "total_tokens": 493606 } } ## Step 15 · agent · 2026-09-23T05:22:50.786Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T05:22:53.146Z { "code": "text(await tools.clock__curr_time({}));\ntext(await tools.exec_command({cmd:\"tail -4 /app/solution/hard.log && tail -3 /app/solution/baseline.log\",max_output_tokens:1000}));\ntext(await tools.write_stdin({session_id:86364,chars:\"\",yield_time_ms:1000,max_output_tokens:1000}));\n" } ### Tool output · 2026-09-23T05:22:54.024Z Script completed Wall time 0.9 seconds Output: {"current_time":"2026-09-23 05:22:53 UTC"} {"chunk_id":"eff700","wall_time_seconds":0.000005,"exit_code":0,"original_token_count":141,"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\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"} {"chunk_id":"f72439","wall_time_seconds":0.819232013,"exit_code":0,"original_token_count":131,"output":"PASS: prefix positives, likes, target, duplicates excluded; finite nonzero gradients\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\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-b23f-76c2-905d-9e8b0e97f817", "turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "session_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "root_turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "response_id": "resp_021598e9bafc9da7016ab36229681487d29a7cca3e48cadd51", "usage": { "input_tokens": 51116, "cached_input_tokens": 49920, "cache_write_input_tokens": 0, "output_tokens": 97, "reasoning_output_tokens": 9, "total_tokens": 51213 }, "turn_token_usage": { "input_tokens": 539600, "cached_input_tokens": 498432, "cache_write_input_tokens": 0, "output_tokens": 5219, "reasoning_output_tokens": 942, "total_tokens": 544819 }, "thread_token_usage": { "input_tokens": 539600, "cached_input_tokens": 498432, "cache_write_input_tokens": 0, "output_tokens": 5219, "reasoning_output_tokens": 942, "total_tokens": 544819 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-76033ee8-4d64-4de3-8d9d-f8f00b3afdd7", "process_id": "36678", "command": [ "/bin/sh", "-lc", "tail -4 /app/solution/hard.log && tail -3 /app/solution/baseline.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -4 /app/solution/hard.log && tail -3 /app/solution/baseline.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\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\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": 5000 }, "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\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": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-b651c1f0-a581-460f-833c-0bc7e8bb47a4", "process_id": "86364", "command": [ "/bin/sh", "-lc", "cat > /app/solution/test_hard_negatives.py <<'PY'\n\"\"\"Check mined-negative exclusions, uniqueness, and end-to-end gradients.\"\"\"\nfrom types import SimpleNamespace\nimport numpy as np\nimport torch\nimport train_current_model as t\n\ndef test_mining():\n torch.set_num_threads(1)\n t.seed_everything(5)\n config = t.Config(embedding_dim=8, hidden_dim=8, user_hidden_1=16,\n user_hidden_2=12, item_hidden_1=16, item_hidden_2=12,\n uniform_negatives=3, inbatch_negatives=2, hard_negatives=4,\n hard_pool_size=128, loss_microbatch_size=2)\n data = SimpleNamespace(item_ids=np.arange(64),\n liked_states=np.array([[6, 64], [7, 64]], dtype=np.uint16),\n disliked_states=np.array([[64], [64]], dtype=np.uint16),\n state_counts=np.array([[1, 0], [1, 0]]))\n model = t.base.SequentialTwoTower(np.zeros((64, 4), np.float32), t.model_config(config))\n history = np.zeros((2, 64), dtype=np.uint8)\n history[0, [0, 1, 8, 9]] = 1\n history[1, [2, 3, 10, 11]] = 1\n packed = np.packbits(history, axis=1, bitorder='little')\n random = torch.tensor([[12, 13, 14, 15, 16], [17, 18, 19, 20, 21]])\n original = t.candidate_vectors\n captured = []\n def capture(model, positives, negatives):\n captured.append(negatives.clone())\n return original(model, positives, negatives)\n t.candidate_vectors = capture\n stats, grad = t.score_target_microbatches(model, data, config,\n torch.randn(2, 8), np.array([0, 1]), np.zeros((2, 4), np.float32),\n torch.tensor([4, 5]), random, torch.ones((2, 5)), np.ones(2, np.float32),\n torch.device('cpu'), True, np.array([0, 1]), packed,\n torch.Generator().manual_seed(9))\n t.candidate_vectors = original\n selected = captured[0].numpy()\n for row in range(2):\n assert len(np.unique(selected[row])) == 9\n forbidden = set(np.flatnonzero(history[row])) | {4 + row, 6 + row}\n assert not forbidden.intersection(selected[row])\n assert np.array_equal(selected[row, :5], random[row].numpy())\n assert np.isfinite(stats['loss_sum'])\n assert torch.isfinite(grad).all() and grad.abs().sum() > 0\n assert all(torch.isfinite(p.grad).all() for p in model.parameters() if p.grad is not None)\n print('PASS: prefix positives, likes, target, duplicates excluded; finite nonzero gradients')\n\nif __name__ == '__main__':\n test_mining()\nPY\nPYTHONDONTWRITEBYTECODE=1 /opt/task-venv/bin/python /app/solution/test_hard_negatives.py && tail -3 /app/solution/hard.log && tail -3 /app/solution/baseline.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat > /app/solution/test_hard_negatives.py <<'PY'\n\"\"\"Check mined-negative exclusions, uniqueness, and end-to-end gradients.\"\"\"\nfrom types import SimpleNamespace\nimport numpy as np\nimport torch\nimport train_current_model as t\n\ndef test_mining():\n torch.set_num_threads(1)\n t.seed_everything(5)\n config = t.Config(embedding_dim=8, hidden_dim=8, user_hidden_1=16,\n user_hidden_2=12, item_hidden_1=16, item_hidden_2=12,\n uniform_negatives=3, inbatch_negatives=2, hard_negatives=4,\n hard_pool_size=128, loss_microbatch_size=2)\n data = SimpleNamespace(item_ids=np.arange(64),\n liked_states=np.array([[6, 64], [7, 64]], dtype=np.uint16),\n disliked_states=np.array([[64], [64]], dtype=np.uint16),\n state_counts=np.array([[1, 0], [1, 0]]))\n model = t.base.SequentialTwoTower(np.zeros((64, 4), np.float32), t.model_config(config))\n history = np.zeros((2, 64), dtype=np.uint8)\n history[0, [0, 1, 8, 9]] = 1\n history[1, [2, 3, 10, 11]] = 1\n packed = np.packbits(history, axis=1, bitorder='little')\n random = torch.tensor([[12, 13, 14, 15, 16], [17, 18, 19, 20, 21]])\n original = t.candidate_vectors\n captured = []\n def capture(model, positives, negatives):\n captured.append(negatives.clone())\n return original(model, positives, negatives)\n t.candidate_vectors = capture\n stats, grad = t.score_target_microbatches(model, data, config,\n torch.randn(2, 8), np.array([0, 1]), np.zeros((2, 4), np.float32),\n torch.tensor([4, 5]), random, torch.ones((2, 5)), np.ones(2, np.float32),\n torch.device('cpu'), True, np.array([0, 1]), packed,\n torch.Generator().manual_seed(9))\n t.candidate_vectors = original\n selected = captured[0].numpy()\n for row in range(2):\n assert len(np.unique(selected[row])) == 9\n forbidden = set(np.flatnonzero(history[row])) | {4 + row, 6 + row}\n assert not forbidden.intersection(selected[row])\n assert np.array_equal(selected[row, :5], random[row].numpy())\n assert np.isfinite(stats['loss_sum'])\n assert torch.isfinite(grad).all() and grad.abs().sum() > 0\n assert all(torch.isfinite(p.grad).all() for p in model.parameters() if p.grad is not None)\n print('PASS: prefix positives, likes, target, duplicates excluded; finite nonzero gradients')\n\nif __name__ == '__main__':\n test_mining()\nPY\nPYTHONDONTWRITEBYTECODE=1 /opt/task-venv/bin/python /app/solution/test_hard_negatives.py && tail -3 /app/solution/hard.log && tail -3 /app/solution/baseline.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "PASS: prefix positives, likes, target, duplicates excluded; finite nonzero gradients\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\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": "PASS: prefix positives, likes, target, duplicates excluded; finite nonzero gradients\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\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": 5, "nanos": 853792615 }, "formatted_output": "PASS: prefix positives, likes, target, duplicates excluded; finite nonzero gradients\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\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 16 · agent · 2026-09-23T05:23:12.036Z ### Thinking unavailable — no text recorded The sampler passed a focused test: it excludes prefix positives, active likes, the current target, and duplicate negatives, and produces finite gradients. The baseline and hard-negative runs are now training on the same chronological partition; public NDCG@10 will determine whether the change helps. ### exec · 2026-09-23T05:23:13.852Z { "code": "text(await tools.exec_command({cmd:\"tail -5 /app/solution/hard.log && tail -4 /app/solution/baseline.log && /opt/task-venv/bin/python /app/solution/check_resources.py\",max_output_tokens:1800}));\n" } ### Tool output · 2026-09-23T05:23:14.062Z Script completed Wall time 0.2 seconds Output: {"chunk_id":"16b878","wall_time_seconds":0.00000499,"exit_code":0,"original_token_count":394,"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=25 targets=104,067 batch_bce=0.692562 running_bce=0.693772 elapsed=61.3s\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\n267 /bin/sh -c OPENBLAS_NUM_THREADS=1 OMP_NUM_THREADS=4 PYTHONDONTWRITEBYTECODE=1 /opt/task-venv/bin/python /task/current_model/train_current_model.py --stage valid\nVmRSS:\t 1800 kB\nThreads:\t1\n268 /opt/task-venv/bin/python /task/current_model/train_current_model.py --stage validation --public-dir /task --output /app/solution/production_validation.parquet \nVmRSS:\t 4857292 kB\nThreads:\t13\n360 /bin/sh -c OPENBLAS_NUM_THREADS=1 OMP_NUM_THREADS=4 PYTHONDONTWRITEBYTECODE=1 /opt/task-venv/bin/python /app/solution/train_current_model.py --stage validation \nVmRSS:\t 1560 kB\nThreads:\t1\n361 /opt/task-venv/bin/python /app/solution/train_current_model.py --stage validation --public-dir /task --output /app/solution/hard_validation.parquet --prepared-c\nVmRSS:\t 3235896 kB\nThreads:\t8\n8134950912\n\n"} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "session_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "root_turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "response_id": "resp_021598e9bafc9da7016ab3622e319887d2ada152a357398ecf", "usage": { "input_tokens": 51666, "cached_input_tokens": 50944, "cache_write_input_tokens": 0, "output_tokens": 599, "reasoning_output_tokens": 470, "total_tokens": 52265 }, "turn_token_usage": { "input_tokens": 591266, "cached_input_tokens": 549376, "cache_write_input_tokens": 0, "output_tokens": 5818, "reasoning_output_tokens": 1412, "total_tokens": 597084 }, "thread_token_usage": { "input_tokens": 591266, "cached_input_tokens": 549376, "cache_write_input_tokens": 0, "output_tokens": 5818, "reasoning_output_tokens": 1412, "total_tokens": 597084 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-7446bef5-f46c-48db-b950-e06eebc923c3", "process_id": "86576", "command": [ "/bin/sh", "-lc", "tail -5 /app/solution/hard.log && tail -4 /app/solution/baseline.log && /opt/task-venv/bin/python /app/solution/check_resources.py" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -5 /app/solution/hard.log && tail -4 /app/solution/baseline.log && /opt/task-venv/bin/python /app/solution/check_resources.py" } ], "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=25 targets=104,067 batch_bce=0.692562 running_bce=0.693772 elapsed=61.3s\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\n267 /bin/sh -c OPENBLAS_NUM_THREADS=1 OMP_NUM_THREADS=4 PYTHONDONTWRITEBYTECODE=1 /opt/task-venv/bin/python /task/current_model/train_current_model.py --stage valid\nVmRSS:\t 1800 kB\nThreads:\t1\n268 /opt/task-venv/bin/python /task/current_model/train_current_model.py --stage validation --public-dir /task --output /app/solution/production_validation.parquet \nVmRSS:\t 4857292 kB\nThreads:\t13\n360 /bin/sh -c OPENBLAS_NUM_THREADS=1 OMP_NUM_THREADS=4 PYTHONDONTWRITEBYTECODE=1 /opt/task-venv/bin/python /app/solution/train_current_model.py --stage validation \nVmRSS:\t 1560 kB\nThreads:\t1\n361 /opt/task-venv/bin/python /app/solution/train_current_model.py --stage validation --public-dir /task --output /app/solution/hard_validation.parquet --prepared-c\nVmRSS:\t 3235896 kB\nThreads:\t8\n8134950912\n\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=25 targets=104,067 batch_bce=0.692562 running_bce=0.693772 elapsed=61.3s\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\n267 /bin/sh -c OPENBLAS_NUM_THREADS=1 OMP_NUM_THREADS=4 PYTHONDONTWRITEBYTECODE=1 /opt/task-venv/bin/python /task/current_model/train_current_model.py --stage valid\nVmRSS:\t 1800 kB\nThreads:\t1\n268 /opt/task-venv/bin/python /task/current_model/train_current_model.py --stage validation --public-dir /task --output /app/solution/production_validation.parquet \nVmRSS:\t 4857292 kB\nThreads:\t13\n360 /bin/sh -c OPENBLAS_NUM_THREADS=1 OMP_NUM_THREADS=4 PYTHONDONTWRITEBYTECODE=1 /opt/task-venv/bin/python /app/solution/train_current_model.py --stage validation \nVmRSS:\t 1560 kB\nThreads:\t1\n361 /opt/task-venv/bin/python /app/solution/train_current_model.py --stage validation --public-dir /task --output /app/solution/hard_validation.parquet --prepared-c\nVmRSS:\t 3235896 kB\nThreads:\t8\n8134950912\n\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4990 }, "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=25 targets=104,067 batch_bce=0.692562 running_bce=0.693772 elapsed=61.3s\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\n267 /bin/sh -c OPENBLAS_NUM_THREADS=1 OMP_NUM_THREADS=4 PYTHONDONTWRITEBYTECODE=1 /opt/task-venv/bin/python /task/current_model/train_current_model.py --stage valid\nVmRSS:\t 1800 kB\nThreads:\t1\n268 /opt/task-venv/bin/python /task/current_model/train_current_model.py --stage validation --public-dir /task --output /app/solution/production_validation.parquet \nVmRSS:\t 4857292 kB\nThreads:\t13\n360 /bin/sh -c OPENBLAS_NUM_THREADS=1 OMP_NUM_THREADS=4 PYTHONDONTWRITEBYTECODE=1 /opt/task-venv/bin/python /app/solution/train_current_model.py --stage validation \nVmRSS:\t 1560 kB\nThreads:\t1\n361 /opt/task-venv/bin/python /app/solution/train_current_model.py --stage validation --public-dir /task --output /app/solution/hard_validation.parquet --prepared-c\nVmRSS:\t 3235896 kB\nThreads:\t8\n8134950912\n\n" } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-04263e0e-18d7-46f4-b5b7-6e0425a3b9a4", "process_id": "8117", "command": [ "/bin/sh", "-lc", "OPENBLAS_NUM_THREADS=1 OMP_NUM_THREADS=4 PYTHONDONTWRITEBYTECODE=1 /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 4 --loss-microbatch-size 1024 > /app/solution/hard.json 2> /app/solution/hard.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "OPENBLAS_NUM_THREADS=1 OMP_NUM_THREADS=4 PYTHONDONTWRITEBYTECODE=1 /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 4 --loss-microbatch-size 1024 > /app/solution/hard.json 2> /app/solution/hard.log" } ], "source": "unified_exec_startup", "status": "failed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 137, "duration": { "secs": 79, "nanos": 550908913 }, "formatted_output": "" } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-1ce88811-88e3-40d8-b051-492bc088edab", "process_id": "84315", "command": [ "/bin/sh", "-lc", "OPENBLAS_NUM_THREADS=1 OMP_NUM_THREADS=4 PYTHONDONTWRITEBYTECODE=1 /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/baseline_ckpt --threads 4 > /app/solution/baseline.json 2> /app/solution/baseline.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "OPENBLAS_NUM_THREADS=1 OMP_NUM_THREADS=4 PYTHONDONTWRITEBYTECODE=1 /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/baseline_ckpt --threads 4 > /app/solution/baseline.json 2> /app/solution/baseline.log" } ], "source": "unified_exec_startup", "status": "failed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 137, "duration": { "secs": 182, "nanos": 632455603 }, "formatted_output": "" } } ## Step 17 · agent · 2026-09-23T05:23:28.704Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T05:23:30.518Z { "code": "text(await tools.exec_command({cmd:\"tail -3 /app/solution/hard.log && tail -3 /app/solution/baseline.log && ls -lh /app/solution/baseline_ckpt\",max_output_tokens:1000}));\ntext(await tools.clock__curr_time({}));\n" } ### Tool output · 2026-09-23T05:23:30.592Z Script completed Wall time 0.1 seconds Output: {"chunk_id":"f8a852","wall_time_seconds":0.000003751,"exit_code":2,"original_token_count":108,"output":"full-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250\nepoch=1/1 batches=25 targets=104,067 batch_bce=0.692562 running_bce=0.693772 elapsed=61.3s\nKilled\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\nKilled\nls: cannot access '/app/solution/baseline_ckpt': No such file or directory\n"} {"current_time":"2026-09-23 05:23:30 UTC"} ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-a6def96c-0212-4c7a-831d-daf9fded4d23", "process_id": "42181", "command": [ "/bin/sh", "-lc", "tail -3 /app/solution/hard.log && tail -3 /app/solution/baseline.log && ls -lh /app/solution/baseline_ckpt" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -3 /app/solution/hard.log && tail -3 /app/solution/baseline.log && ls -lh /app/solution/baseline_ckpt" } ], "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=25 targets=104,067 batch_bce=0.692562 running_bce=0.693772 elapsed=61.3s\nKilled\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\nKilled\nls: cannot access '/app/solution/baseline_ckpt': 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=25 targets=104,067 batch_bce=0.692562 running_bce=0.693772 elapsed=61.3s\nKilled\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\nKilled\nls: cannot access '/app/solution/baseline_ckpt': No such file or directory\n", "exit_code": 2, "duration": { "secs": 0, "nanos": 3751 }, "formatted_output": "full-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250\nepoch=1/1 batches=25 targets=104,067 batch_bce=0.692562 running_bce=0.693772 elapsed=61.3s\nKilled\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\nKilled\nls: cannot access '/app/solution/baseline_ckpt': No such file or directory\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "session_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "root_turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "response_id": "resp_021598e9bafc9da7016ab362424fd087d2819d9040a68a4d6d", "usage": { "input_tokens": 52830, "cached_input_tokens": 51456, "cache_write_input_tokens": 0, "output_tokens": 511, "reasoning_output_tokens": 439, "total_tokens": 53341 }, "turn_token_usage": { "input_tokens": 644096, "cached_input_tokens": 600832, "cache_write_input_tokens": 0, "output_tokens": 6329, "reasoning_output_tokens": 1851, "total_tokens": 650425 }, "thread_token_usage": { "input_tokens": 644096, "cached_input_tokens": 600832, "cache_write_input_tokens": 0, "output_tokens": 6329, "reasoning_output_tokens": 1851, "total_tokens": 650425 } } ## Step 18 · agent · 2026-09-23T05:23:36.641Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T05:23:42.888Z { "code": "text(await tools.exec_command({cmd:\"cp /task/current_model/train_current_model.py /app/solution/train_production.py && chmod u+w /app/solution/train_production.py && /opt/task-venv/bin/python -c \\\"from pathlib import Path; p=Path('/app/solution/train_production.py'); p.write_text(p.read_text().replace('batches % 250 == 0', 'batches % 25 == 0'))\\\" && OPENBLAS_NUM_THREADS=1 OMP_NUM_THREADS=4 PYTHONDONTWRITEBYTECODE=1 /opt/task-venv/bin/python /app/solution/train_production.py --stage validation --public-dir /task --output /app/solution/production_validation.parquet --prepared-cache /app/solution/validation_cache.npz --checkpoint-dir /app/solution/baseline_ckpt --threads 4 > /app/solution/baseline.json 2> /app/solution/baseline.log\",yield_time_ms:1000}));\n" } ### Tool output · 2026-09-23T05:23:44.064Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"5c7731","wall_time_seconds":1.00140929,"session_id":30148,"original_token_count":0,"output":""} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "session_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "root_turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "response_id": "resp_021598e9bafc9da7016ab3625318fc87d28856484363ec72ef", "usage": { "input_tokens": 53555, "cached_input_tokens": 52608, "cache_write_input_tokens": 0, "output_tokens": 358, "reasoning_output_tokens": 137, "total_tokens": 53913 }, "turn_token_usage": { "input_tokens": 697651, "cached_input_tokens": 653440, "cache_write_input_tokens": 0, "output_tokens": 6687, "reasoning_output_tokens": 1988, "total_tokens": 704338 }, "thread_token_usage": { "input_tokens": 697651, "cached_input_tokens": 653440, "cache_write_input_tokens": 0, "output_tokens": 6687, "reasoning_output_tokens": 1988, "total_tokens": 704338 } } ## Step 19 · agent · 2026-09-23T05:23:45.920Z ### Thinking unavailable — no text recorded Running both trainers concurrently exceeded the container’s 8 GB memory limit, so both processes were killed. The prepared data cache survived. I’ve restarted the baseline on its own and will run subsequent training jobs serially with bounded loss batches. ## Step 20 · agent · 2026-09-23T05:24:03.286Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T05:24:04.675Z { "code": "text(await tools.exec_command({cmd:\"tail -4 /app/solution/baseline.log && cat /sys/fs/cgroup/memory.current\",max_output_tokens:1000}));\ntext(await tools.clock__curr_time({}));\n" } ### Tool output · 2026-09-23T05:24:04.745Z Script completed Wall time 0.1 seconds Output: {"chunk_id":"512293","wall_time_seconds":0.00000494,"exit_code":0,"original_token_count":90,"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\n2650652672\n"} {"current_time":"2026-09-23 05:24:04 UTC"} ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-4735cb75-de66-4f30-9384-4d0ccf46a7cc", "process_id": "26983", "command": [ "/bin/sh", "-lc", "tail -4 /app/solution/baseline.log && cat /sys/fs/cgroup/memory.current" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -4 /app/solution/baseline.log && cat /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\n2650652672\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\n2650652672\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4940 }, "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\n2650652672\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "session_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "root_turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "response_id": "resp_021598e9bafc9da7016ab36260973887d2a90d029c336a2446", "usage": { "input_tokens": 53971, "cached_input_tokens": 53376, "cache_write_input_tokens": 0, "output_tokens": 640, "reasoning_output_tokens": 528, "total_tokens": 54611 }, "turn_token_usage": { "input_tokens": 751622, "cached_input_tokens": 706816, "cache_write_input_tokens": 0, "output_tokens": 7327, "reasoning_output_tokens": 2516, "total_tokens": 758949 }, "thread_token_usage": { "input_tokens": 751622, "cached_input_tokens": 706816, "cache_write_input_tokens": 0, "output_tokens": 7327, "reasoning_output_tokens": 2516, "total_tokens": 758949 } } ## Step 21 · agent · 2026-09-23T05:24:18.385Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T05:24:19.876Z { "code": "text(await tools.exec_command({cmd:\"sed -n '1570,1608p' /app/solution/train_current_model.py && tail -3 /app/solution/baseline.log\",max_output_tokens:1200}));\n" } ### Tool output · 2026-09-23T05:24:19.934Z Script completed Wall time 0.1 seconds Output: {"chunk_id":"7ddd15","wall_time_seconds":0.00000294,"exit_code":0,"original_token_count":395,"output":" \"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\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=25 targets=104,067 batch_bce=0.580409 running_bce=0.599170 elapsed=21.3s\n"} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "session_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "root_turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "response_id": "resp_021598e9bafc9da7016ab362751bf087d2bd647008da474f15", "usage": { "input_tokens": 54802, "cached_input_tokens": 53760, "cache_write_input_tokens": 0, "output_tokens": 451, "reasoning_output_tokens": 392, "total_tokens": 55253 }, "turn_token_usage": { "input_tokens": 806424, "cached_input_tokens": 760576, "cache_write_input_tokens": 0, "output_tokens": 7778, "reasoning_output_tokens": 2908, "total_tokens": 814202 }, "thread_token_usage": { "input_tokens": 806424, "cached_input_tokens": 760576, "cache_write_input_tokens": 0, "output_tokens": 7778, "reasoning_output_tokens": 2908, "total_tokens": 814202 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-fe910c44-ad21-4ce7-8ad4-25557be9bf70", "process_id": "75083", "command": [ "/bin/sh", "-lc", "sed -n '1570,1608p' /app/solution/train_current_model.py && tail -3 /app/solution/baseline.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "sed -n '1570,1608p' /app/solution/train_current_model.py && tail -3 /app/solution/baseline.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": " \"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\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=25 targets=104,067 batch_bce=0.580409 running_bce=0.599170 elapsed=21.3s\n", "stderr": "", "aggregated_output": " \"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\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=25 targets=104,067 batch_bce=0.580409 running_bce=0.599170 elapsed=21.3s\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 2940 }, "formatted_output": " \"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\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=25 targets=104,067 batch_bce=0.580409 running_bce=0.599170 elapsed=21.3s\n" } } ## Step 22 · agent · 2026-09-23T05:24:29.971Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T05:24:45.915Z { "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(''' 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\\\")''',''' temporary = output.with_name(output.stem + \\\".tmp\\\" + output.suffix)\\n if output.suffix == \\\".parquet\\\":\\n frame.to_parquet(temporary, index=False, compression=\\\"zstd\\\")\\n elif output.suffix == \\\".csv\\\":\\n frame.to_csv(temporary, index=False)\\n else:\\n raise ValueError(\\\"--output must end in .parquet or .csv\\\")\\n temporary.replace(output)''')\\ns=s.replace('statistics[\\\"negative_sum\\\"] += float(negative_logits.detach().sum().cpu())', 'statistics[\\\"negative_sum\\\"] += float(negative_logits[:, :random_count].detach().sum().cpu())')\\ns=s.replace('(positive_logits.detach()[:, None] > negative_logits.detach()).sum().cpu()', '(positive_logits.detach()[:, None] > negative_logits[:, :random_count].detach()).sum().cpu()')\\ns=s.replace(''' if args.threads < 1 or args.bptt_steps < 2 or args.user_batch_size < 1:''',''' if (args.hard_negatives < 0 or args.hard_pool_size < args.hard_negatives\\n or not 0.0 <= args.hard_loss_weight <= 1.0):\\n parser.error(\\\"hard-negative count, pool size, or loss weight is invalid\\\")\\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.threads < 1 or args.bptt_steps < 2 or args.user_batch_size < 1:''')\\np.write_text(s)\\nPY\\nPYTHONDONTWRITEBYTECODE=1 /opt/task-venv/bin/python /app/solution/test_hard_negatives.py\",yield_time_ms:1000,max_output_tokens:1000}));\n" } ### Tool output · 2026-09-23T05:24:47.088Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"8a3251","wall_time_seconds":1.001199708,"session_id":11038,"original_token_count":0,"output":""} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "session_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "root_turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "response_id": "resp_021598e9bafc9da7016ab36284369487d2a26d44edce76c6d4", "usage": { "input_tokens": 55731, "cached_input_tokens": 54656, "cache_write_input_tokens": 0, "output_tokens": 763, "reasoning_output_tokens": 222, "total_tokens": 56494 }, "turn_token_usage": { "input_tokens": 862155, "cached_input_tokens": 815232, "cache_write_input_tokens": 0, "output_tokens": 8541, "reasoning_output_tokens": 3130, "total_tokens": 870696 }, "thread_token_usage": { "input_tokens": 862155, "cached_input_tokens": 815232, "cache_write_input_tokens": 0, "output_tokens": 8541, "reasoning_output_tokens": 3130, "total_tokens": 870696 } } ### sleep · 2026-09-23T05:24:49.160Z { "duration_ms": 45000 } ### Tool output · 2026-09-23T05:25:34.179Z Wall time: 45.0104 seconds Sleep completed. ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "session_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "root_turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "response_id": "resp_021598e9bafc9da7016ab3629f4a9c87d293fc83cd43647f41", "usage": { "input_tokens": 56552, "cached_input_tokens": 55552, "cache_write_input_tokens": 0, "output_tokens": 19, "reasoning_output_tokens": 0, "total_tokens": 56571 }, "turn_token_usage": { "input_tokens": 918707, "cached_input_tokens": 870784, "cache_write_input_tokens": 0, "output_tokens": 8560, "reasoning_output_tokens": 3130, "total_tokens": 927267 }, "thread_token_usage": { "input_tokens": 918707, "cached_input_tokens": 870784, "cache_write_input_tokens": 0, "output_tokens": 8560, "reasoning_output_tokens": 3130, "total_tokens": 927267 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-0652f8fb-3820-4d22-a3c7-8e667f55e891", "process_id": "11038", "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(''' 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\")''',''' temporary = output.with_name(output.stem + \".tmp\" + output.suffix)\n if output.suffix == \".parquet\":\n frame.to_parquet(temporary, index=False, compression=\"zstd\")\n elif output.suffix == \".csv\":\n frame.to_csv(temporary, index=False)\n else:\n raise ValueError(\"--output must end in .parquet or .csv\")\n temporary.replace(output)''')\ns=s.replace('statistics[\"negative_sum\"] += float(negative_logits.detach().sum().cpu())', 'statistics[\"negative_sum\"] += float(negative_logits[:, :random_count].detach().sum().cpu())')\ns=s.replace('(positive_logits.detach()[:, None] > negative_logits.detach()).sum().cpu()', '(positive_logits.detach()[:, None] > negative_logits[:, :random_count].detach()).sum().cpu()')\ns=s.replace(''' if args.threads < 1 or args.bptt_steps < 2 or args.user_batch_size < 1:''',''' if (args.hard_negatives < 0 or args.hard_pool_size < args.hard_negatives\n or not 0.0 <= args.hard_loss_weight <= 1.0):\n parser.error(\"hard-negative count, pool size, or loss weight is invalid\")\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.threads < 1 or args.bptt_steps < 2 or args.user_batch_size < 1:''')\np.write_text(s)\nPY\nPYTHONDONTWRITEBYTECODE=1 /opt/task-venv/bin/python /app/solution/test_hard_negatives.py" ], "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(''' 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\")''',''' temporary = output.with_name(output.stem + \".tmp\" + output.suffix)\n if output.suffix == \".parquet\":\n frame.to_parquet(temporary, index=False, compression=\"zstd\")\n elif output.suffix == \".csv\":\n frame.to_csv(temporary, index=False)\n else:\n raise ValueError(\"--output must end in .parquet or .csv\")\n temporary.replace(output)''')\ns=s.replace('statistics[\"negative_sum\"] += float(negative_logits.detach().sum().cpu())', 'statistics[\"negative_sum\"] += float(negative_logits[:, :random_count].detach().sum().cpu())')\ns=s.replace('(positive_logits.detach()[:, None] > negative_logits.detach()).sum().cpu()', '(positive_logits.detach()[:, None] > negative_logits[:, :random_count].detach()).sum().cpu()')\ns=s.replace(''' if args.threads < 1 or args.bptt_steps < 2 or args.user_batch_size < 1:''',''' if (args.hard_negatives < 0 or args.hard_pool_size < args.hard_negatives\n or not 0.0 <= args.hard_loss_weight <= 1.0):\n parser.error(\"hard-negative count, pool size, or loss weight is invalid\")\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.threads < 1 or args.bptt_steps < 2 or args.user_batch_size < 1:''')\np.write_text(s)\nPY\nPYTHONDONTWRITEBYTECODE=1 /opt/task-venv/bin/python /app/solution/test_hard_negatives.py" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "PASS: prefix positives, likes, target, duplicates excluded; finite nonzero gradients\n", "stderr": "", "aggregated_output": "PASS: prefix positives, likes, target, duplicates excluded; finite nonzero gradients\n", "exit_code": 0, "duration": { "secs": 4, "nanos": 512282678 }, "formatted_output": "PASS: prefix positives, likes, target, duplicates excluded; finite nonzero gradients\n" } } ### Harness context { "type": "codex_item_completed", "item": { "type": "Extension", "kind": "clock.sleep", "id": "call_30eMrAZlvQrZuozmVQ2fplmj", "durationMs": 45000 } } ### exec · 2026-09-23T05:25:39.376Z { "code": "text(await tools.exec_command({cmd:\"tail -4 /app/solution/baseline.log\",max_output_tokens:1000}));\ntext(await tools.write_stdin({session_id:11038,chars:\"\",yield_time_ms:1000,max_output_tokens:1000}));\n" } ### Tool output · 2026-09-23T05:25:39.456Z Script completed Wall time 0.0 seconds Output: {"chunk_id":"83c181","wall_time_seconds":0.00000388,"exit_code":0,"original_token_count":92,"output":"epoch=1/1 batches=50 targets=206,457 batch_bce=0.538665 running_bce=0.583441 elapsed=42.4s\nepoch=1/1 batches=75 targets=305,335 batch_bce=0.566309 running_bce=0.575185 elapsed=63.6s\nepoch=1/1 batches=100 targets=402,892 batch_bce=0.558304 running_bce=0.567536 elapsed=83.7s\nepoch=1/1 batches=125 targets=505,534 batch_bce=0.554255 running_bce=0.560968 elapsed=104.7s\n"} {"chunk_id":"eb1813","wall_time_seconds":0.00000177,"exit_code":0,"original_token_count":22,"output":"PASS: prefix positives, likes, target, duplicates excluded; finite nonzero gradients\n"} ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-90ce7afc-2ac6-4e82-8a3f-8a8fbc7f1b6d", "process_id": "34193", "command": [ "/bin/sh", "-lc", "tail -4 /app/solution/baseline.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -4 /app/solution/baseline.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "epoch=1/1 batches=50 targets=206,457 batch_bce=0.538665 running_bce=0.583441 elapsed=42.4s\nepoch=1/1 batches=75 targets=305,335 batch_bce=0.566309 running_bce=0.575185 elapsed=63.6s\nepoch=1/1 batches=100 targets=402,892 batch_bce=0.558304 running_bce=0.567536 elapsed=83.7s\nepoch=1/1 batches=125 targets=505,534 batch_bce=0.554255 running_bce=0.560968 elapsed=104.7s\n", "stderr": "", "aggregated_output": "epoch=1/1 batches=50 targets=206,457 batch_bce=0.538665 running_bce=0.583441 elapsed=42.4s\nepoch=1/1 batches=75 targets=305,335 batch_bce=0.566309 running_bce=0.575185 elapsed=63.6s\nepoch=1/1 batches=100 targets=402,892 batch_bce=0.558304 running_bce=0.567536 elapsed=83.7s\nepoch=1/1 batches=125 targets=505,534 batch_bce=0.554255 running_bce=0.560968 elapsed=104.7s\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 3880 }, "formatted_output": "epoch=1/1 batches=50 targets=206,457 batch_bce=0.538665 running_bce=0.583441 elapsed=42.4s\nepoch=1/1 batches=75 targets=305,335 batch_bce=0.566309 running_bce=0.575185 elapsed=63.6s\nepoch=1/1 batches=100 targets=402,892 batch_bce=0.558304 running_bce=0.567536 elapsed=83.7s\nepoch=1/1 batches=125 targets=505,534 batch_bce=0.554255 running_bce=0.560968 elapsed=104.7s\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "session_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "root_turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "response_id": "resp_021598e9bafc9da7016ab362ce692c87d28ac5c9e28a49b712", "usage": { "input_tokens": 56594, "cached_input_tokens": 56320, "cache_write_input_tokens": 0, "output_tokens": 66, "reasoning_output_tokens": 0, "total_tokens": 56660 }, "turn_token_usage": { "input_tokens": 975301, "cached_input_tokens": 927104, "cache_write_input_tokens": 0, "output_tokens": 8626, "reasoning_output_tokens": 3130, "total_tokens": 983927 }, "thread_token_usage": { "input_tokens": 975301, "cached_input_tokens": 927104, "cache_write_input_tokens": 0, "output_tokens": 8626, "reasoning_output_tokens": 3130, "total_tokens": 983927 } } ## Step 23 · agent · 2026-09-23T05:25:42.724Z ### Thinking unavailable — no text recorded The serial baseline run is progressing normally and is about halfway through its training targets. I’ll also test hard-negative fine-tuning from the baseline checkpoint, so mining starts after the model has learned a useful scoring function. ## Step 24 · agent · 2026-09-23T05:25:59.922Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T05:26:01.606Z { "code": "text(await tools.exec_command({cmd:\"rg -n 'epoch-.*pt|epoch_.*pt|path = checkpoint' /app/solution/train_current_model.py && tail -3 /app/solution/baseline.log\",max_output_tokens:1000}));\n" } ### Tool output · 2026-09-23T05:26:01.670Z Script completed Wall time 0.1 seconds Output: {"chunk_id":"2ec76e","wall_time_seconds":0.000005441,"exit_code":0,"original_token_count":84,"output":"1564: path = checkpoint_dir / f\"epoch-{epoch:02d}.pt\"\nepoch=1/1 batches=100 targets=402,892 batch_bce=0.558304 running_bce=0.567536 elapsed=83.7s\nepoch=1/1 batches=125 targets=505,534 batch_bce=0.554255 running_bce=0.560968 elapsed=104.7s\nepoch=1/1 batches=150 targets=596,745 batch_bce=0.534683 running_bce=0.556214 elapsed=123.9s\n"} ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-699e81e3-bf32-4bdd-a769-bc5d80ab4004", "process_id": "84115", "command": [ "/bin/sh", "-lc", "rg -n 'epoch-.*pt|epoch_.*pt|path = checkpoint' /app/solution/train_current_model.py && tail -3 /app/solution/baseline.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "rg -n 'epoch-.*pt|epoch_.*pt|path = checkpoint' /app/solution/train_current_model.py && tail -3 /app/solution/baseline.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "1564: path = checkpoint_dir / f\"epoch-{epoch:02d}.pt\"\nepoch=1/1 batches=100 targets=402,892 batch_bce=0.558304 running_bce=0.567536 elapsed=83.7s\nepoch=1/1 batches=125 targets=505,534 batch_bce=0.554255 running_bce=0.560968 elapsed=104.7s\nepoch=1/1 batches=150 targets=596,745 batch_bce=0.534683 running_bce=0.556214 elapsed=123.9s\n", "stderr": "", "aggregated_output": "1564: path = checkpoint_dir / f\"epoch-{epoch:02d}.pt\"\nepoch=1/1 batches=100 targets=402,892 batch_bce=0.558304 running_bce=0.567536 elapsed=83.7s\nepoch=1/1 batches=125 targets=505,534 batch_bce=0.554255 running_bce=0.560968 elapsed=104.7s\nepoch=1/1 batches=150 targets=596,745 batch_bce=0.534683 running_bce=0.556214 elapsed=123.9s\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 5441 }, "formatted_output": "1564: path = checkpoint_dir / f\"epoch-{epoch:02d}.pt\"\nepoch=1/1 batches=100 targets=402,892 batch_bce=0.558304 running_bce=0.567536 elapsed=83.7s\nepoch=1/1 batches=125 targets=505,534 batch_bce=0.554255 running_bce=0.560968 elapsed=104.7s\nepoch=1/1 batches=150 targets=596,745 batch_bce=0.534683 running_bce=0.556214 elapsed=123.9s\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "session_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "root_turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "response_id": "resp_021598e9bafc9da7016ab362d3ca5c87d2a51273fa8cc9b4c0", "usage": { "input_tokens": 56911, "cached_input_tokens": 56448, "cache_write_input_tokens": 0, "output_tokens": 653, "reasoning_output_tokens": 537, "total_tokens": 57564 }, "turn_token_usage": { "input_tokens": 1032212, "cached_input_tokens": 983552, "cache_write_input_tokens": 0, "output_tokens": 9279, "reasoning_output_tokens": 3667, "total_tokens": 1041491 }, "thread_token_usage": { "input_tokens": 1032212, "cached_input_tokens": 983552, "cache_write_input_tokens": 0, "output_tokens": 9279, "reasoning_output_tokens": 3667, "total_tokens": 1041491 } } ## Step 25 · agent · 2026-09-23T05:26:12.863Z ### Thinking unavailable — no text recorded ### sleep · 2026-09-23T05:26:13.131Z { "duration_ms": 45000 } ### Tool output · 2026-09-23T05:26:58.147Z Wall time: 45.0077 seconds Sleep completed. ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "session_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "root_turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "response_id": "resp_021598e9bafc9da7016ab362e9eee887d28b56156f9a460780", "usage": { "input_tokens": 57751, "cached_input_tokens": 56704, "cache_write_input_tokens": 0, "output_tokens": 280, "reasoning_output_tokens": 259, "total_tokens": 58031 }, "turn_token_usage": { "input_tokens": 1089963, "cached_input_tokens": 1040256, "cache_write_input_tokens": 0, "output_tokens": 9559, "reasoning_output_tokens": 3926, "total_tokens": 1099522 }, "thread_token_usage": { "input_tokens": 1089963, "cached_input_tokens": 1040256, "cache_write_input_tokens": 0, "output_tokens": 9559, "reasoning_output_tokens": 3926, "total_tokens": 1099522 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "Extension", "kind": "clock.sleep", "id": "call_Zj7TQEnzwH1wzJ74qxd4dVR3", "durationMs": 45000 } } ### exec · 2026-09-23T05:27:02.976Z { "code": "text(await tools.exec_command({cmd:\"tail -5 /app/solution/baseline.log\",max_output_tokens:1000}));\ntext(await tools.clock__curr_time({}));\n" } ### Tool output · 2026-09-23T05:27:03.060Z Script completed Wall time 0.1 seconds Output: {"chunk_id":"bb1384","wall_time_seconds":0.000004861,"exit_code":0,"original_token_count":117,"output":"epoch=1/1 batches=125 targets=505,534 batch_bce=0.554255 running_bce=0.560968 elapsed=104.7s\nepoch=1/1 batches=150 targets=596,745 batch_bce=0.534683 running_bce=0.556214 elapsed=123.9s\nepoch=1/1 batches=175 targets=696,141 batch_bce=0.540465 running_bce=0.553094 elapsed=144.9s\nepoch=1/1 batches=200 targets=790,005 batch_bce=0.521552 running_bce=0.549799 elapsed=165.0s\nepoch=1/1 batches=225 targets=884,435 batch_bce=0.502326 running_bce=0.545935 elapsed=184.7s\n"} {"current_time":"2026-09-23 05:27:03 UTC"} ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-84fe388a-f057-45bb-91e7-34903c703592", "process_id": "23477", "command": [ "/bin/sh", "-lc", "tail -5 /app/solution/baseline.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -5 /app/solution/baseline.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "epoch=1/1 batches=125 targets=505,534 batch_bce=0.554255 running_bce=0.560968 elapsed=104.7s\nepoch=1/1 batches=150 targets=596,745 batch_bce=0.534683 running_bce=0.556214 elapsed=123.9s\nepoch=1/1 batches=175 targets=696,141 batch_bce=0.540465 running_bce=0.553094 elapsed=144.9s\nepoch=1/1 batches=200 targets=790,005 batch_bce=0.521552 running_bce=0.549799 elapsed=165.0s\nepoch=1/1 batches=225 targets=884,435 batch_bce=0.502326 running_bce=0.545935 elapsed=184.7s\n", "stderr": "", "aggregated_output": "epoch=1/1 batches=125 targets=505,534 batch_bce=0.554255 running_bce=0.560968 elapsed=104.7s\nepoch=1/1 batches=150 targets=596,745 batch_bce=0.534683 running_bce=0.556214 elapsed=123.9s\nepoch=1/1 batches=175 targets=696,141 batch_bce=0.540465 running_bce=0.553094 elapsed=144.9s\nepoch=1/1 batches=200 targets=790,005 batch_bce=0.521552 running_bce=0.549799 elapsed=165.0s\nepoch=1/1 batches=225 targets=884,435 batch_bce=0.502326 running_bce=0.545935 elapsed=184.7s\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4861 }, "formatted_output": "epoch=1/1 batches=125 targets=505,534 batch_bce=0.554255 running_bce=0.560968 elapsed=104.7s\nepoch=1/1 batches=150 targets=596,745 batch_bce=0.534683 running_bce=0.556214 elapsed=123.9s\nepoch=1/1 batches=175 targets=696,141 batch_bce=0.540465 running_bce=0.553094 elapsed=144.9s\nepoch=1/1 batches=200 targets=790,005 batch_bce=0.521552 running_bce=0.549799 elapsed=165.0s\nepoch=1/1 batches=225 targets=884,435 batch_bce=0.502326 running_bce=0.545935 elapsed=184.7s\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "session_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "root_turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "response_id": "resp_021598e9bafc9da7016ab3632251d487d2ad45beeb0e8a50dc", "usage": { "input_tokens": 58054, "cached_input_tokens": 57600, "cache_write_input_tokens": 0, "output_tokens": 46, "reasoning_output_tokens": 0, "total_tokens": 58100 }, "turn_token_usage": { "input_tokens": 1148017, "cached_input_tokens": 1097856, "cache_write_input_tokens": 0, "output_tokens": 9605, "reasoning_output_tokens": 3926, "total_tokens": 1157622 }, "thread_token_usage": { "input_tokens": 1148017, "cached_input_tokens": 1097856, "cache_write_input_tokens": 0, "output_tokens": 9605, "reasoning_output_tokens": 3926, "total_tokens": 1157622 } } ## Step 26 · agent · 2026-09-23T05:27:05.974Z ### Thinking unavailable — no text recorded I’ll compare hard-negative fine-tuning with an extra epoch of the original objective. That control will help separate gains from negative selection from gains due to additional training. All validation runs will use only `train_*` history. ## Step 27 · agent · 2026-09-23T05:27:16.046Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T05:27:29.041Z { "code": "text(await tools.exec_command({cmd:\"cat > /app/solution/validate_submission.py <<'PY'\\n\\\"\\\"\\\"Validate the saved final ranking against the public test candidate service.\\\"\\\"\\\"\\nfrom pathlib import Path\\nimport numpy as np\\nimport pandas as pd\\n\\nroot = Path('/app/solution')\\npath = root / 'submission.parquet'\\nassert path.is_file() and not path.is_symlink()\\nf = pd.read_parquet(path)[['uid', 'item_id', 'rank']]\\nfor c in f:\\n a = f[c].to_numpy()\\n assert np.isfinite(a).all() and np.equal(a, np.floor(a)).all(), c\\nexpected_users = pd.read_parquet('/task/data/target_users.parquet').uid.to_numpy()\\nassert set(f.uid) == set(expected_users)\\ngroups = f.groupby('uid')\\nassert (groups.size() == 100).all()\\nassert (groups.item_id.nunique() == 100).all()\\nassert (groups['rank'].nunique() == 100).all()\\nassert (groups['rank'].min() == 1).all() and (groups['rank'].max() == 100).all()\\nwith np.load('/task/data/test_eligible_candidates.npz') as e:\\n rows = pd.Index(e['uids']).get_indexer(f.uid)\\n cols = pd.Index(e['item_ids']).get_indexer(f.item_id)\\n assert (rows >= 0).all() and (cols >= 0).all()\\n allowed = (e['packed_eligible'][rows, cols >> 3] >> (cols & 7)) & 1\\n assert allowed.all()\\nprint(f'PASS: regular file; {len(expected_users):,} users; {len(f):,} rows; integer columns; 100 distinct eligible items and ranks 1..100 per user')\\nPY\\ntail -4 /app/solution/baseline.log\",max_output_tokens:1000}));\n" } ### Tool output · 2026-09-23T05:27:29.115Z Script completed Wall time 0.0 seconds Output: {"chunk_id":"761c14","wall_time_seconds":0.00000555,"exit_code":0,"original_token_count":93,"output":"epoch=1/1 batches=175 targets=696,141 batch_bce=0.540465 running_bce=0.553094 elapsed=144.9s\nepoch=1/1 batches=200 targets=790,005 batch_bce=0.521552 running_bce=0.549799 elapsed=165.0s\nepoch=1/1 batches=225 targets=884,435 batch_bce=0.502326 running_bce=0.545935 elapsed=184.7s\nepoch=1/1 batches=250 targets=987,417 batch_bce=0.547529 running_bce=0.541614 elapsed=206.2s\n"} ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-8889d4a6-c861-4108-b801-e3da1b1fca24", "process_id": "31775", "command": [ "/bin/sh", "-lc", "cat > /app/solution/validate_submission.py <<'PY'\n\"\"\"Validate the saved final ranking against the public test candidate service.\"\"\"\nfrom pathlib import Path\nimport numpy as np\nimport pandas as pd\n\nroot = Path('/app/solution')\npath = root / 'submission.parquet'\nassert path.is_file() and not path.is_symlink()\nf = pd.read_parquet(path)[['uid', 'item_id', 'rank']]\nfor c in f:\n a = f[c].to_numpy()\n assert np.isfinite(a).all() and np.equal(a, np.floor(a)).all(), c\nexpected_users = pd.read_parquet('/task/data/target_users.parquet').uid.to_numpy()\nassert set(f.uid) == set(expected_users)\ngroups = f.groupby('uid')\nassert (groups.size() == 100).all()\nassert (groups.item_id.nunique() == 100).all()\nassert (groups['rank'].nunique() == 100).all()\nassert (groups['rank'].min() == 1).all() and (groups['rank'].max() == 100).all()\nwith np.load('/task/data/test_eligible_candidates.npz') as e:\n rows = pd.Index(e['uids']).get_indexer(f.uid)\n cols = pd.Index(e['item_ids']).get_indexer(f.item_id)\n assert (rows >= 0).all() and (cols >= 0).all()\n allowed = (e['packed_eligible'][rows, cols >> 3] >> (cols & 7)) & 1\n assert allowed.all()\nprint(f'PASS: regular file; {len(expected_users):,} users; {len(f):,} rows; integer columns; 100 distinct eligible items and ranks 1..100 per user')\nPY\ntail -4 /app/solution/baseline.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat > /app/solution/validate_submission.py <<'PY'\n\"\"\"Validate the saved final ranking against the public test candidate service.\"\"\"\nfrom pathlib import Path\nimport numpy as np\nimport pandas as pd\n\nroot = Path('/app/solution')\npath = root / 'submission.parquet'\nassert path.is_file() and not path.is_symlink()\nf = pd.read_parquet(path)[['uid', 'item_id', 'rank']]\nfor c in f:\n a = f[c].to_numpy()\n assert np.isfinite(a).all() and np.equal(a, np.floor(a)).all(), c\nexpected_users = pd.read_parquet('/task/data/target_users.parquet').uid.to_numpy()\nassert set(f.uid) == set(expected_users)\ngroups = f.groupby('uid')\nassert (groups.size() == 100).all()\nassert (groups.item_id.nunique() == 100).all()\nassert (groups['rank'].nunique() == 100).all()\nassert (groups['rank'].min() == 1).all() and (groups['rank'].max() == 100).all()\nwith np.load('/task/data/test_eligible_candidates.npz') as e:\n rows = pd.Index(e['uids']).get_indexer(f.uid)\n cols = pd.Index(e['item_ids']).get_indexer(f.item_id)\n assert (rows >= 0).all() and (cols >= 0).all()\n allowed = (e['packed_eligible'][rows, cols >> 3] >> (cols & 7)) & 1\n assert allowed.all()\nprint(f'PASS: regular file; {len(expected_users):,} users; {len(f):,} rows; integer columns; 100 distinct eligible items and ranks 1..100 per user')\nPY\ntail -4 /app/solution/baseline.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "epoch=1/1 batches=175 targets=696,141 batch_bce=0.540465 running_bce=0.553094 elapsed=144.9s\nepoch=1/1 batches=200 targets=790,005 batch_bce=0.521552 running_bce=0.549799 elapsed=165.0s\nepoch=1/1 batches=225 targets=884,435 batch_bce=0.502326 running_bce=0.545935 elapsed=184.7s\nepoch=1/1 batches=250 targets=987,417 batch_bce=0.547529 running_bce=0.541614 elapsed=206.2s\n", "stderr": "", "aggregated_output": "epoch=1/1 batches=175 targets=696,141 batch_bce=0.540465 running_bce=0.553094 elapsed=144.9s\nepoch=1/1 batches=200 targets=790,005 batch_bce=0.521552 running_bce=0.549799 elapsed=165.0s\nepoch=1/1 batches=225 targets=884,435 batch_bce=0.502326 running_bce=0.545935 elapsed=184.7s\nepoch=1/1 batches=250 targets=987,417 batch_bce=0.547529 running_bce=0.541614 elapsed=206.2s\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 5550 }, "formatted_output": "epoch=1/1 batches=175 targets=696,141 batch_bce=0.540465 running_bce=0.553094 elapsed=144.9s\nepoch=1/1 batches=200 targets=790,005 batch_bce=0.521552 running_bce=0.549799 elapsed=165.0s\nepoch=1/1 batches=225 targets=884,435 batch_bce=0.502326 running_bce=0.545935 elapsed=184.7s\nepoch=1/1 batches=250 targets=987,417 batch_bce=0.547529 running_bce=0.541614 elapsed=206.2s\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "session_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "root_turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "response_id": "resp_021598e9bafc9da7016ab36327480087d29ce700fa57ec1149", "usage": { "input_tokens": 58357, "cached_input_tokens": 57856, "cache_write_input_tokens": 0, "output_tokens": 806, "reasoning_output_tokens": 311, "total_tokens": 59163 }, "turn_token_usage": { "input_tokens": 1206374, "cached_input_tokens": 1155712, "cache_write_input_tokens": 0, "output_tokens": 10411, "reasoning_output_tokens": 4237, "total_tokens": 1216785 }, "thread_token_usage": { "input_tokens": 1206374, "cached_input_tokens": 1155712, "cache_write_input_tokens": 0, "output_tokens": 10411, "reasoning_output_tokens": 4237, "total_tokens": 1216785 } } ### sleep · 2026-09-23T05:27:33.811Z { "duration_ms": 45000 } ### Tool output · 2026-09-23T05:28:18.844Z Wall time: 45.0134 seconds Sleep completed. ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "session_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "root_turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "response_id": "resp_021598e9bafc9da7016ab36341754087d2a7fe0e5db3a2bdf0", "usage": { "input_tokens": 59365, "cached_input_tokens": 58240, "cache_write_input_tokens": 0, "output_tokens": 19, "reasoning_output_tokens": 0, "total_tokens": 59384 }, "turn_token_usage": { "input_tokens": 1265739, "cached_input_tokens": 1213952, "cache_write_input_tokens": 0, "output_tokens": 10430, "reasoning_output_tokens": 4237, "total_tokens": 1276169 }, "thread_token_usage": { "input_tokens": 1265739, "cached_input_tokens": 1213952, "cache_write_input_tokens": 0, "output_tokens": 10430, "reasoning_output_tokens": 4237, "total_tokens": 1276169 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "Extension", "kind": "clock.sleep", "id": "call_Ju34yA58IwFpcXTBOwzNTfnB", "durationMs": 45000 } } ### exec · 2026-09-23T05:28:22.613Z { "code": "text(await tools.exec_command({cmd:\"tail -5 /app/solution/baseline.log\",max_output_tokens:1000}));\ntext(await tools.exec_command({cmd:\"ls -lh /app/solution/baseline_ckpt\",max_output_tokens:1000}));\n" } ### Tool output · 2026-09-23T05:28:22.925Z Script completed Wall time 0.3 seconds Output: {"chunk_id":"93b8aa","wall_time_seconds":0.000003261,"exit_code":0,"original_token_count":121,"output":"epoch=1/1 batches=225 targets=884,435 batch_bce=0.502326 running_bce=0.545935 elapsed=184.7s\nepoch=1/1 batches=250 targets=987,417 batch_bce=0.547529 running_bce=0.541614 elapsed=206.2s\nsaved epoch checkpoint: /app/solution/baseline_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=219.9 validation_seconds=16.2\nrestored best validation-BCE checkpoint: epoch=1 loss=0.496636\n"} {"chunk_id":"16f983","wall_time_seconds":0.000005651,"exit_code":0,"original_token_count":30,"output":"total 289M\n-rw-r--r-- 1 agent agent 145M Sep 23 05:27 best.pt\n-rw-r--r-- 1 agent agent 145M Sep 23 05:27 epoch-01.pt\n"} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "session_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "root_turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "response_id": "resp_021598e9bafc9da7016ab36373047887d2bba023dc06e640c1", "usage": { "input_tokens": 59407, "cached_input_tokens": 59136, "cache_write_input_tokens": 0, "output_tokens": 65, "reasoning_output_tokens": 0, "total_tokens": 59472 }, "turn_token_usage": { "input_tokens": 1325146, "cached_input_tokens": 1273088, "cache_write_input_tokens": 0, "output_tokens": 10495, "reasoning_output_tokens": 4237, "total_tokens": 1335641 }, "thread_token_usage": { "input_tokens": 1325146, "cached_input_tokens": 1273088, "cache_write_input_tokens": 0, "output_tokens": 10495, "reasoning_output_tokens": 4237, "total_tokens": 1335641 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-670193fd-f0b7-42d6-9a02-c78cdf16f92a", "process_id": "72221", "command": [ "/bin/sh", "-lc", "tail -5 /app/solution/baseline.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -5 /app/solution/baseline.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "epoch=1/1 batches=225 targets=884,435 batch_bce=0.502326 running_bce=0.545935 elapsed=184.7s\nepoch=1/1 batches=250 targets=987,417 batch_bce=0.547529 running_bce=0.541614 elapsed=206.2s\nsaved epoch checkpoint: /app/solution/baseline_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=219.9 validation_seconds=16.2\nrestored best validation-BCE checkpoint: epoch=1 loss=0.496636\n", "stderr": "", "aggregated_output": "epoch=1/1 batches=225 targets=884,435 batch_bce=0.502326 running_bce=0.545935 elapsed=184.7s\nepoch=1/1 batches=250 targets=987,417 batch_bce=0.547529 running_bce=0.541614 elapsed=206.2s\nsaved epoch checkpoint: /app/solution/baseline_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=219.9 validation_seconds=16.2\nrestored best validation-BCE checkpoint: epoch=1 loss=0.496636\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 3261 }, "formatted_output": "epoch=1/1 batches=225 targets=884,435 batch_bce=0.502326 running_bce=0.545935 elapsed=184.7s\nepoch=1/1 batches=250 targets=987,417 batch_bce=0.547529 running_bce=0.541614 elapsed=206.2s\nsaved epoch checkpoint: /app/solution/baseline_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=219.9 validation_seconds=16.2\nrestored best validation-BCE checkpoint: epoch=1 loss=0.496636\n" } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-16d94f94-45b9-49e2-b5b5-338de82d792b", "process_id": "77777", "command": [ "/bin/sh", "-lc", "ls -lh /app/solution/baseline_ckpt" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "list_files", "cmd": "ls -lh /app/solution/baseline_ckpt", "path": "baseline_ckpt" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "total 289M\n-rw-r--r-- 1 agent agent 145M Sep 23 05:27 best.pt\n-rw-r--r-- 1 agent agent 145M Sep 23 05:27 epoch-01.pt\n", "stderr": "", "aggregated_output": "total 289M\n-rw-r--r-- 1 agent agent 145M Sep 23 05:27 best.pt\n-rw-r--r-- 1 agent agent 145M Sep 23 05:27 epoch-01.pt\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 5651 }, "formatted_output": "total 289M\n-rw-r--r-- 1 agent agent 145M Sep 23 05:27 best.pt\n-rw-r--r-- 1 agent agent 145M Sep 23 05:27 epoch-01.pt\n" } } ## Step 28 · agent · 2026-09-23T05:28:25.585Z ### Thinking unavailable — no text recorded The baseline has finished training and internal validation. It correctly orders about 75.5% of sampled positive–negative pairs; I’ll compare that diagnostic with its public top-ten discovery score once ranking finishes. ## Step 29 · agent · 2026-09-23T05:28:43.214Z ### Thinking unavailable — no text recorded ### sleep · 2026-09-23T05:28:43.363Z { "duration_ms": 45000 } ### Tool output · 2026-09-23T05:29:28.381Z Wall time: 45.0082 seconds Sleep completed. ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "session_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "root_turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "response_id": "resp_021598e9bafc9da7016ab36377288c87d29c19618d82c9df30", "usage": { "input_tokens": 59776, "cached_input_tokens": 59264, "cache_write_input_tokens": 0, "output_tokens": 503, "reasoning_output_tokens": 434, "total_tokens": 60279 }, "turn_token_usage": { "input_tokens": 1384922, "cached_input_tokens": 1332352, "cache_write_input_tokens": 0, "output_tokens": 10998, "reasoning_output_tokens": 4671, "total_tokens": 1395920 }, "thread_token_usage": { "input_tokens": 1384922, "cached_input_tokens": 1332352, "cache_write_input_tokens": 0, "output_tokens": 10998, "reasoning_output_tokens": 4671, "total_tokens": 1395920 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-f023eca2-b898-4e9b-8f65-7087addbd523", "process_id": "30148", "command": [ "/bin/sh", "-lc", "cp /task/current_model/train_current_model.py /app/solution/train_production.py && chmod u+w /app/solution/train_production.py && /opt/task-venv/bin/python -c \"from pathlib import Path; p=Path('/app/solution/train_production.py'); p.write_text(p.read_text().replace('batches % 250 == 0', 'batches % 25 == 0'))\" && OPENBLAS_NUM_THREADS=1 OMP_NUM_THREADS=4 PYTHONDONTWRITEBYTECODE=1 /opt/task-venv/bin/python /app/solution/train_production.py --stage validation --public-dir /task --output /app/solution/production_validation.parquet --prepared-cache /app/solution/validation_cache.npz --checkpoint-dir /app/solution/baseline_ckpt --threads 4 > /app/solution/baseline.json 2> /app/solution/baseline.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cp /task/current_model/train_current_model.py /app/solution/train_production.py && chmod u+w /app/solution/train_production.py && /opt/task-venv/bin/python -c \"from pathlib import Path; p=Path('/app/solution/train_production.py'); p.write_text(p.read_text().replace('batches % 250 == 0', 'batches % 25 == 0'))\" && OPENBLAS_NUM_THREADS=1 OMP_NUM_THREADS=4 PYTHONDONTWRITEBYTECODE=1 /opt/task-venv/bin/python /app/solution/train_production.py --stage validation --public-dir /task --output /app/solution/production_validation.parquet --prepared-cache /app/solution/validation_cache.npz --checkpoint-dir /app/solution/baseline_ckpt --threads 4 > /app/solution/baseline.json 2> /app/solution/baseline.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 323, "nanos": 967665831 }, "formatted_output": "" } } ### Harness context { "type": "codex_item_completed", "item": { "type": "Extension", "kind": "clock.sleep", "id": "call_vLZZ5kAmfycqx7Xdf710Dt7m", "durationMs": 45000 } } ### exec · 2026-09-23T05:29:31.429Z { "code": "text(await tools.exec_command({cmd:\"tail -3 /app/solution/baseline.log && ls -lh /app/solution/production_validation.parquet\",max_output_tokens:1000}));\ntext(await tools.clock__curr_time({}));\n" } ### Tool output · 2026-09-23T05:29:31.497Z Script completed Wall time 0.1 seconds Output: {"chunk_id":"221f37","wall_time_seconds":0.00000686,"exit_code":0,"original_token_count":41,"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 05:29 /app/solution/production_validation.parquet\n"} {"current_time":"2026-09-23 05:29:31 UTC"} ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-fb2a2a8f-f4b8-415a-9cbf-126e20c8fe76", "process_id": "51162", "command": [ "/bin/sh", "-lc", "tail -3 /app/solution/baseline.log && ls -lh /app/solution/production_validation.parquet" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -3 /app/solution/baseline.log && ls -lh /app/solution/production_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 05:29 /app/solution/production_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 05:29 /app/solution/production_validation.parquet\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 6860 }, "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 05:29 /app/solution/production_validation.parquet\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "session_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "root_turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "response_id": "resp_021598e9bafc9da7016ab363b893f487d295963afea9c7fffe", "usage": { "input_tokens": 60302, "cached_input_tokens": 59648, "cache_write_input_tokens": 0, "output_tokens": 59, "reasoning_output_tokens": 0, "total_tokens": 60361 }, "turn_token_usage": { "input_tokens": 1445224, "cached_input_tokens": 1392000, "cache_write_input_tokens": 0, "output_tokens": 11057, "reasoning_output_tokens": 4671, "total_tokens": 1456281 }, "thread_token_usage": { "input_tokens": 1445224, "cached_input_tokens": 1392000, "cache_write_input_tokens": 0, "output_tokens": 11057, "reasoning_output_tokens": 4671, "total_tokens": 1456281 } } ### exec · 2026-09-23T05:29:38.995Z { "code": "text(await tools.exec_command({cmd:\"PYTHONDONTWRITEBYTECODE=1 /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/production_validation.parquet > /app/solution/baseline_score.json && cat /app/solution/baseline_score.json\",yield_time_ms:1000,max_output_tokens:1000}));\ntext(await tools.exec_command({cmd:\"OPENBLAS_NUM_THREADS=1 OMP_NUM_THREADS=4 PYTHONDONTWRITEBYTECODE=1 /opt/task-venv/bin/python /app/solution/train_production.py --stage submission --public-dir /task --output /app/solution/submission.parquet --prepared-cache /app/solution/submission_cache.npz --checkpoint-dir /app/solution/production_submission_ckpt --threads 4 > /app/solution/production_submission.json 2> /app/solution/production_submission.log\",yield_time_ms:1000}));\n" } ### Tool output · 2026-09-23T05:29:41.331Z Script completed Wall time 2.3 seconds Output: {"chunk_id":"9fd929","wall_time_seconds":1.000689254,"session_id":70437,"original_token_count":0,"output":""} {"chunk_id":"c901d2","wall_time_seconds":1.001352139,"session_id":31559,"original_token_count":0,"output":""} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "session_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "root_turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "response_id": "resp_021598e9bafc9da7016ab363bbb38887d2a80349a0db524471", "usage": { "input_tokens": 60502, "cached_input_tokens": 60160, "cache_write_input_tokens": 0, "output_tokens": 205, "reasoning_output_tokens": 0, "total_tokens": 60707 }, "turn_token_usage": { "input_tokens": 1505726, "cached_input_tokens": 1452160, "cache_write_input_tokens": 0, "output_tokens": 11262, "reasoning_output_tokens": 4671, "total_tokens": 1516988 }, "thread_token_usage": { "input_tokens": 1505726, "cached_input_tokens": 1452160, "cache_write_input_tokens": 0, "output_tokens": 11262, "reasoning_output_tokens": 4671, "total_tokens": 1516988 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-90c124a6-33a3-4696-83fd-2e30f393a949", "process_id": "70437", "command": [ "/bin/sh", "-lc", "PYTHONDONTWRITEBYTECODE=1 /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/production_validation.parquet > /app/solution/baseline_score.json && cat /app/solution/baseline_score.json" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "PYTHONDONTWRITEBYTECODE=1 /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/production_validation.parquet > /app/solution/baseline_score.json && cat /app/solution/baseline_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": 223252107 }, "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 30 · agent · 2026-09-23T05:29:46.137Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T05:29:47.985Z { "code": "text(await tools.write_stdin({session_id:70437,chars:\"\",yield_time_ms:1000,max_output_tokens:1000}));\ntext(await tools.exec_command({cmd:\"cat /app/solution/baseline_score.json && tail -4 /app/solution/production_submission.log\",max_output_tokens:1200}));\n" } ### Tool output · 2026-09-23T05:29:48.064Z Script completed Wall time 0.0 seconds Output: {"chunk_id":"cfc6c3","wall_time_seconds":0.00000239,"exit_code":0,"original_token_count":35,"output":"{\n \"model\": \"/app/solution/production_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.02128,\n \"quality_users\": 4263\n }\n}\n"} {"chunk_id":"1f8ba6","wall_time_seconds":0.00000311,"exit_code":0,"original_token_count":47,"output":"{\n \"model\": \"/app/solution/production_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.02128,\n \"quality_users\": 4263\n }\n}\nloaded explicit events: rows=746,504 users=8,307\n"} ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-f384e222-ba85-4610-970a-479a69b850a4", "process_id": "65265", "command": [ "/bin/sh", "-lc", "cat /app/solution/baseline_score.json && tail -4 /app/solution/production_submission.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat /app/solution/baseline_score.json && tail -4 /app/solution/production_submission.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 explicit events: rows=746,504 users=8,307\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 explicit events: rows=746,504 users=8,307\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 3110 }, "formatted_output": "{\n \"model\": \"/app/solution/production_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.02128,\n \"quality_users\": 4263\n }\n}\nloaded explicit events: rows=746,504 users=8,307\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "session_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "root_turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "response_id": "resp_021598e9bafc9da7016ab363c5835487d284447b0a0bff3600", "usage": { "input_tokens": 60799, "cached_input_tokens": 60288, "cache_write_input_tokens": 0, "output_tokens": 178, "reasoning_output_tokens": 99, "total_tokens": 60977 }, "turn_token_usage": { "input_tokens": 1566525, "cached_input_tokens": 1512448, "cache_write_input_tokens": 0, "output_tokens": 11440, "reasoning_output_tokens": 4770, "total_tokens": 1577965 }, "thread_token_usage": { "input_tokens": 1566525, "cached_input_tokens": 1512448, "cache_write_input_tokens": 0, "output_tokens": 11440, "reasoning_output_tokens": 4770, "total_tokens": 1577965 } } ## Step 31 · agent · 2026-09-23T05:30:06.768Z ### Thinking unavailable — no text recorded The reproduced baseline scored **0.021280 NDCG@10** across 4,263 users with eligible relevant outcomes. I’m now refitting it on `train_* + val_*` to save a valid fallback submission before continuing the objective experiments. ## Step 32 · agent · 2026-09-23T05:30:12.400Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T05:30:23.369Z { "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()\\na=s.index(' priorities = torch.rand(', s.index('def draw_full_history_filtered_negatives'))\\nb=s.index(' inbatch = unique_targets[columns]', a)\\ns=s[:a]+''' # Uniform sampling without replacement, excluding each row's target.\\n # Avoid the old B x number-of-unique-targets random-priority matrix.\\n target_columns = torch.searchsorted(unique_targets, targets.detach().cpu())\\n columns = torch.empty((batch_size, corrected_count), dtype=torch.long)\\n for column in range(corrected_count):\\n draw = torch.randint(len(unique_targets) - 1, (batch_size,), generator=generator)\\n draw += draw.ge(target_columns).long()\\n if column:\\n duplicate = draw[:, None].eq(columns[:, :column]).any(dim=1)\\n while bool(duplicate.any()):\\n replacement = torch.randint(len(unique_targets) - 1,\\n (int(duplicate.sum()),), generator=generator)\\n replacement += replacement.ge(target_columns[duplicate]).long()\\n draw[duplicate] = replacement\\n duplicate = draw[:, None].eq(columns[:, :column]).any(dim=1)\\n columns[:, column] = draw\\n''' +s[b:]\\np.write_text(s)\\nPY\\nPYTHONDONTWRITEBYTECODE=1 /opt/task-venv/bin/python /app/solution/test_hard_negatives.py\",yield_time_ms:1000,max_output_tokens:1000}));\n" } ### Tool output · 2026-09-23T05:30:24.537Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"be1cb5","wall_time_seconds":1.000683441,"session_id":52856,"original_token_count":0,"output":""} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "session_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "root_turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "response_id": "resp_021598e9bafc9da7016ab363cc5d7487d2a69ca30d104b4b7e", "usage": { "input_tokens": 61189, "cached_input_tokens": 60672, "cache_write_input_tokens": 0, "output_tokens": 1093, "reasoning_output_tokens": 659, "total_tokens": 62282 }, "turn_token_usage": { "input_tokens": 1627714, "cached_input_tokens": 1573120, "cache_write_input_tokens": 0, "output_tokens": 12533, "reasoning_output_tokens": 5429, "total_tokens": 1640247 }, "thread_token_usage": { "input_tokens": 1627714, "cached_input_tokens": 1573120, "cache_write_input_tokens": 0, "output_tokens": 12533, "reasoning_output_tokens": 5429, "total_tokens": 1640247 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-b6911356-6a10-43f7-ae57-535a2e6401ce", "process_id": "52856", "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()\na=s.index(' priorities = torch.rand(', s.index('def draw_full_history_filtered_negatives'))\nb=s.index(' inbatch = unique_targets[columns]', a)\ns=s[:a]+''' # Uniform sampling without replacement, excluding each row's target.\n # Avoid the old B x number-of-unique-targets random-priority matrix.\n target_columns = torch.searchsorted(unique_targets, targets.detach().cpu())\n columns = torch.empty((batch_size, corrected_count), dtype=torch.long)\n for column in range(corrected_count):\n draw = torch.randint(len(unique_targets) - 1, (batch_size,), generator=generator)\n draw += draw.ge(target_columns).long()\n if column:\n duplicate = draw[:, None].eq(columns[:, :column]).any(dim=1)\n while bool(duplicate.any()):\n replacement = torch.randint(len(unique_targets) - 1,\n (int(duplicate.sum()),), generator=generator)\n replacement += replacement.ge(target_columns[duplicate]).long()\n draw[duplicate] = replacement\n duplicate = draw[:, None].eq(columns[:, :column]).any(dim=1)\n columns[:, column] = draw\n''' +s[b:]\np.write_text(s)\nPY\nPYTHONDONTWRITEBYTECODE=1 /opt/task-venv/bin/python /app/solution/test_hard_negatives.py" ], "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()\na=s.index(' priorities = torch.rand(', s.index('def draw_full_history_filtered_negatives'))\nb=s.index(' inbatch = unique_targets[columns]', a)\ns=s[:a]+''' # Uniform sampling without replacement, excluding each row's target.\n # Avoid the old B x number-of-unique-targets random-priority matrix.\n target_columns = torch.searchsorted(unique_targets, targets.detach().cpu())\n columns = torch.empty((batch_size, corrected_count), dtype=torch.long)\n for column in range(corrected_count):\n draw = torch.randint(len(unique_targets) - 1, (batch_size,), generator=generator)\n draw += draw.ge(target_columns).long()\n if column:\n duplicate = draw[:, None].eq(columns[:, :column]).any(dim=1)\n while bool(duplicate.any()):\n replacement = torch.randint(len(unique_targets) - 1,\n (int(duplicate.sum()),), generator=generator)\n replacement += replacement.ge(target_columns[duplicate]).long()\n draw[duplicate] = replacement\n duplicate = draw[:, None].eq(columns[:, :column]).any(dim=1)\n columns[:, column] = draw\n''' +s[b:]\np.write_text(s)\nPY\nPYTHONDONTWRITEBYTECODE=1 /opt/task-venv/bin/python /app/solution/test_hard_negatives.py" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "PASS: prefix positives, likes, target, duplicates excluded; finite nonzero gradients\n", "stderr": "", "aggregated_output": "PASS: prefix positives, likes, target, duplicates excluded; finite nonzero gradients\n", "exit_code": 0, "duration": { "secs": 4, "nanos": 11874696 }, "formatted_output": "PASS: prefix positives, likes, target, duplicates excluded; finite nonzero gradients\n" } } ## Step 33 · agent · 2026-09-23T05:30:40.759Z ### Thinking unavailable — no text recorded ### sleep · 2026-09-23T05:30:40.811Z { "duration_ms": 45000 } ### Tool output · 2026-09-23T05:31:25.820Z Wall time: 45.0048 seconds Sleep completed. ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "session_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "root_turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "response_id": "resp_021598e9bafc9da7016ab363f0b0b887d28d49101347592fb7", "usage": { "input_tokens": 62340, "cached_input_tokens": 61056, "cache_write_input_tokens": 0, "output_tokens": 334, "reasoning_output_tokens": 313, "total_tokens": 62674 }, "turn_token_usage": { "input_tokens": 1690054, "cached_input_tokens": 1634176, "cache_write_input_tokens": 0, "output_tokens": 12867, "reasoning_output_tokens": 5742, "total_tokens": 1702921 }, "thread_token_usage": { "input_tokens": 1690054, "cached_input_tokens": 1634176, "cache_write_input_tokens": 0, "output_tokens": 12867, "reasoning_output_tokens": 5742, "total_tokens": 1702921 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "Extension", "kind": "clock.sleep", "id": "call_YtIpOY2od1LgShJZY99TNo7O", "durationMs": 45000 } } ### exec · 2026-09-23T05:31:32.778Z { "code": "text(await tools.exec_command({cmd:\"tail -4 /app/solution/production_submission.log && cat /sys/fs/cgroup/memory.current\",max_output_tokens:1000}));\ntext(await tools.write_stdin({session_id:52856,chars:\"\",yield_time_ms:1000,max_output_tokens:1000}));\ntext(await tools.clock__curr_time({}));\n" } ### Tool output · 2026-09-23T05:31:32.849Z Script completed Wall time 0.0 seconds Output: {"chunk_id":"4499d1","wall_time_seconds":0.00000517,"exit_code":0,"original_token_count":87,"output":"target weighting=organic-strong min=0.250 mean=0.576 max=1.000\nfull-history false-negative filter: users=9,153 unique_user_items=5,682,910 bytes=34,323,750\nepoch=1/1 batches=25 targets=97,617 batch_bce=0.572423 running_bce=0.605275 elapsed=20.4s\nepoch=1/1 batches=50 targets=196,561 batch_bce=0.554199 running_bce=0.588416 elapsed=40.9s\n5392592896\n"} {"chunk_id":"39a058","wall_time_seconds":0.00000186,"exit_code":0,"original_token_count":22,"output":"PASS: prefix positives, likes, target, duplicates excluded; finite nonzero gradients\n"} {"current_time":"2026-09-23 05:31:32 UTC"} ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-b4dec03c-1779-4bbb-b6f3-335cc627337e", "process_id": "49482", "command": [ "/bin/sh", "-lc", "tail -4 /app/solution/production_submission.log && cat /sys/fs/cgroup/memory.current" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -4 /app/solution/production_submission.log && cat /sys/fs/cgroup/memory.current" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "target weighting=organic-strong min=0.250 mean=0.576 max=1.000\nfull-history false-negative filter: users=9,153 unique_user_items=5,682,910 bytes=34,323,750\nepoch=1/1 batches=25 targets=97,617 batch_bce=0.572423 running_bce=0.605275 elapsed=20.4s\nepoch=1/1 batches=50 targets=196,561 batch_bce=0.554199 running_bce=0.588416 elapsed=40.9s\n5392592896\n", "stderr": "", "aggregated_output": "target weighting=organic-strong min=0.250 mean=0.576 max=1.000\nfull-history false-negative filter: users=9,153 unique_user_items=5,682,910 bytes=34,323,750\nepoch=1/1 batches=25 targets=97,617 batch_bce=0.572423 running_bce=0.605275 elapsed=20.4s\nepoch=1/1 batches=50 targets=196,561 batch_bce=0.554199 running_bce=0.588416 elapsed=40.9s\n5392592896\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 5170 }, "formatted_output": "target weighting=organic-strong min=0.250 mean=0.576 max=1.000\nfull-history false-negative filter: users=9,153 unique_user_items=5,682,910 bytes=34,323,750\nepoch=1/1 batches=25 targets=97,617 batch_bce=0.572423 running_bce=0.605275 elapsed=20.4s\nepoch=1/1 batches=50 targets=196,561 batch_bce=0.554199 running_bce=0.588416 elapsed=40.9s\n5392592896\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "session_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "root_turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "response_id": "resp_021598e9bafc9da7016ab3642e22f087d2bdbabf073f00acaa", "usage": { "input_tokens": 62697, "cached_input_tokens": 62208, "cache_write_input_tokens": 0, "output_tokens": 85, "reasoning_output_tokens": 0, "total_tokens": 62782 }, "turn_token_usage": { "input_tokens": 1752751, "cached_input_tokens": 1696384, "cache_write_input_tokens": 0, "output_tokens": 12952, "reasoning_output_tokens": 5742, "total_tokens": 1765703 }, "thread_token_usage": { "input_tokens": 1752751, "cached_input_tokens": 1696384, "cache_write_input_tokens": 0, "output_tokens": 12952, "reasoning_output_tokens": 5742, "total_tokens": 1765703 } } ## Step 34 · agent · 2026-09-23T05:31:40.466Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T05:31:46.194Z { "code": "text(await tools.exec_command({cmd:\"PYTHONDONTWRITEBYTECODE=1 /opt/task-venv/bin/python - <<'PY'\\nimport pandas as pd, json\\nf=pd.read_parquet('/app/solution/production_validation.parquet')\\nc=pd.read_parquet('/task/data/candidate_catalog.parquet')\\ntop=f[f['rank']<=10].merge(c[['item_id','popularity_rank']],on='item_id')\\nr={'top10_unique_items':int(top.item_id.nunique()),'top10_median_popularity_rank':float(top.popularity_rank.median()),'top1_most_common':f[f['rank']==1].item_id.value_counts().head(5).to_dict()}\\nprint(json.dumps(r,indent=2))\\nopen('/app/solution/baseline_ranking_diagnostics.json','w').write(json.dumps(r,indent=2))\\nPY\",yield_time_ms:1000,max_output_tokens:1000}));\n" } ### Tool output · 2026-09-23T05:31:47.365Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"078117","wall_time_seconds":1.000997056,"session_id":40704,"original_token_count":0,"output":""} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "session_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "root_turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "response_id": "resp_021598e9bafc9da7016ab364352ef087d29d09795b87272627", "usage": { "input_tokens": 63034, "cached_input_tokens": 62464, "cache_write_input_tokens": 0, "output_tokens": 349, "reasoning_output_tokens": 130, "total_tokens": 63383 }, "turn_token_usage": { "input_tokens": 1815785, "cached_input_tokens": 1758848, "cache_write_input_tokens": 0, "output_tokens": 13301, "reasoning_output_tokens": 5872, "total_tokens": 1829086 }, "thread_token_usage": { "input_tokens": 1815785, "cached_input_tokens": 1758848, "cache_write_input_tokens": 0, "output_tokens": 13301, "reasoning_output_tokens": 5872, "total_tokens": 1829086 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-fb53f376-59c8-47de-af83-ef7d292effe9", "process_id": "40704", "command": [ "/bin/sh", "-lc", "PYTHONDONTWRITEBYTECODE=1 /opt/task-venv/bin/python - <<'PY'\nimport pandas as pd, json\nf=pd.read_parquet('/app/solution/production_validation.parquet')\nc=pd.read_parquet('/task/data/candidate_catalog.parquet')\ntop=f[f['rank']<=10].merge(c[['item_id','popularity_rank']],on='item_id')\nr={'top10_unique_items':int(top.item_id.nunique()),'top10_median_popularity_rank':float(top.popularity_rank.median()),'top1_most_common':f[f['rank']==1].item_id.value_counts().head(5).to_dict()}\nprint(json.dumps(r,indent=2))\nopen('/app/solution/baseline_ranking_diagnostics.json','w').write(json.dumps(r,indent=2))\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "PYTHONDONTWRITEBYTECODE=1 /opt/task-venv/bin/python - <<'PY'\nimport pandas as pd, json\nf=pd.read_parquet('/app/solution/production_validation.parquet')\nc=pd.read_parquet('/task/data/candidate_catalog.parquet')\ntop=f[f['rank']<=10].merge(c[['item_id','popularity_rank']],on='item_id')\nr={'top10_unique_items':int(top.item_id.nunique()),'top10_median_popularity_rank':float(top.popularity_rank.median()),'top1_most_common':f[f['rank']==1].item_id.value_counts().head(5).to_dict()}\nprint(json.dumps(r,indent=2))\nopen('/app/solution/baseline_ranking_diagnostics.json','w').write(json.dumps(r,indent=2))\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{\n \"top10_unique_items\": 592,\n \"top10_median_popularity_rank\": 111.0,\n \"top1_most_common\": {\n \"3542184\": 2197,\n \"8213481\": 880,\n \"6006390\": 577,\n \"8743390\": 439,\n \"6741823\": 293\n }\n}\n", "stderr": "", "aggregated_output": "{\n \"top10_unique_items\": 592,\n \"top10_median_popularity_rank\": 111.0,\n \"top1_most_common\": {\n \"3542184\": 2197,\n \"8213481\": 880,\n \"6006390\": 577,\n \"8743390\": 439,\n \"6741823\": 293\n }\n}\n", "exit_code": 0, "duration": { "secs": 1, "nanos": 854331764 }, "formatted_output": "{\n \"top10_unique_items\": 592,\n \"top10_median_popularity_rank\": 111.0,\n \"top1_most_common\": {\n \"3542184\": 2197,\n \"8213481\": 880,\n \"6006390\": 577,\n \"8743390\": 439,\n \"6741823\": 293\n }\n}\n" } } ## Step 35 · agent · 2026-09-23T05:32:00.350Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T05:32:01.122Z { "code": "text(await tools.write_stdin({session_id:40704,chars:\"\",yield_time_ms:1000,max_output_tokens:1000}));\ntext(await tools.exec_command({cmd:\"tail -3 /app/solution/production_submission.log\",max_output_tokens:1000}));\n" } ### Tool output · 2026-09-23T05:32:01.173Z Script completed Wall time 0.0 seconds Output: {"chunk_id":"db61b9","wall_time_seconds":0.00000249,"exit_code":0,"original_token_count":51,"output":"{\n \"top10_unique_items\": 592,\n \"top10_median_popularity_rank\": 111.0,\n \"top1_most_common\": {\n \"3542184\": 2197,\n \"8213481\": 880,\n \"6006390\": 577,\n \"8743390\": 439,\n \"6741823\": 293\n }\n}\n"} {"chunk_id":"4881e3","wall_time_seconds":0.00000505,"exit_code":0,"original_token_count":68,"output":"epoch=1/1 batches=25 targets=97,617 batch_bce=0.572423 running_bce=0.605275 elapsed=20.4s\nepoch=1/1 batches=50 targets=196,561 batch_bce=0.554199 running_bce=0.588416 elapsed=40.9s\nepoch=1/1 batches=75 targets=301,265 batch_bce=0.549864 running_bce=0.579743 elapsed=62.6s\n"} ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-c79d253d-b67a-4fdc-a9e8-c1417f6d8e84", "process_id": "36690", "command": [ "/bin/sh", "-lc", "tail -3 /app/solution/production_submission.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -3 /app/solution/production_submission.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "epoch=1/1 batches=25 targets=97,617 batch_bce=0.572423 running_bce=0.605275 elapsed=20.4s\nepoch=1/1 batches=50 targets=196,561 batch_bce=0.554199 running_bce=0.588416 elapsed=40.9s\nepoch=1/1 batches=75 targets=301,265 batch_bce=0.549864 running_bce=0.579743 elapsed=62.6s\n", "stderr": "", "aggregated_output": "epoch=1/1 batches=25 targets=97,617 batch_bce=0.572423 running_bce=0.605275 elapsed=20.4s\nepoch=1/1 batches=50 targets=196,561 batch_bce=0.554199 running_bce=0.588416 elapsed=40.9s\nepoch=1/1 batches=75 targets=301,265 batch_bce=0.549864 running_bce=0.579743 elapsed=62.6s\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 5050 }, "formatted_output": "epoch=1/1 batches=25 targets=97,617 batch_bce=0.572423 running_bce=0.605275 elapsed=20.4s\nepoch=1/1 batches=50 targets=196,561 batch_bce=0.554199 running_bce=0.588416 elapsed=40.9s\nepoch=1/1 batches=75 targets=301,265 batch_bce=0.549864 running_bce=0.579743 elapsed=62.6s\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "session_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "root_turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "response_id": "resp_021598e9bafc9da7016ab36443889487d2a6d6426bd29d70ef", "usage": { "input_tokens": 63439, "cached_input_tokens": 62848, "cache_write_input_tokens": 0, "output_tokens": 407, "reasoning_output_tokens": 339, "total_tokens": 63846 }, "turn_token_usage": { "input_tokens": 1879224, "cached_input_tokens": 1821696, "cache_write_input_tokens": 0, "output_tokens": 13708, "reasoning_output_tokens": 6211, "total_tokens": 1892932 }, "thread_token_usage": { "input_tokens": 1879224, "cached_input_tokens": 1821696, "cache_write_input_tokens": 0, "output_tokens": 13708, "reasoning_output_tokens": 6211, "total_tokens": 1892932 } } ## Step 36 · agent · 2026-09-23T05:32:09.020Z The baseline rankings are concentrated: one track ranks first for 2,197 of 8,456 users, and the median popularity rank of its top-ten recommendations is 111 out of 30,000. That supports testing negatives drawn from high-scoring competitors, though the public score will tell us whether the change improves relevance. ## Step 37 · agent · 2026-09-23T05:32:24.966Z ### Thinking unavailable — no text recorded ## Step 38 · agent · 2026-09-23T05:32:32.432Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T05:32:40.342Z { "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()\\ns=s.replace('# Held-out BCE remains the original deterministic random-negative diagnostic.', '# Chronological validation uses the same objective with a fixed sampling seed.')\\ns=s.replace('if backward and config.hard_negatives:', 'if config.hard_negatives:')\\ns=s.replace('if config.ranking_loss == \\\"softmax\\\" and backward:', 'if config.ranking_loss == \\\"softmax\\\":')\\ns=s.replace(''' backward=False,\\n )''',''' backward=False,\\n user_rows=flat_user_rows,\\n known_positive_packed=known_positive_packed,\\n generator=generator,\\n )''')\\ns=s.replace('\\\"\\\"\\\"Measure deterministic BCE on each user\\\\'s chronological target suffix.\\\"\\\"\\\"','\\\"\\\"\\\"Measure the configured objective on the unchanged chronological suffix.\\\"\\\"\\\"')\\np.write_text(s)\\nPY\\nPYTHONDONTWRITEBYTECODE=1 /opt/task-venv/bin/python /app/solution/test_hard_negatives.py && tail -3 /app/solution/production_submission.log\",yield_time_ms:1000,max_output_tokens:1000}));\n" } ### Tool output · 2026-09-23T05:32:41.511Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"456b3a","wall_time_seconds":1.001511323,"session_id":93133,"original_token_count":0,"output":""} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "session_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "root_turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "response_id": "resp_021598e9bafc9da7016ab3645172ec87d2b2273809883d4a66", "usage": { "input_tokens": 64139, "cached_input_tokens": 63232, "cache_write_input_tokens": 0, "output_tokens": 1118, "reasoning_output_tokens": 754, "total_tokens": 65257 }, "turn_token_usage": { "input_tokens": 1943363, "cached_input_tokens": 1884928, "cache_write_input_tokens": 0, "output_tokens": 14826, "reasoning_output_tokens": 6965, "total_tokens": 1958189 }, "thread_token_usage": { "input_tokens": 1943363, "cached_input_tokens": 1884928, "cache_write_input_tokens": 0, "output_tokens": 14826, "reasoning_output_tokens": 6965, "total_tokens": 1958189 } } ## Step 39 · agent · 2026-09-23T05:32:44.341Z ### Thinking unavailable — no text recorded ### sleep · 2026-09-23T05:32:44.424Z { "duration_ms": 45000 } ### Tool output · 2026-09-23T05:33:29.454Z Wall time: 45.0220 seconds Sleep completed. ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-d3f0085c-83a6-45e6-9011-c08c9680cc44", "process_id": "93133", "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()\ns=s.replace('# Held-out BCE remains the original deterministic random-negative diagnostic.', '# Chronological validation uses the same objective with a fixed sampling seed.')\ns=s.replace('if backward and config.hard_negatives:', 'if config.hard_negatives:')\ns=s.replace('if config.ranking_loss == \"softmax\" and backward:', 'if config.ranking_loss == \"softmax\":')\ns=s.replace(''' backward=False,\n )''',''' backward=False,\n user_rows=flat_user_rows,\n known_positive_packed=known_positive_packed,\n generator=generator,\n )''')\ns=s.replace('\"\"\"Measure deterministic BCE on each user\\'s chronological target suffix.\"\"\"','\"\"\"Measure the configured objective on the unchanged chronological suffix.\"\"\"')\np.write_text(s)\nPY\nPYTHONDONTWRITEBYTECODE=1 /opt/task-venv/bin/python /app/solution/test_hard_negatives.py && tail -3 /app/solution/production_submission.log" ], "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()\ns=s.replace('# Held-out BCE remains the original deterministic random-negative diagnostic.', '# Chronological validation uses the same objective with a fixed sampling seed.')\ns=s.replace('if backward and config.hard_negatives:', 'if config.hard_negatives:')\ns=s.replace('if config.ranking_loss == \"softmax\" and backward:', 'if config.ranking_loss == \"softmax\":')\ns=s.replace(''' backward=False,\n )''',''' backward=False,\n user_rows=flat_user_rows,\n known_positive_packed=known_positive_packed,\n generator=generator,\n )''')\ns=s.replace('\"\"\"Measure deterministic BCE on each user\\'s chronological target suffix.\"\"\"','\"\"\"Measure the configured objective on the unchanged chronological suffix.\"\"\"')\np.write_text(s)\nPY\nPYTHONDONTWRITEBYTECODE=1 /opt/task-venv/bin/python /app/solution/test_hard_negatives.py && tail -3 /app/solution/production_submission.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "PASS: prefix positives, likes, target, duplicates excluded; finite nonzero gradients\nepoch=1/1 batches=75 targets=301,265 batch_bce=0.549864 running_bce=0.579743 elapsed=62.6s\nepoch=1/1 batches=100 targets=394,078 batch_bce=0.549014 running_bce=0.574242 elapsed=82.5s\nepoch=1/1 batches=125 targets=498,782 batch_bce=0.535500 running_bce=0.566724 elapsed=104.1s\n", "stderr": "", "aggregated_output": "PASS: prefix positives, likes, target, duplicates excluded; finite nonzero gradients\nepoch=1/1 batches=75 targets=301,265 batch_bce=0.549864 running_bce=0.579743 elapsed=62.6s\nepoch=1/1 batches=100 targets=394,078 batch_bce=0.549014 running_bce=0.574242 elapsed=82.5s\nepoch=1/1 batches=125 targets=498,782 batch_bce=0.535500 running_bce=0.566724 elapsed=104.1s\n", "exit_code": 0, "duration": { "secs": 4, "nanos": 420166123 }, "formatted_output": "PASS: prefix positives, likes, target, duplicates excluded; finite nonzero gradients\nepoch=1/1 batches=75 targets=301,265 batch_bce=0.549864 running_bce=0.579743 elapsed=62.6s\nepoch=1/1 batches=100 targets=394,078 batch_bce=0.549014 running_bce=0.574242 elapsed=82.5s\nepoch=1/1 batches=125 targets=498,782 batch_bce=0.535500 running_bce=0.566724 elapsed=104.1s\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "session_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "root_turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "response_id": "resp_021598e9bafc9da7016ab36479b5d887d283e711743b7f2f78", "usage": { "input_tokens": 65315, "cached_input_tokens": 64000, "cache_write_input_tokens": 0, "output_tokens": 50, "reasoning_output_tokens": 29, "total_tokens": 65365 }, "turn_token_usage": { "input_tokens": 2008678, "cached_input_tokens": 1948928, "cache_write_input_tokens": 0, "output_tokens": 14876, "reasoning_output_tokens": 6994, "total_tokens": 2023554 }, "thread_token_usage": { "input_tokens": 2008678, "cached_input_tokens": 1948928, "cache_write_input_tokens": 0, "output_tokens": 14876, "reasoning_output_tokens": 6994, "total_tokens": 2023554 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "Extension", "kind": "clock.sleep", "id": "call_QVmZd3qe50HcFKADoSYu3IWP", "durationMs": 45000 } } ### exec · 2026-09-23T05:33:32.868Z { "code": "text(await tools.exec_command({cmd:\"tail -4 /app/solution/production_submission.log\",max_output_tokens:1000}));\ntext(await tools.write_stdin({session_id:93133,chars:\"\",yield_time_ms:1000,max_output_tokens:1000}));\ntext(await tools.clock__curr_time({}));\n" } ### Tool output · 2026-09-23T05:33:32.951Z Script completed Wall time 0.0 seconds Output: {"chunk_id":"023fdd","wall_time_seconds":0.000003791,"exit_code":0,"original_token_count":93,"output":"epoch=1/1 batches=125 targets=498,782 batch_bce=0.535500 running_bce=0.566724 elapsed=104.1s\nepoch=1/1 batches=150 targets=597,467 batch_bce=0.523286 running_bce=0.561320 elapsed=125.2s\nepoch=1/1 batches=175 targets=689,009 batch_bce=0.512711 running_bce=0.557183 elapsed=145.4s\nepoch=1/1 batches=200 targets=790,657 batch_bce=0.503230 running_bce=0.552294 elapsed=166.6s\n"} {"chunk_id":"af5d74","wall_time_seconds":0.00000264,"exit_code":0,"original_token_count":91,"output":"PASS: prefix positives, likes, target, duplicates excluded; finite nonzero gradients\nepoch=1/1 batches=75 targets=301,265 batch_bce=0.549864 running_bce=0.579743 elapsed=62.6s\nepoch=1/1 batches=100 targets=394,078 batch_bce=0.549014 running_bce=0.574242 elapsed=82.5s\nepoch=1/1 batches=125 targets=498,782 batch_bce=0.535500 running_bce=0.566724 elapsed=104.1s\n"} {"current_time":"2026-09-23 05:33:32 UTC"} ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-218e8689-fa22-45f0-a6ad-1f20bca1204d", "process_id": "33706", "command": [ "/bin/sh", "-lc", "tail -4 /app/solution/production_submission.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -4 /app/solution/production_submission.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "epoch=1/1 batches=125 targets=498,782 batch_bce=0.535500 running_bce=0.566724 elapsed=104.1s\nepoch=1/1 batches=150 targets=597,467 batch_bce=0.523286 running_bce=0.561320 elapsed=125.2s\nepoch=1/1 batches=175 targets=689,009 batch_bce=0.512711 running_bce=0.557183 elapsed=145.4s\nepoch=1/1 batches=200 targets=790,657 batch_bce=0.503230 running_bce=0.552294 elapsed=166.6s\n", "stderr": "", "aggregated_output": "epoch=1/1 batches=125 targets=498,782 batch_bce=0.535500 running_bce=0.566724 elapsed=104.1s\nepoch=1/1 batches=150 targets=597,467 batch_bce=0.523286 running_bce=0.561320 elapsed=125.2s\nepoch=1/1 batches=175 targets=689,009 batch_bce=0.512711 running_bce=0.557183 elapsed=145.4s\nepoch=1/1 batches=200 targets=790,657 batch_bce=0.503230 running_bce=0.552294 elapsed=166.6s\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 3791 }, "formatted_output": "epoch=1/1 batches=125 targets=498,782 batch_bce=0.535500 running_bce=0.566724 elapsed=104.1s\nepoch=1/1 batches=150 targets=597,467 batch_bce=0.523286 running_bce=0.561320 elapsed=125.2s\nepoch=1/1 batches=175 targets=689,009 batch_bce=0.512711 running_bce=0.557183 elapsed=145.4s\nepoch=1/1 batches=200 targets=790,657 batch_bce=0.503230 running_bce=0.552294 elapsed=166.6s\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "session_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "root_turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "response_id": "resp_021598e9bafc9da7016ab364a99c6887d2a856397e5a9ae84e", "usage": { "input_tokens": 65388, "cached_input_tokens": 65152, "cache_write_input_tokens": 0, "output_tokens": 75, "reasoning_output_tokens": 0, "total_tokens": 65463 }, "turn_token_usage": { "input_tokens": 2074066, "cached_input_tokens": 2014080, "cache_write_input_tokens": 0, "output_tokens": 14951, "reasoning_output_tokens": 6994, "total_tokens": 2089017 }, "thread_token_usage": { "input_tokens": 2074066, "cached_input_tokens": 2014080, "cache_write_input_tokens": 0, "output_tokens": 14951, "reasoning_output_tokens": 6994, "total_tokens": 2089017 } } ## Step 40 · agent · 2026-09-23T05:33:39.790Z ### Thinking unavailable — no text recorded I’ve removed a large temporary sampling matrix from the experimental trainer while preserving uniform sampling without replacement. Its internal validation will use the configured training objective with a fixed seed, so checkpoint selection accounts for hard negatives rather than measuring only random-negative BCE. ## Step 41 · agent · 2026-09-23T05:33:44.535Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T05:33:58.165Z { "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()\\na=s.index('\\\"\\\"\\\"'); b=s.index('\\\"\\\"\\\"',a+3)+3\\ns=s[:a]+'''\\\"\\\"\\\"Train and rank the chronological music-discovery two-tower.\\n\\nRetains the production data windows, causal sequence/state features, target\\nweights, GRU and independent item tower. The improved objective mixes filtered\\nrandom negatives with model-scored hard negatives from a shared candidate pool.\\nAll mining happens offline; serving remains one user vector and raw item-vector\\ndot products. Use --hard-negatives 0 for the random-negative control, and\\n--resume-checkpoint to fine-tune a production warm-up checkpoint. Chronological\\nvalidation uses the configured objective and fixed sampling seeds.\\n\\\"\\\"\\\"'''+s[b:]\\ns=s.replace('\\\"baseline\\\": \\\"yambda-windowed-sequence-two-tower-base\\\"', '\\\"baseline\\\": \\\"yambda-windowed-sequence-two-tower-hard-negatives\\\"')\\ns=s.replace('\\\"model_selection\\\": \\\"chronological-validation-BCE-early-stopping\\\"','\\\"model_selection\\\": \\\"chronological-configured-objective-early-stopping\\\"')\\ns=s.replace('\\\"objective\\\": \\\"sample-weighted-BCE-at-every-timestep\\\"', '\\\"objective\\\": config.ranking_loss')\\ns=s.replace('''\\\"negative_sampling\\\": (\\n \\\"24-uniform-plus-8-propensity-corrected-inbatch\\\"\\n ),''','''\\\"negative_sampling\\\": {\\n \\\"uniform\\\": config.uniform_negatives,\\n \\\"frequency-corrected-inbatch\\\": config.inbatch_negatives,\\n \\\"mined_hard\\\": config.hard_negatives,\\n \\\"shared_random_pool\\\": config.hard_pool_size,\\n \\\"hard_loss_weight\\\": config.hard_loss_weight,\\n },''')\\np.write_text(s)\\nPY\\ntail -3 /app/solution/production_submission.log\",max_output_tokens:1000}));\n" } ### Tool output · 2026-09-23T05:33:58.319Z Script completed Wall time 0.1 seconds Output: {"chunk_id":"e360b4","wall_time_seconds":0.000007471,"exit_code":0,"original_token_count":70,"output":"epoch=1/1 batches=175 targets=689,009 batch_bce=0.512711 running_bce=0.557183 elapsed=145.4s\nepoch=1/1 batches=200 targets=790,657 batch_bce=0.503230 running_bce=0.552294 elapsed=166.6s\nepoch=1/1 batches=225 targets=893,699 batch_bce=0.510067 running_bce=0.547040 elapsed=188.1s\n"} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "session_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "root_turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "response_id": "resp_021598e9bafc9da7016ab364ad290487d2a0994a9c03a2153d", "usage": { "input_tokens": 65844, "cached_input_tokens": 65152, "cache_write_input_tokens": 0, "output_tokens": 631, "reasoning_output_tokens": 112, "total_tokens": 66475 }, "turn_token_usage": { "input_tokens": 2139910, "cached_input_tokens": 2079232, "cache_write_input_tokens": 0, "output_tokens": 15582, "reasoning_output_tokens": 7106, "total_tokens": 2155492 }, "thread_token_usage": { "input_tokens": 2139910, "cached_input_tokens": 2079232, "cache_write_input_tokens": 0, "output_tokens": 15582, "reasoning_output_tokens": 7106, "total_tokens": 2155492 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-212133cf-b856-4891-8ac9-48de949fd509", "process_id": "60536", "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()\na=s.index('\"\"\"'); b=s.index('\"\"\"',a+3)+3\ns=s[:a]+'''\"\"\"Train and rank the chronological music-discovery two-tower.\n\nRetains the production data windows, causal sequence/state features, target\nweights, GRU and independent item tower. The improved objective mixes filtered\nrandom negatives with model-scored hard negatives from a shared candidate pool.\nAll mining happens offline; serving remains one user vector and raw item-vector\ndot products. Use --hard-negatives 0 for the random-negative control, and\n--resume-checkpoint to fine-tune a production warm-up checkpoint. Chronological\nvalidation uses the configured objective and fixed sampling seeds.\n\"\"\"'''+s[b:]\ns=s.replace('\"baseline\": \"yambda-windowed-sequence-two-tower-base\"', '\"baseline\": \"yambda-windowed-sequence-two-tower-hard-negatives\"')\ns=s.replace('\"model_selection\": \"chronological-validation-BCE-early-stopping\"','\"model_selection\": \"chronological-configured-objective-early-stopping\"')\ns=s.replace('\"objective\": \"sample-weighted-BCE-at-every-timestep\"', '\"objective\": config.ranking_loss')\ns=s.replace('''\"negative_sampling\": (\n \"24-uniform-plus-8-propensity-corrected-inbatch\"\n ),''','''\"negative_sampling\": {\n \"uniform\": config.uniform_negatives,\n \"frequency-corrected-inbatch\": config.inbatch_negatives,\n \"mined_hard\": config.hard_negatives,\n \"shared_random_pool\": config.hard_pool_size,\n \"hard_loss_weight\": config.hard_loss_weight,\n },''')\np.write_text(s)\nPY\ntail -3 /app/solution/production_submission.log" ], "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()\na=s.index('\"\"\"'); b=s.index('\"\"\"',a+3)+3\ns=s[:a]+'''\"\"\"Train and rank the chronological music-discovery two-tower.\n\nRetains the production data windows, causal sequence/state features, target\nweights, GRU and independent item tower. The improved objective mixes filtered\nrandom negatives with model-scored hard negatives from a shared candidate pool.\nAll mining happens offline; serving remains one user vector and raw item-vector\ndot products. Use --hard-negatives 0 for the random-negative control, and\n--resume-checkpoint to fine-tune a production warm-up checkpoint. Chronological\nvalidation uses the configured objective and fixed sampling seeds.\n\"\"\"'''+s[b:]\ns=s.replace('\"baseline\": \"yambda-windowed-sequence-two-tower-base\"', '\"baseline\": \"yambda-windowed-sequence-two-tower-hard-negatives\"')\ns=s.replace('\"model_selection\": \"chronological-validation-BCE-early-stopping\"','\"model_selection\": \"chronological-configured-objective-early-stopping\"')\ns=s.replace('\"objective\": \"sample-weighted-BCE-at-every-timestep\"', '\"objective\": config.ranking_loss')\ns=s.replace('''\"negative_sampling\": (\n \"24-uniform-plus-8-propensity-corrected-inbatch\"\n ),''','''\"negative_sampling\": {\n \"uniform\": config.uniform_negatives,\n \"frequency-corrected-inbatch\": config.inbatch_negatives,\n \"mined_hard\": config.hard_negatives,\n \"shared_random_pool\": config.hard_pool_size,\n \"hard_loss_weight\": config.hard_loss_weight,\n },''')\np.write_text(s)\nPY\ntail -3 /app/solution/production_submission.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "epoch=1/1 batches=175 targets=689,009 batch_bce=0.512711 running_bce=0.557183 elapsed=145.4s\nepoch=1/1 batches=200 targets=790,657 batch_bce=0.503230 running_bce=0.552294 elapsed=166.6s\nepoch=1/1 batches=225 targets=893,699 batch_bce=0.510067 running_bce=0.547040 elapsed=188.1s\n", "stderr": "", "aggregated_output": "epoch=1/1 batches=175 targets=689,009 batch_bce=0.512711 running_bce=0.557183 elapsed=145.4s\nepoch=1/1 batches=200 targets=790,657 batch_bce=0.503230 running_bce=0.552294 elapsed=166.6s\nepoch=1/1 batches=225 targets=893,699 batch_bce=0.510067 running_bce=0.547040 elapsed=188.1s\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 7471 }, "formatted_output": "epoch=1/1 batches=175 targets=689,009 batch_bce=0.512711 running_bce=0.557183 elapsed=145.4s\nepoch=1/1 batches=200 targets=790,657 batch_bce=0.503230 running_bce=0.552294 elapsed=166.6s\nepoch=1/1 batches=225 targets=893,699 batch_bce=0.510067 running_bce=0.547040 elapsed=188.1s\n" } } ## Step 42 · agent · 2026-09-23T05:34:16.193Z ### Thinking unavailable — no text recorded ### sleep · 2026-09-23T05:34:16.339Z { "duration_ms": 45000 } ### Tool output · 2026-09-23T05:35:01.347Z Wall time: 45.0038 seconds Sleep completed. ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "session_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "root_turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "response_id": "resp_021598e9bafc9da7016ab364c683b087d2a756d7f03bcb75d0", "usage": { "input_tokens": 66641, "cached_input_tokens": 65664, "cache_write_input_tokens": 0, "output_tokens": 458, "reasoning_output_tokens": 437, "total_tokens": 67099 }, "turn_token_usage": { "input_tokens": 2206551, "cached_input_tokens": 2144896, "cache_write_input_tokens": 0, "output_tokens": 16040, "reasoning_output_tokens": 7543, "total_tokens": 2222591 }, "thread_token_usage": { "input_tokens": 2206551, "cached_input_tokens": 2144896, "cache_write_input_tokens": 0, "output_tokens": 16040, "reasoning_output_tokens": 7543, "total_tokens": 2222591 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "Extension", "kind": "clock.sleep", "id": "call_ao5rfEXGhaybVTvyWaVuLdjU", "durationMs": 45000 } } ### exec · 2026-09-23T05:35:04.604Z { "code": "text(await tools.exec_command({cmd:\"tail -5 /app/solution/production_submission.log\",max_output_tokens:1000}));\ntext(await tools.clock__curr_time({}));\n" } ### Tool output · 2026-09-23T05:35:04.665Z Script completed Wall time 0.0 seconds Output: {"chunk_id":"eca566","wall_time_seconds":0.00000603,"exit_code":0,"original_token_count":124,"output":"epoch=1/1 batches=225 targets=893,699 batch_bce=0.510067 running_bce=0.547040 elapsed=188.1s\nepoch=1/1 batches=250 targets=994,228 batch_bce=0.460538 running_bce=0.541708 elapsed=209.5s\nsaved epoch checkpoint: /app/solution/production_submission_ckpt/epoch-01.pt\nepoch=1/1 train_loss=0.53835 validation_loss=0.49795 pair_accuracy=0.755 train_targets=1,067,606 validation_targets=131,997 train_seconds=224.9 validation_seconds=17.1\nrestored best validation-BCE checkpoint: epoch=1 loss=0.497949\n"} {"current_time":"2026-09-23 05:35:04 UTC"} ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-28f3528e-2f03-4d71-bdd5-319a65741fe7", "process_id": "30500", "command": [ "/bin/sh", "-lc", "tail -5 /app/solution/production_submission.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -5 /app/solution/production_submission.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "epoch=1/1 batches=225 targets=893,699 batch_bce=0.510067 running_bce=0.547040 elapsed=188.1s\nepoch=1/1 batches=250 targets=994,228 batch_bce=0.460538 running_bce=0.541708 elapsed=209.5s\nsaved epoch checkpoint: /app/solution/production_submission_ckpt/epoch-01.pt\nepoch=1/1 train_loss=0.53835 validation_loss=0.49795 pair_accuracy=0.755 train_targets=1,067,606 validation_targets=131,997 train_seconds=224.9 validation_seconds=17.1\nrestored best validation-BCE checkpoint: epoch=1 loss=0.497949\n", "stderr": "", "aggregated_output": "epoch=1/1 batches=225 targets=893,699 batch_bce=0.510067 running_bce=0.547040 elapsed=188.1s\nepoch=1/1 batches=250 targets=994,228 batch_bce=0.460538 running_bce=0.541708 elapsed=209.5s\nsaved epoch checkpoint: /app/solution/production_submission_ckpt/epoch-01.pt\nepoch=1/1 train_loss=0.53835 validation_loss=0.49795 pair_accuracy=0.755 train_targets=1,067,606 validation_targets=131,997 train_seconds=224.9 validation_seconds=17.1\nrestored best validation-BCE checkpoint: epoch=1 loss=0.497949\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 6030 }, "formatted_output": "epoch=1/1 batches=225 targets=893,699 batch_bce=0.510067 running_bce=0.547040 elapsed=188.1s\nepoch=1/1 batches=250 targets=994,228 batch_bce=0.460538 running_bce=0.541708 elapsed=209.5s\nsaved epoch checkpoint: /app/solution/production_submission_ckpt/epoch-01.pt\nepoch=1/1 train_loss=0.53835 validation_loss=0.49795 pair_accuracy=0.755 train_targets=1,067,606 validation_targets=131,997 train_seconds=224.9 validation_seconds=17.1\nrestored best validation-BCE checkpoint: epoch=1 loss=0.497949\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "session_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "root_turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "response_id": "resp_021598e9bafc9da7016ab36505896c87d28278025ab65a1789", "usage": { "input_tokens": 67122, "cached_input_tokens": 66432, "cache_write_input_tokens": 0, "output_tokens": 46, "reasoning_output_tokens": 0, "total_tokens": 67168 }, "turn_token_usage": { "input_tokens": 2273673, "cached_input_tokens": 2211328, "cache_write_input_tokens": 0, "output_tokens": 16086, "reasoning_output_tokens": 7543, "total_tokens": 2289759 }, "thread_token_usage": { "input_tokens": 2273673, "cached_input_tokens": 2211328, "cache_write_input_tokens": 0, "output_tokens": 16086, "reasoning_output_tokens": 7543, "total_tokens": 2289759 } } ## Step 43 · agent · 2026-09-23T05:35:07.536Z ### Thinking unavailable — no text recorded The fallback refit has finished training on `train_* + val_*` and is generating test-candidate rankings. Once it is saved and checked, I’ll run the hard-negative backtest against the 0.021280 baseline. ## Step 44 · agent · 2026-09-23T05:35:24.051Z ### Thinking unavailable — no text recorded ### sleep · 2026-09-23T05:35:24.316Z { "duration_ms": 35000 } ### Tool output · 2026-09-23T05:35:59.332Z Wall time: 35.0117 seconds Sleep completed. ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "session_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "root_turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "response_id": "resp_021598e9bafc9da7016ab3650900e087d2a82a4c37cf7d2ddf", "usage": { "input_tokens": 67403, "cached_input_tokens": 66944, "cache_write_input_tokens": 0, "output_tokens": 524, "reasoning_output_tokens": 449, "total_tokens": 67927 }, "turn_token_usage": { "input_tokens": 2341076, "cached_input_tokens": 2278272, "cache_write_input_tokens": 0, "output_tokens": 16610, "reasoning_output_tokens": 7992, "total_tokens": 2357686 }, "thread_token_usage": { "input_tokens": 2341076, "cached_input_tokens": 2278272, "cache_write_input_tokens": 0, "output_tokens": 16610, "reasoning_output_tokens": 7992, "total_tokens": 2357686 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "Extension", "kind": "clock.sleep", "id": "call_Yb5MAsxNG7IIdAoM4A5f62sI", "durationMs": 35000 } } ### exec · 2026-09-23T05:36:01.745Z { "code": "text(await tools.exec_command({cmd:\"tail -3 /app/solution/production_submission.log && ls -l /app/solution/submission.parquet\",max_output_tokens:1000}));\n" } ### Tool output · 2026-09-23T05:36:01.826Z Script completed Wall time 0.0 seconds Output: {"chunk_id":"1d39b2","wall_time_seconds":0.00000495,"exit_code":2,"original_token_count":39,"output":"ranked users=1,024/8,456\nranked users=2,048/8,456\nranked users=3,072/8,456\nls: cannot access '/app/solution/submission.parquet': No such file or directory\n"} ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-3a81bccf-e255-4c08-bddf-5a20728d447f", "process_id": "64120", "command": [ "/bin/sh", "-lc", "tail -3 /app/solution/production_submission.log && ls -l /app/solution/submission.parquet" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -3 /app/solution/production_submission.log && ls -l /app/solution/submission.parquet" } ], "source": "unified_exec_startup", "status": "failed", "stdout": "ranked users=1,024/8,456\nranked users=2,048/8,456\nranked users=3,072/8,456\nls: cannot access '/app/solution/submission.parquet': No such file or directory\n", "stderr": "", "aggregated_output": "ranked users=1,024/8,456\nranked users=2,048/8,456\nranked users=3,072/8,456\nls: cannot access '/app/solution/submission.parquet': No such file or directory\n", "exit_code": 2, "duration": { "secs": 0, "nanos": 4950 }, "formatted_output": "ranked users=1,024/8,456\nranked users=2,048/8,456\nranked users=3,072/8,456\nls: cannot access '/app/solution/submission.parquet': No such file or directory\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "session_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "root_turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "response_id": "resp_021598e9bafc9da7016ab3653f7dac87d293f6080169a21422", "usage": { "input_tokens": 67950, "cached_input_tokens": 67200, "cache_write_input_tokens": 0, "output_tokens": 49, "reasoning_output_tokens": 0, "total_tokens": 67999 }, "turn_token_usage": { "input_tokens": 2409026, "cached_input_tokens": 2345472, "cache_write_input_tokens": 0, "output_tokens": 16659, "reasoning_output_tokens": 7992, "total_tokens": 2425685 }, "thread_token_usage": { "input_tokens": 2409026, "cached_input_tokens": 2345472, "cache_write_input_tokens": 0, "output_tokens": 16659, "reasoning_output_tokens": 7992, "total_tokens": 2425685 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-c0d72b69-2ce7-4b50-89e4-ff5cf90ad331", "process_id": "31559", "command": [ "/bin/sh", "-lc", "OPENBLAS_NUM_THREADS=1 OMP_NUM_THREADS=4 PYTHONDONTWRITEBYTECODE=1 /opt/task-venv/bin/python /app/solution/train_production.py --stage submission --public-dir /task --output /app/solution/submission.parquet --prepared-cache /app/solution/submission_cache.npz --checkpoint-dir /app/solution/production_submission_ckpt --threads 4 > /app/solution/production_submission.json 2> /app/solution/production_submission.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "OPENBLAS_NUM_THREADS=1 OMP_NUM_THREADS=4 PYTHONDONTWRITEBYTECODE=1 /opt/task-venv/bin/python /app/solution/train_production.py --stage submission --public-dir /task --output /app/solution/submission.parquet --prepared-cache /app/solution/submission_cache.npz --checkpoint-dir /app/solution/production_submission_ckpt --threads 4 > /app/solution/production_submission.json 2> /app/solution/production_submission.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 383, "nanos": 277571379 }, "formatted_output": "" } } ## Step 45 · agent · 2026-09-23T05:36:04.310Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T05:36:05.244Z { "code": "text(await tools.write_stdin({session_id:31559,chars:\"\",yield_time_ms:1000,max_output_tokens:1000}));\n" } ### Tool output · 2026-09-23T05:36:05.337Z Script completed Wall time 0.0 seconds Output: {"chunk_id":"46b8ba","wall_time_seconds":0.00000128,"exit_code":0,"original_token_count":0,"output":""} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "session_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "root_turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "response_id": "resp_021598e9bafc9da7016ab36542215c87d2999e75978fe51465", "usage": { "input_tokens": 68111, "cached_input_tokens": 67712, "cache_write_input_tokens": 0, "output_tokens": 63, "reasoning_output_tokens": 22, "total_tokens": 68174 }, "turn_token_usage": { "input_tokens": 2477137, "cached_input_tokens": 2413184, "cache_write_input_tokens": 0, "output_tokens": 16722, "reasoning_output_tokens": 8014, "total_tokens": 2493859 }, "thread_token_usage": { "input_tokens": 2477137, "cached_input_tokens": 2413184, "cache_write_input_tokens": 0, "output_tokens": 16722, "reasoning_output_tokens": 8014, "total_tokens": 2493859 } } ### exec · 2026-09-23T05:36:16.413Z { "code": "text(await tools.exec_command({cmd:\"PYTHONDONTWRITEBYTECODE=1 /opt/task-venv/bin/python /app/solution/validate_submission.py > /app/solution/submission_check.txt && cat /app/solution/submission_check.txt && cp /app/solution/submission.parquet /app/solution/production_submission.parquet\",yield_time_ms:1000,max_output_tokens:1000}));\ntext(await tools.exec_command({cmd:\"OPENBLAS_NUM_THREADS=1 OMP_NUM_THREADS=4 PYTHONDONTWRITEBYTECODE=1 /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 --resume-checkpoint /app/solution/baseline_ckpt/best.pt --epochs 2 --threads 4 --loss-microbatch-size 4096 > /app/solution/hard.json 2> /app/solution/hard.log\",yield_time_ms:1000,max_output_tokens:1000}));\n" } ### Tool output · 2026-09-23T05:36:18.738Z Script completed Wall time 2.3 seconds Output: {"chunk_id":"a5812c","wall_time_seconds":1.000871316,"session_id":90879,"original_token_count":0,"output":""} {"chunk_id":"e61023","wall_time_seconds":1.001064568,"session_id":22773,"original_token_count":0,"output":""} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "session_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "root_turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "response_id": "resp_021598e9bafc9da7016ab3654590d487d282656bcd905952ba", "usage": { "input_tokens": 68231, "cached_input_tokens": 67968, "cache_write_input_tokens": 0, "output_tokens": 248, "reasoning_output_tokens": 0, "total_tokens": 68479 }, "turn_token_usage": { "input_tokens": 2545368, "cached_input_tokens": 2481152, "cache_write_input_tokens": 0, "output_tokens": 16970, "reasoning_output_tokens": 8014, "total_tokens": 2562338 }, "thread_token_usage": { "input_tokens": 2545368, "cached_input_tokens": 2481152, "cache_write_input_tokens": 0, "output_tokens": 16970, "reasoning_output_tokens": 8014, "total_tokens": 2562338 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-1c5d1816-644c-4081-8f6f-e608f0aa7030", "process_id": "90879", "command": [ "/bin/sh", "-lc", "PYTHONDONTWRITEBYTECODE=1 /opt/task-venv/bin/python /app/solution/validate_submission.py > /app/solution/submission_check.txt && cat /app/solution/submission_check.txt && cp /app/solution/submission.parquet /app/solution/production_submission.parquet" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "PYTHONDONTWRITEBYTECODE=1 /opt/task-venv/bin/python /app/solution/validate_submission.py > /app/solution/submission_check.txt && cat /app/solution/submission_check.txt && cp /app/solution/submission.parquet /app/solution/production_submission.parquet" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "PASS: regular file; 8,456 users; 845,600 rows; integer columns; 100 distinct eligible items and ranks 1..100 per user\n", "stderr": "", "aggregated_output": "PASS: regular file; 8,456 users; 845,600 rows; integer columns; 100 distinct eligible items and ranks 1..100 per user\n", "exit_code": 0, "duration": { "secs": 1, "nanos": 722639917 }, "formatted_output": "PASS: regular file; 8,456 users; 845,600 rows; integer columns; 100 distinct eligible items and ranks 1..100 per user\n" } } ## Step 46 · agent · 2026-09-23T05:36:27.853Z ### Thinking unavailable — no text recorded The fallback submission is saved. Hard-negative fine-tuning is now running from the validation baseline checkpoint, keeping the same towers, positive weights, chronological split, and serving path. ### exec · 2026-09-23T05:36:30.616Z { "code": "text(await tools.write_stdin({session_id:90879,chars:\"\",yield_time_ms:1000,max_output_tokens:1000}));\ntext(await tools.exec_command({cmd:\"tail -8 /app/solution/hard.log && cat /sys/fs/cgroup/memory.current\",max_output_tokens:1400}));\n" } ### Tool output · 2026-09-23T05:36:30.715Z Script completed Wall time 0.0 seconds Output: {"chunk_id":"4fff66","wall_time_seconds":0.00000294,"exit_code":0,"original_token_count":30,"output":"PASS: regular file; 8,456 users; 845,600 rows; integer columns; 100 distinct eligible items and ranks 1..100 per user\n"} {"chunk_id":"003365","wall_time_seconds":0.00000593,"exit_code":0,"original_token_count":181,"output":"loaded full-sequence cache: tokens=20,056,329 targets=20,047,194\nchronological BCE window: train=1,052,953 validation=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194\ninbatch correction: min=0.250 mean_on_targets=1.001 max=4.000\ntarget weighting=organic-strong min=0.250 mean=0.575 max=1.000\nfull-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250\nusing a fresh AdamW optimizer after loading /app/solution/baseline_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\n3648311296\n"} ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-7d08199d-e815-4ee3-be50-ce9ccd999cf8", "process_id": "65836", "command": [ "/bin/sh", "-lc", "tail -8 /app/solution/hard.log && cat /sys/fs/cgroup/memory.current" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -8 /app/solution/hard.log && cat /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\ninbatch correction: min=0.250 mean_on_targets=1.001 max=4.000\ntarget weighting=organic-strong min=0.250 mean=0.575 max=1.000\nfull-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250\nusing a fresh AdamW optimizer after loading /app/solution/baseline_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\n3648311296\n", "stderr": "", "aggregated_output": "loaded full-sequence cache: tokens=20,056,329 targets=20,047,194\nchronological BCE window: train=1,052,953 validation=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194\ninbatch correction: min=0.250 mean_on_targets=1.001 max=4.000\ntarget weighting=organic-strong min=0.250 mean=0.575 max=1.000\nfull-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250\nusing a fresh AdamW optimizer after loading /app/solution/baseline_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\n3648311296\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 5930 }, "formatted_output": "loaded full-sequence cache: tokens=20,056,329 targets=20,047,194\nchronological BCE window: train=1,052,953 validation=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194\ninbatch correction: min=0.250 mean_on_targets=1.001 max=4.000\ntarget weighting=organic-strong min=0.250 mean=0.575 max=1.000\nfull-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250\nusing a fresh AdamW optimizer after loading /app/solution/baseline_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\n3648311296\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "session_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "root_turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "response_id": "resp_021598e9bafc9da7016ab36552f98087d2ac483c4a7b43d900", "usage": { "input_tokens": 68571, "cached_input_tokens": 68096, "cache_write_input_tokens": 0, "output_tokens": 302, "reasoning_output_tokens": 183, "total_tokens": 68873 }, "turn_token_usage": { "input_tokens": 2613939, "cached_input_tokens": 2549248, "cache_write_input_tokens": 0, "output_tokens": 17272, "reasoning_output_tokens": 8197, "total_tokens": 2631211 }, "thread_token_usage": { "input_tokens": 2613939, "cached_input_tokens": 2549248, "cache_write_input_tokens": 0, "output_tokens": 17272, "reasoning_output_tokens": 8197, "total_tokens": 2631211 } } ## Step 47 · agent · 2026-09-23T05:36:40.376Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T05:36:54.899Z { "code": "text(await tools.exec_command({cmd:\"cat > /app/solution/compare_public.py <<'PY'\\n\\\"\\\"\\\"Paired public NDCG comparison; never imported by the trainer.\\\"\\\"\\\"\\nimport sys\\nsys.path.insert(0, '/task')\\nimport json\\nfrom pathlib import Path\\nimport numpy as np\\nimport pandas as pd\\nimport evaluate_public as e\\n\\nusers = set(pd.read_parquet(e.DATA / 'target_users.parquet').uid.astype(int))\\neligibility = e.CandidateSet(e.DATA / 'validation_eligible_candidates.npz')\\ntruth = e.validation_truth(eligibility, users)\\nordered = sorted(truth)\\ndiscount = 1.0 / np.log2(np.arange(10) + 2)\\nresults = {}\\nfor name in sys.argv[1:]:\\n ranks = e.load_submission(Path(name), eligibility, users)\\n values = []\\n for uid in ordered:\\n grades = truth[uid]\\n ideal = sorted(grades.values(), reverse=True)[:10]\\n idcg = sum((2**g - 1) * discount[r] for r, g in enumerate(ideal))\\n dcg = sum((2**grades.get(item, 0) - 1) * discount[r]\\n for r, item in enumerate(ranks[uid][:10]))\\n values.append(dcg / idcg)\\n results[name] = np.asarray(values)\\nbase = next(iter(results.values()))\\nreport = {}\\nrng = np.random.default_rng(29)\\nfor name, values in results.items():\\n delta = values - base\\n boot = np.asarray([rng.choice(delta, size=len(delta), replace=True).mean() for _ in range(2000)])\\n report[name] = {'ndcg_at_10': float(values.mean()), 'users': len(values),\\n 'delta_from_first': float(delta.mean()),\\n 'paired_bootstrap_95pct': np.quantile(boot, [.025, .975]).tolist()}\\nprint(json.dumps(report, indent=2))\\nPY\\ntail -3 /app/solution/hard.log\",max_output_tokens:1000}));\n" } ### Tool output · 2026-09-23T05:36:55.006Z Script completed Wall time 0.0 seconds Output: {"chunk_id":"1f6702","wall_time_seconds":0.00000545,"exit_code":0,"original_token_count":90,"output":"using a fresh AdamW optimizer after loading /app/solution/baseline_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=1.203840\n"} ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-f663e580-c52d-4de7-aa49-f94ad85f2a17", "process_id": "91099", "command": [ "/bin/sh", "-lc", "cat > /app/solution/compare_public.py <<'PY'\n\"\"\"Paired public NDCG comparison; never imported by the trainer.\"\"\"\nimport sys\nsys.path.insert(0, '/task')\nimport json\nfrom pathlib import Path\nimport numpy as np\nimport pandas as pd\nimport evaluate_public as e\n\nusers = set(pd.read_parquet(e.DATA / 'target_users.parquet').uid.astype(int))\neligibility = e.CandidateSet(e.DATA / 'validation_eligible_candidates.npz')\ntruth = e.validation_truth(eligibility, users)\nordered = sorted(truth)\ndiscount = 1.0 / np.log2(np.arange(10) + 2)\nresults = {}\nfor name in sys.argv[1:]:\n ranks = e.load_submission(Path(name), eligibility, users)\n values = []\n for uid in ordered:\n grades = truth[uid]\n ideal = sorted(grades.values(), reverse=True)[:10]\n idcg = sum((2**g - 1) * discount[r] for r, g in enumerate(ideal))\n dcg = sum((2**grades.get(item, 0) - 1) * discount[r]\n for r, item in enumerate(ranks[uid][:10]))\n values.append(dcg / idcg)\n results[name] = np.asarray(values)\nbase = next(iter(results.values()))\nreport = {}\nrng = np.random.default_rng(29)\nfor name, values in results.items():\n delta = values - base\n boot = np.asarray([rng.choice(delta, size=len(delta), replace=True).mean() for _ in range(2000)])\n report[name] = {'ndcg_at_10': float(values.mean()), 'users': len(values),\n 'delta_from_first': float(delta.mean()),\n 'paired_bootstrap_95pct': np.quantile(boot, [.025, .975]).tolist()}\nprint(json.dumps(report, indent=2))\nPY\ntail -3 /app/solution/hard.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat > /app/solution/compare_public.py <<'PY'\n\"\"\"Paired public NDCG comparison; never imported by the trainer.\"\"\"\nimport sys\nsys.path.insert(0, '/task')\nimport json\nfrom pathlib import Path\nimport numpy as np\nimport pandas as pd\nimport evaluate_public as e\n\nusers = set(pd.read_parquet(e.DATA / 'target_users.parquet').uid.astype(int))\neligibility = e.CandidateSet(e.DATA / 'validation_eligible_candidates.npz')\ntruth = e.validation_truth(eligibility, users)\nordered = sorted(truth)\ndiscount = 1.0 / np.log2(np.arange(10) + 2)\nresults = {}\nfor name in sys.argv[1:]:\n ranks = e.load_submission(Path(name), eligibility, users)\n values = []\n for uid in ordered:\n grades = truth[uid]\n ideal = sorted(grades.values(), reverse=True)[:10]\n idcg = sum((2**g - 1) * discount[r] for r, g in enumerate(ideal))\n dcg = sum((2**grades.get(item, 0) - 1) * discount[r]\n for r, item in enumerate(ranks[uid][:10]))\n values.append(dcg / idcg)\n results[name] = np.asarray(values)\nbase = next(iter(results.values()))\nreport = {}\nrng = np.random.default_rng(29)\nfor name, values in results.items():\n delta = values - base\n boot = np.asarray([rng.choice(delta, size=len(delta), replace=True).mean() for _ in range(2000)])\n report[name] = {'ndcg_at_10': float(values.mean()), 'users': len(values),\n 'delta_from_first': float(delta.mean()),\n 'paired_bootstrap_95pct': np.quantile(boot, [.025, .975]).tolist()}\nprint(json.dumps(report, indent=2))\nPY\ntail -3 /app/solution/hard.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "using a fresh AdamW optimizer after loading /app/solution/baseline_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=1.203840\n", "stderr": "", "aggregated_output": "using a fresh AdamW optimizer after loading /app/solution/baseline_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=1.203840\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 5450 }, "formatted_output": "using a fresh AdamW optimizer after loading /app/solution/baseline_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=1.203840\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "session_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "root_turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "response_id": "resp_021598e9bafc9da7016ab3655fb36c87d282cbcdf4b34d2611", "usage": { "input_tokens": 69212, "cached_input_tokens": 68352, "cache_write_input_tokens": 0, "output_tokens": 706, "reasoning_output_tokens": 215, "total_tokens": 69918 }, "turn_token_usage": { "input_tokens": 2683151, "cached_input_tokens": 2617600, "cache_write_input_tokens": 0, "output_tokens": 17978, "reasoning_output_tokens": 8412, "total_tokens": 2701129 }, "thread_token_usage": { "input_tokens": 2683151, "cached_input_tokens": 2617600, "cache_write_input_tokens": 0, "output_tokens": 17978, "reasoning_output_tokens": 8412, "total_tokens": 2701129 } } ## Step 48 · agent · 2026-09-23T05:36:59.211Z ### Thinking unavailable — no text recorded ### sleep · 2026-09-23T05:36:59.516Z { "duration_ms": 35000 } ### Tool output · 2026-09-23T05:37:34.524Z Wall time: 35.0044 seconds Sleep completed. ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "session_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "root_turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "response_id": "resp_021598e9bafc9da7016ab365774b5c87d28bcc8c0c8ab25c88", "usage": { "input_tokens": 70070, "cached_input_tokens": 68992, "cache_write_input_tokens": 0, "output_tokens": 67, "reasoning_output_tokens": 46, "total_tokens": 70137 }, "turn_token_usage": { "input_tokens": 2753221, "cached_input_tokens": 2686592, "cache_write_input_tokens": 0, "output_tokens": 18045, "reasoning_output_tokens": 8458, "total_tokens": 2771266 }, "thread_token_usage": { "input_tokens": 2753221, "cached_input_tokens": 2686592, "cache_write_input_tokens": 0, "output_tokens": 18045, "reasoning_output_tokens": 8458, "total_tokens": 2771266 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "Extension", "kind": "clock.sleep", "id": "call_nZgp3h6GKVxZ0QpjkQlQbgbw", "durationMs": 35000 } } ### exec · 2026-09-23T05:37:41.201Z { "code": "text(await tools.exec_command({cmd:\"tail -5 /app/solution/hard.log && cat /sys/fs/cgroup/memory.current\",max_output_tokens:1000}));\ntext(await tools.clock__curr_time({}));\n" } ### Tool output · 2026-09-23T05:37:41.275Z Script completed Wall time 0.0 seconds Output: {"chunk_id":"316360","wall_time_seconds":0.000003191,"exit_code":0,"original_token_count":138,"output":"full-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250\nusing a fresh AdamW optimizer after loading /app/solution/baseline_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=1.203840\nepoch=2/2 batches=25 targets=104,067 batch_bce=0.667400 running_bce=0.729884 elapsed=29.7s\n6062686208\n"} {"current_time":"2026-09-23 05:37:41 UTC"} ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-7210d8e7-4373-41de-a08e-73c3a05b9e51", "process_id": "16793", "command": [ "/bin/sh", "-lc", "tail -5 /app/solution/hard.log && cat /sys/fs/cgroup/memory.current" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -5 /app/solution/hard.log && cat /sys/fs/cgroup/memory.current" } ], "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\nusing a fresh AdamW optimizer after loading /app/solution/baseline_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=1.203840\nepoch=2/2 batches=25 targets=104,067 batch_bce=0.667400 running_bce=0.729884 elapsed=29.7s\n6062686208\n", "stderr": "", "aggregated_output": "full-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250\nusing a fresh AdamW optimizer after loading /app/solution/baseline_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=1.203840\nepoch=2/2 batches=25 targets=104,067 batch_bce=0.667400 running_bce=0.729884 elapsed=29.7s\n6062686208\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 3191 }, "formatted_output": "full-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250\nusing a fresh AdamW optimizer after loading /app/solution/baseline_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=1.203840\nepoch=2/2 batches=25 targets=104,067 batch_bce=0.667400 running_bce=0.729884 elapsed=29.7s\n6062686208\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "session_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "root_turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "response_id": "resp_021598e9bafc9da7016ab3659eb29087d285d1b922821437e0", "usage": { "input_tokens": 70160, "cached_input_tokens": 69888, "cache_write_input_tokens": 0, "output_tokens": 55, "reasoning_output_tokens": 0, "total_tokens": 70215 }, "turn_token_usage": { "input_tokens": 2823381, "cached_input_tokens": 2756480, "cache_write_input_tokens": 0, "output_tokens": 18100, "reasoning_output_tokens": 8458, "total_tokens": 2841481 }, "thread_token_usage": { "input_tokens": 2823381, "cached_input_tokens": 2756480, "cache_write_input_tokens": 0, "output_tokens": 18100, "reasoning_output_tokens": 8458, "total_tokens": 2841481 } } ## Step 49 · agent · 2026-09-23T05:37:55.585Z ### Thinking unavailable — no text recorded The fallback passed all format and eligibility checks: 8,456 users, exactly 100 distinct eligible tracks per user, and ranks 1–100. The hard-negative run is progressing normally and remains within the memory limit. ## Step 50 · agent · 2026-09-23T05:38:18.517Z ### Thinking unavailable — no text recorded ### sleep · 2026-09-23T05:38:18.693Z { "duration_ms": 35000 } ### Tool output · 2026-09-23T05:38:53.701Z Wall time: 35.0033 seconds Sleep completed. ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "session_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "root_turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "response_id": "resp_021598e9bafc9da7016ab365a5bcdc87d2b5db10fcda26ac12", "usage": { "input_tokens": 70453, "cached_input_tokens": 70016, "cache_write_input_tokens": 0, "output_tokens": 903, "reasoning_output_tokens": 830, "total_tokens": 71356 }, "turn_token_usage": { "input_tokens": 2893834, "cached_input_tokens": 2826496, "cache_write_input_tokens": 0, "output_tokens": 19003, "reasoning_output_tokens": 9288, "total_tokens": 2912837 }, "thread_token_usage": { "input_tokens": 2893834, "cached_input_tokens": 2826496, "cache_write_input_tokens": 0, "output_tokens": 19003, "reasoning_output_tokens": 9288, "total_tokens": 2912837 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "Extension", "kind": "clock.sleep", "id": "call_ftOAfEswXiK4nuRgXZIt1AVj", "durationMs": 35000 } } ### exec · 2026-09-23T05:38:57.547Z { "code": "text(await tools.exec_command({cmd:\"tail -4 /app/solution/hard.log\",max_output_tokens:1000}));\ntext(await tools.clock__curr_time({}));\n" } ### Tool output · 2026-09-23T05:38:57.617Z Script completed Wall time 0.0 seconds Output: {"chunk_id":"2e660a","wall_time_seconds":0.000005051,"exit_code":0,"original_token_count":92,"output":"epoch=2/2 batches=25 targets=104,067 batch_bce=0.667400 running_bce=0.729884 elapsed=29.7s\nepoch=2/2 batches=50 targets=206,457 batch_bce=0.658240 running_bce=0.697139 elapsed=58.8s\nepoch=2/2 batches=75 targets=305,335 batch_bce=0.669835 running_bce=0.686990 elapsed=87.8s\nepoch=2/2 batches=100 targets=402,892 batch_bce=0.655339 running_bce=0.680685 elapsed=116.0s\n"} {"current_time":"2026-09-23 05:38:57 UTC"} ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-c08de1ef-35b1-4ead-ac6e-47402e37e8a0", "process_id": "40442", "command": [ "/bin/sh", "-lc", "tail -4 /app/solution/hard.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -4 /app/solution/hard.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "epoch=2/2 batches=25 targets=104,067 batch_bce=0.667400 running_bce=0.729884 elapsed=29.7s\nepoch=2/2 batches=50 targets=206,457 batch_bce=0.658240 running_bce=0.697139 elapsed=58.8s\nepoch=2/2 batches=75 targets=305,335 batch_bce=0.669835 running_bce=0.686990 elapsed=87.8s\nepoch=2/2 batches=100 targets=402,892 batch_bce=0.655339 running_bce=0.680685 elapsed=116.0s\n", "stderr": "", "aggregated_output": "epoch=2/2 batches=25 targets=104,067 batch_bce=0.667400 running_bce=0.729884 elapsed=29.7s\nepoch=2/2 batches=50 targets=206,457 batch_bce=0.658240 running_bce=0.697139 elapsed=58.8s\nepoch=2/2 batches=75 targets=305,335 batch_bce=0.669835 running_bce=0.686990 elapsed=87.8s\nepoch=2/2 batches=100 targets=402,892 batch_bce=0.655339 running_bce=0.680685 elapsed=116.0s\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 5051 }, "formatted_output": "epoch=2/2 batches=25 targets=104,067 batch_bce=0.667400 running_bce=0.729884 elapsed=29.7s\nepoch=2/2 batches=50 targets=206,457 batch_bce=0.658240 running_bce=0.697139 elapsed=58.8s\nepoch=2/2 batches=75 targets=305,335 batch_bce=0.669835 running_bce=0.686990 elapsed=87.8s\nepoch=2/2 batches=100 targets=402,892 batch_bce=0.655339 running_bce=0.680685 elapsed=116.0s\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "session_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "root_turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "response_id": "resp_021598e9bafc9da7016ab365ede68c87d29227b459a0c6528e", "usage": { "input_tokens": 71379, "cached_input_tokens": 70272, "cache_write_input_tokens": 0, "output_tokens": 45, "reasoning_output_tokens": 0, "total_tokens": 71424 }, "turn_token_usage": { "input_tokens": 2965213, "cached_input_tokens": 2896768, "cache_write_input_tokens": 0, "output_tokens": 19048, "reasoning_output_tokens": 9288, "total_tokens": 2984261 }, "thread_token_usage": { "input_tokens": 2965213, "cached_input_tokens": 2896768, "cache_write_input_tokens": 0, "output_tokens": 19048, "reasoning_output_tokens": 9288, "total_tokens": 2984261 } } ## Step 51 · agent · 2026-09-23T05:39:00.184Z ### Thinking unavailable — no text recorded Hard-negative fine-tuning is reducing loss against the stronger competitors. This is encouraging as an optimization check, but it is not yet evidence of better retrieval; the public backtest is still pending. ## Step 52 · agent · 2026-09-23T05:39:20.777Z ### Thinking unavailable — no text recorded ## Step 53 · agent · 2026-09-23T05:39:31.618Z ### Thinking unavailable — no text recorded ### sleep · 2026-09-23T05:39:31.870Z { "duration_ms": 35000 } ### Tool output · 2026-09-23T05:40:06.879Z Wall time: 35.0046 seconds Sleep completed. ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "session_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "root_turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "response_id": "resp_021598e9bafc9da7016ab365f1d99087d2b1dcba9ac046a74e", "usage": { "input_tokens": 71645, "cached_input_tokens": 71168, "cache_write_input_tokens": 0, "output_tokens": 887, "reasoning_output_tokens": 820, "total_tokens": 72532 }, "turn_token_usage": { "input_tokens": 3036858, "cached_input_tokens": 2967936, "cache_write_input_tokens": 0, "output_tokens": 19935, "reasoning_output_tokens": 10108, "total_tokens": 3056793 }, "thread_token_usage": { "input_tokens": 3036858, "cached_input_tokens": 2967936, "cache_write_input_tokens": 0, "output_tokens": 19935, "reasoning_output_tokens": 10108, "total_tokens": 3056793 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "Extension", "kind": "clock.sleep", "id": "call_a3izcfHir8qrT1THSFbKXKqD", "durationMs": 35000 } } ### exec · 2026-09-23T05:40:13.492Z { "code": "text(await tools.exec_command({cmd:\"tail -4 /app/solution/hard.log\",max_output_tokens:1000}));\ntext(await tools.clock__curr_time({}));\n" } ### Tool output · 2026-09-23T05:40:13.566Z Script completed Wall time 0.0 seconds Output: {"chunk_id":"10e466","wall_time_seconds":0.00000468,"exit_code":0,"original_token_count":93,"output":"epoch=2/2 batches=100 targets=402,892 batch_bce=0.655339 running_bce=0.680685 elapsed=116.0s\nepoch=2/2 batches=125 targets=505,534 batch_bce=0.666566 running_bce=0.676064 elapsed=145.7s\nepoch=2/2 batches=150 targets=596,745 batch_bce=0.654280 running_bce=0.673343 elapsed=172.3s\nepoch=2/2 batches=175 targets=696,141 batch_bce=0.664338 running_bce=0.671501 elapsed=201.7s\n"} {"current_time":"2026-09-23 05:40:13 UTC"} ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-be4e2a7d-86d1-42b2-99e3-4d7d5bacb3c7", "process_id": "2349", "command": [ "/bin/sh", "-lc", "tail -4 /app/solution/hard.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -4 /app/solution/hard.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "epoch=2/2 batches=100 targets=402,892 batch_bce=0.655339 running_bce=0.680685 elapsed=116.0s\nepoch=2/2 batches=125 targets=505,534 batch_bce=0.666566 running_bce=0.676064 elapsed=145.7s\nepoch=2/2 batches=150 targets=596,745 batch_bce=0.654280 running_bce=0.673343 elapsed=172.3s\nepoch=2/2 batches=175 targets=696,141 batch_bce=0.664338 running_bce=0.671501 elapsed=201.7s\n", "stderr": "", "aggregated_output": "epoch=2/2 batches=100 targets=402,892 batch_bce=0.655339 running_bce=0.680685 elapsed=116.0s\nepoch=2/2 batches=125 targets=505,534 batch_bce=0.666566 running_bce=0.676064 elapsed=145.7s\nepoch=2/2 batches=150 targets=596,745 batch_bce=0.654280 running_bce=0.673343 elapsed=172.3s\nepoch=2/2 batches=175 targets=696,141 batch_bce=0.664338 running_bce=0.671501 elapsed=201.7s\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4680 }, "formatted_output": "epoch=2/2 batches=100 targets=402,892 batch_bce=0.655339 running_bce=0.680685 elapsed=116.0s\nepoch=2/2 batches=125 targets=505,534 batch_bce=0.666566 running_bce=0.676064 elapsed=145.7s\nepoch=2/2 batches=150 targets=596,745 batch_bce=0.654280 running_bce=0.673343 elapsed=172.3s\nepoch=2/2 batches=175 targets=696,141 batch_bce=0.664338 running_bce=0.671501 elapsed=201.7s\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "session_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "root_turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "response_id": "resp_021598e9bafc9da7016ab366372bc887d28760200c2da81282", "usage": { "input_tokens": 72555, "cached_input_tokens": 71424, "cache_write_input_tokens": 0, "output_tokens": 45, "reasoning_output_tokens": 0, "total_tokens": 72600 }, "turn_token_usage": { "input_tokens": 3109413, "cached_input_tokens": 3039360, "cache_write_input_tokens": 0, "output_tokens": 19980, "reasoning_output_tokens": 10108, "total_tokens": 3129393 }, "thread_token_usage": { "input_tokens": 3109413, "cached_input_tokens": 3039360, "cache_write_input_tokens": 0, "output_tokens": 19980, "reasoning_output_tokens": 10108, "total_tokens": 3129393 } } ## Step 54 · agent · 2026-09-23T05:40:17.320Z ### Thinking unavailable — no text recorded The hard-negative run has processed roughly two-thirds of its training targets without numerical or memory issues. Next I’ll evaluate its ranking and run the extra-epoch control. ## Step 55 · agent · 2026-09-23T05:40:38.397Z ### Thinking unavailable — no text recorded ### sleep · 2026-09-23T05:40:38.782Z { "duration_ms": 20000 } ### Tool output · 2026-09-23T05:40:58.794Z Wall time: 20.0081 seconds Sleep completed. ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "session_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "root_turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "response_id": "resp_021598e9bafc9da7016ab3663dccc087d2880e514813827cb3", "usage": { "input_tokens": 72820, "cached_input_tokens": 72320, "cache_write_input_tokens": 0, "output_tokens": 616, "reasoning_output_tokens": 555, "total_tokens": 73436 }, "turn_token_usage": { "input_tokens": 3182233, "cached_input_tokens": 3111680, "cache_write_input_tokens": 0, "output_tokens": 20596, "reasoning_output_tokens": 10663, "total_tokens": 3202829 }, "thread_token_usage": { "input_tokens": 3182233, "cached_input_tokens": 3111680, "cache_write_input_tokens": 0, "output_tokens": 20596, "reasoning_output_tokens": 10663, "total_tokens": 3202829 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "Extension", "kind": "clock.sleep", "id": "call_4CChcLSCzTQzUKWBU6H0YMq3", "durationMs": 20000 } } ### exec · 2026-09-23T05:41:01.753Z { "code": "text(await tools.exec_command({cmd:\"tail -3 /app/solution/hard.log\",max_output_tokens:1000}));\ntext(await tools.clock__curr_time({}));\n" } ### Tool output · 2026-09-23T05:41:01.869Z Script completed Wall time 0.0 seconds Output: {"chunk_id":"8e90c1","wall_time_seconds":0.00000537,"exit_code":0,"original_token_count":70,"output":"epoch=2/2 batches=150 targets=596,745 batch_bce=0.654280 running_bce=0.673343 elapsed=172.3s\nepoch=2/2 batches=175 targets=696,141 batch_bce=0.664338 running_bce=0.671501 elapsed=201.7s\nepoch=2/2 batches=200 targets=790,005 batch_bce=0.658774 running_bce=0.669670 elapsed=229.3s\n"} {"current_time":"2026-09-23 05:41:01 UTC"} ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-cb9530cb-c3e1-4ce7-8263-d398a88e8269", "process_id": "70174", "command": [ "/bin/sh", "-lc", "tail -3 /app/solution/hard.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -3 /app/solution/hard.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "epoch=2/2 batches=150 targets=596,745 batch_bce=0.654280 running_bce=0.673343 elapsed=172.3s\nepoch=2/2 batches=175 targets=696,141 batch_bce=0.664338 running_bce=0.671501 elapsed=201.7s\nepoch=2/2 batches=200 targets=790,005 batch_bce=0.658774 running_bce=0.669670 elapsed=229.3s\n", "stderr": "", "aggregated_output": "epoch=2/2 batches=150 targets=596,745 batch_bce=0.654280 running_bce=0.673343 elapsed=172.3s\nepoch=2/2 batches=175 targets=696,141 batch_bce=0.664338 running_bce=0.671501 elapsed=201.7s\nepoch=2/2 batches=200 targets=790,005 batch_bce=0.658774 running_bce=0.669670 elapsed=229.3s\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 5370 }, "formatted_output": "epoch=2/2 batches=150 targets=596,745 batch_bce=0.654280 running_bce=0.673343 elapsed=172.3s\nepoch=2/2 batches=175 targets=696,141 batch_bce=0.664338 running_bce=0.671501 elapsed=201.7s\nepoch=2/2 batches=200 targets=790,005 batch_bce=0.658774 running_bce=0.669670 elapsed=229.3s\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "session_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "root_turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "response_id": "resp_021598e9bafc9da7016ab3666af5ec87d29ec22ed185f8f483", "usage": { "input_tokens": 73459, "cached_input_tokens": 72704, "cache_write_input_tokens": 0, "output_tokens": 45, "reasoning_output_tokens": 0, "total_tokens": 73504 }, "turn_token_usage": { "input_tokens": 3255692, "cached_input_tokens": 3184384, "cache_write_input_tokens": 0, "output_tokens": 20641, "reasoning_output_tokens": 10663, "total_tokens": 3276333 }, "thread_token_usage": { "input_tokens": 3255692, "cached_input_tokens": 3184384, "cache_write_input_tokens": 0, "output_tokens": 20641, "reasoning_output_tokens": 10663, "total_tokens": 3276333 } } ## Step 56 · agent · 2026-09-23T05:41:23.562Z ### Thinking unavailable — no text recorded One limitation is clearer from the code: the in-batch sampler draws from unique items, but its correction weights come from event frequencies. Those are different distributions, so the correction is a heuristic rather than an exact sampling correction. The hard-negative component instead deliberately emphasizes high-scoring errors; I won’t interpret its weights as impression propensities. ### sleep · 2026-09-23T05:41:25.740Z { "duration_ms": 40000 } ### Tool output · 2026-09-23T05:42:05.748Z Wall time: 40.0038 seconds Sleep completed. ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "session_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "root_turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "response_id": "resp_021598e9bafc9da7016ab3666e1a4c87d28589d394aa8aa07f", "usage": { "input_tokens": 73689, "cached_input_tokens": 73344, "cache_write_input_tokens": 0, "output_tokens": 605, "reasoning_output_tokens": 508, "total_tokens": 74294 }, "turn_token_usage": { "input_tokens": 3329381, "cached_input_tokens": 3257728, "cache_write_input_tokens": 0, "output_tokens": 21246, "reasoning_output_tokens": 11171, "total_tokens": 3350627 }, "thread_token_usage": { "input_tokens": 3329381, "cached_input_tokens": 3257728, "cache_write_input_tokens": 0, "output_tokens": 21246, "reasoning_output_tokens": 11171, "total_tokens": 3350627 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "Extension", "kind": "clock.sleep", "id": "call_aQAKPoswUSxYG2BevTG3Vcwn", "durationMs": 40000 } } ### exec · 2026-09-23T05:42:10.355Z { "code": "text(await tools.exec_command({cmd:\"tail -4 /app/solution/hard.log\",max_output_tokens:1000}));\ntext(await tools.clock__curr_time({}));\n" } ### Tool output · 2026-09-23T05:42:10.437Z Script completed Wall time 0.0 seconds Output: {"chunk_id":"df9734","wall_time_seconds":0.00000454,"exit_code":0,"original_token_count":96,"output":"epoch=2/2 batches=250 targets=987,417 batch_bce=0.656475 running_bce=0.666511 elapsed=287.4s\nsaved epoch checkpoint: /app/solution/hard_ckpt/epoch-02.pt\nepoch=2/2 train_loss=0.66596 validation_loss=0.65580 pair_accuracy=0.776 train_targets=1,052,953 validation_targets=130,103 train_seconds=306.5 validation_seconds=18.4\nrestored best validation-BCE checkpoint: epoch=2 loss=0.655804\n"} {"current_time":"2026-09-23 05:42:10 UTC"} ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-41762866-9372-4da7-8620-f495718837dd", "process_id": "17064", "command": [ "/bin/sh", "-lc", "tail -4 /app/solution/hard.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -4 /app/solution/hard.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "epoch=2/2 batches=250 targets=987,417 batch_bce=0.656475 running_bce=0.666511 elapsed=287.4s\nsaved epoch checkpoint: /app/solution/hard_ckpt/epoch-02.pt\nepoch=2/2 train_loss=0.66596 validation_loss=0.65580 pair_accuracy=0.776 train_targets=1,052,953 validation_targets=130,103 train_seconds=306.5 validation_seconds=18.4\nrestored best validation-BCE checkpoint: epoch=2 loss=0.655804\n", "stderr": "", "aggregated_output": "epoch=2/2 batches=250 targets=987,417 batch_bce=0.656475 running_bce=0.666511 elapsed=287.4s\nsaved epoch checkpoint: /app/solution/hard_ckpt/epoch-02.pt\nepoch=2/2 train_loss=0.66596 validation_loss=0.65580 pair_accuracy=0.776 train_targets=1,052,953 validation_targets=130,103 train_seconds=306.5 validation_seconds=18.4\nrestored best validation-BCE checkpoint: epoch=2 loss=0.655804\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4540 }, "formatted_output": "epoch=2/2 batches=250 targets=987,417 batch_bce=0.656475 running_bce=0.666511 elapsed=287.4s\nsaved epoch checkpoint: /app/solution/hard_ckpt/epoch-02.pt\nepoch=2/2 train_loss=0.66596 validation_loss=0.65580 pair_accuracy=0.776 train_targets=1,052,953 validation_targets=130,103 train_seconds=306.5 validation_seconds=18.4\nrestored best validation-BCE checkpoint: epoch=2 loss=0.655804\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "session_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "root_turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "response_id": "resp_021598e9bafc9da7016ab366adf52087d28bb117afc2610577", "usage": { "input_tokens": 74317, "cached_input_tokens": 73472, "cache_write_input_tokens": 0, "output_tokens": 45, "reasoning_output_tokens": 0, "total_tokens": 74362 }, "turn_token_usage": { "input_tokens": 3403698, "cached_input_tokens": 3331200, "cache_write_input_tokens": 0, "output_tokens": 21291, "reasoning_output_tokens": 11171, "total_tokens": 3424989 }, "thread_token_usage": { "input_tokens": 3403698, "cached_input_tokens": 3331200, "cache_write_input_tokens": 0, "output_tokens": 21291, "reasoning_output_tokens": 11171, "total_tokens": 3424989 } } ## Step 57 · agent · 2026-09-23T05:42:28.312Z ### Thinking unavailable — no text recorded ### sleep · 2026-09-23T05:42:28.677Z { "duration_ms": 20000 } ### Tool output · 2026-09-23T05:42:48.686Z Wall time: 20.0040 seconds Sleep completed. ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "session_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "root_turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "response_id": "resp_021598e9bafc9da7016ab366b2b7b887d28729dc52bbb5224c", "usage": { "input_tokens": 74560, "cached_input_tokens": 74112, "cache_write_input_tokens": 0, "output_tokens": 483, "reasoning_output_tokens": 462, "total_tokens": 75043 }, "turn_token_usage": { "input_tokens": 3478258, "cached_input_tokens": 3405312, "cache_write_input_tokens": 0, "output_tokens": 21774, "reasoning_output_tokens": 11633, "total_tokens": 3500032 }, "thread_token_usage": { "input_tokens": 3478258, "cached_input_tokens": 3405312, "cache_write_input_tokens": 0, "output_tokens": 21774, "reasoning_output_tokens": 11633, "total_tokens": 3500032 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "Extension", "kind": "clock.sleep", "id": "call_d7DwrEPcWSIMpTw4PUJUIvVG", "durationMs": 20000 } } ## Step 58 · agent · 2026-09-23T05:42:52.212Z Hard-negative fine-tuning has finished training and internal validation, and its public ranking is being generated. Sampled-pair accuracy rose from 75.5% to 77.6%, but I’ll use public NDCG@10 to judge the change. ### exec · 2026-09-23T05:42:55.889Z { "code": "text(await tools.exec_command({cmd:\"tail -3 /app/solution/hard.log && ls -l /app/solution/hard_validation.parquet\",max_output_tokens:1000}));\ntext(await tools.write_stdin({session_id:22773,chars:\"\",yield_time_ms:1000,max_output_tokens:1000}));\n" } ### Tool output · 2026-09-23T05:43:00.939Z Script completed Wall time 5.0 seconds Output: {"chunk_id":"0fd340","wall_time_seconds":0.000004531,"exit_code":2,"original_token_count":94,"output":"saved epoch checkpoint: /app/solution/hard_ckpt/epoch-02.pt\nepoch=2/2 train_loss=0.66596 validation_loss=0.65580 pair_accuracy=0.776 train_targets=1,052,953 validation_targets=130,103 train_seconds=306.5 validation_seconds=18.4\nrestored best validation-BCE checkpoint: epoch=2 loss=0.655804\nls: cannot access '/app/solution/hard_validation.parquet': No such file or directory\n"} {"chunk_id":"e3a525","wall_time_seconds":5.001932424,"session_id":22773,"original_token_count":0,"output":""} ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-a5d753c3-d3fb-466f-9d6c-d0efe07bb004", "process_id": "95821", "command": [ "/bin/sh", "-lc", "tail -3 /app/solution/hard.log && ls -l /app/solution/hard_validation.parquet" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -3 /app/solution/hard.log && ls -l /app/solution/hard_validation.parquet" } ], "source": "unified_exec_startup", "status": "failed", "stdout": "saved epoch checkpoint: /app/solution/hard_ckpt/epoch-02.pt\nepoch=2/2 train_loss=0.66596 validation_loss=0.65580 pair_accuracy=0.776 train_targets=1,052,953 validation_targets=130,103 train_seconds=306.5 validation_seconds=18.4\nrestored best validation-BCE checkpoint: epoch=2 loss=0.655804\nls: cannot access '/app/solution/hard_validation.parquet': No such file or directory\n", "stderr": "", "aggregated_output": "saved epoch checkpoint: /app/solution/hard_ckpt/epoch-02.pt\nepoch=2/2 train_loss=0.66596 validation_loss=0.65580 pair_accuracy=0.776 train_targets=1,052,953 validation_targets=130,103 train_seconds=306.5 validation_seconds=18.4\nrestored best validation-BCE checkpoint: epoch=2 loss=0.655804\nls: cannot access '/app/solution/hard_validation.parquet': No such file or directory\n", "exit_code": 2, "duration": { "secs": 0, "nanos": 4531 }, "formatted_output": "saved epoch checkpoint: /app/solution/hard_ckpt/epoch-02.pt\nepoch=2/2 train_loss=0.66596 validation_loss=0.65580 pair_accuracy=0.776 train_targets=1,052,953 validation_targets=130,103 train_seconds=306.5 validation_seconds=18.4\nrestored best validation-BCE checkpoint: epoch=2 loss=0.655804\nls: cannot access '/app/solution/hard_validation.parquet': No such file or directory\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "session_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "root_turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "response_id": "resp_021598e9bafc9da7016ab366d8dadc87d2b8fbc605360f29bb", "usage": { "input_tokens": 75066, "cached_input_tokens": 74368, "cache_write_input_tokens": 0, "output_tokens": 138, "reasoning_output_tokens": 0, "total_tokens": 75204 }, "turn_token_usage": { "input_tokens": 3553324, "cached_input_tokens": 3479680, "cache_write_input_tokens": 0, "output_tokens": 21912, "reasoning_output_tokens": 11633, "total_tokens": 3575236 }, "thread_token_usage": { "input_tokens": 3553324, "cached_input_tokens": 3479680, "cache_write_input_tokens": 0, "output_tokens": 21912, "reasoning_output_tokens": 11633, "total_tokens": 3575236 } } ## Step 59 · agent · 2026-09-23T05:43:13.510Z ### Thinking unavailable — no text recorded ### sleep · 2026-09-23T05:43:13.854Z { "duration_ms": 35000 } ### Tool output · 2026-09-23T05:43:48.863Z Wall time: 35.0051 seconds Sleep completed. ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "session_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "root_turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "response_id": "resp_021598e9bafc9da7016ab366e51b4487d2bc43b0bd24702615", "usage": { "input_tokens": 75403, "cached_input_tokens": 74880, "cache_write_input_tokens": 0, "output_tokens": 58, "reasoning_output_tokens": 37, "total_tokens": 75461 }, "turn_token_usage": { "input_tokens": 3628727, "cached_input_tokens": 3554560, "cache_write_input_tokens": 0, "output_tokens": 21970, "reasoning_output_tokens": 11670, "total_tokens": 3650697 }, "thread_token_usage": { "input_tokens": 3628727, "cached_input_tokens": 3554560, "cache_write_input_tokens": 0, "output_tokens": 21970, "reasoning_output_tokens": 11670, "total_tokens": 3650697 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-dc8b4168-ee1e-4dfb-95e0-7786d4a99ef6", "process_id": "22773", "command": [ "/bin/sh", "-lc", "OPENBLAS_NUM_THREADS=1 OMP_NUM_THREADS=4 PYTHONDONTWRITEBYTECODE=1 /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 --resume-checkpoint /app/solution/baseline_ckpt/best.pt --epochs 2 --threads 4 --loss-microbatch-size 4096 > /app/solution/hard.json 2> /app/solution/hard.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "OPENBLAS_NUM_THREADS=1 OMP_NUM_THREADS=4 PYTHONDONTWRITEBYTECODE=1 /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 --resume-checkpoint /app/solution/baseline_ckpt/best.pt --epochs 2 --threads 4 --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": 431, "nanos": 866061385 }, "formatted_output": "" } } ### Harness context { "type": "codex_item_completed", "item": { "type": "Extension", "kind": "clock.sleep", "id": "call_kXDjbHkbnChxKGsrnOUs3zyg", "durationMs": 35000 } } ### exec · 2026-09-23T05:43:57.315Z { "code": "text(await tools.exec_command({cmd:\"tail -3 /app/solution/hard.log && ls -l /app/solution/hard_validation.parquet\",max_output_tokens:1000}));\ntext(await tools.clock__curr_time({}));\n" } ### Tool output · 2026-09-23T05:43:57.403Z Script completed Wall time 0.0 seconds Output: {"chunk_id":"27f648","wall_time_seconds":0.00000458,"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 1572215 Sep 23 05:43 /app/solution/hard_validation.parquet\n"} {"current_time":"2026-09-23 05:43:57 UTC"} ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-45de1bd6-9e27-4c33-9518-3210654ad9a4", "process_id": "35116", "command": [ "/bin/sh", "-lc", "tail -3 /app/solution/hard.log && ls -l /app/solution/hard_validation.parquet" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -3 /app/solution/hard.log && ls -l /app/solution/hard_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 1572215 Sep 23 05:43 /app/solution/hard_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 1572215 Sep 23 05:43 /app/solution/hard_validation.parquet\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4580 }, "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 1572215 Sep 23 05:43 /app/solution/hard_validation.parquet\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "session_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "root_turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "response_id": "resp_021598e9bafc9da7016ab367150a4887d2a179a560b8600d1f", "usage": { "input_tokens": 75484, "cached_input_tokens": 75264, "cache_write_input_tokens": 0, "output_tokens": 58, "reasoning_output_tokens": 0, "total_tokens": 75542 }, "turn_token_usage": { "input_tokens": 3704211, "cached_input_tokens": 3629824, "cache_write_input_tokens": 0, "output_tokens": 22028, "reasoning_output_tokens": 11670, "total_tokens": 3726239 }, "thread_token_usage": { "input_tokens": 3704211, "cached_input_tokens": 3629824, "cache_write_input_tokens": 0, "output_tokens": 22028, "reasoning_output_tokens": 11670, "total_tokens": 3726239 } } ### exec · 2026-09-23T05:44:06.414Z { "code": "text(await tools.exec_command({cmd:\"PYTHONDONTWRITEBYTECODE=1 /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.exec_command({cmd:\"OPENBLAS_NUM_THREADS=1 OMP_NUM_THREADS=4 PYTHONDONTWRITEBYTECODE=1 /opt/task-venv/bin/python /app/solution/train_production.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/baseline_ckpt/best.pt --epochs 2 --threads 4 > /app/solution/control.json 2> /app/solution/control.log\",yield_time_ms:1000,max_output_tokens:1000}));\n" } ### Tool output · 2026-09-23T05:44:08.742Z Script completed Wall time 2.3 seconds Output: {"chunk_id":"83587f","wall_time_seconds":1.001694845,"session_id":99405,"original_token_count":0,"output":""} {"chunk_id":"6ee90a","wall_time_seconds":1.001482324,"session_id":74673,"original_token_count":0,"output":""} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "session_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "root_turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "response_id": "resp_021598e9bafc9da7016ab3671d9c8087d2bf02d00e271f8e7a", "usage": { "input_tokens": 75682, "cached_input_tokens": 75264, "cache_write_input_tokens": 0, "output_tokens": 223, "reasoning_output_tokens": 0, "total_tokens": 75905 }, "turn_token_usage": { "input_tokens": 3779893, "cached_input_tokens": 3705088, "cache_write_input_tokens": 0, "output_tokens": 22251, "reasoning_output_tokens": 11670, "total_tokens": 3802144 }, "thread_token_usage": { "input_tokens": 3779893, "cached_input_tokens": 3705088, "cache_write_input_tokens": 0, "output_tokens": 22251, "reasoning_output_tokens": 11670, "total_tokens": 3802144 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-bb1f2a14-f815-4a0d-8c79-a5ca50a0b734", "process_id": "99405", "command": [ "/bin/sh", "-lc", "PYTHONDONTWRITEBYTECODE=1 /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": "PYTHONDONTWRITEBYTECODE=1 /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\": 0.002557,\n \"quality_users\": 4263\n }\n}\n", "stderr": "", "aggregated_output": "{\n \"model\": \"/app/solution/hard_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.002557,\n \"quality_users\": 4263\n }\n}\n", "exit_code": 0, "duration": { "secs": 3, "nanos": 274769602 }, "formatted_output": "{\n \"model\": \"/app/solution/hard_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.002557,\n \"quality_users\": 4263\n }\n}\n" } } ## Step 60 · agent · 2026-09-23T05:44:11.927Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T05:44:12.887Z { "code": "text(await tools.write_stdin({session_id:99405,chars:\"\",yield_time_ms:1000,max_output_tokens:1000}));\n" } ### Tool output · 2026-09-23T05:44:13.003Z Script completed Wall time 0.0 seconds Output: {"chunk_id":"b6ef9c","wall_time_seconds":0.00000243,"exit_code":0,"original_token_count":34,"output":"{\n \"model\": \"/app/solution/hard_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.002557,\n \"quality_users\": 4263\n }\n}\n"} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "session_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "root_turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "response_id": "resp_021598e9bafc9da7016ab36728e83c87d28a82639dfa69201c", "usage": { "input_tokens": 75997, "cached_input_tokens": 75520, "cache_write_input_tokens": 0, "output_tokens": 61, "reasoning_output_tokens": 20, "total_tokens": 76058 }, "turn_token_usage": { "input_tokens": 3855890, "cached_input_tokens": 3780608, "cache_write_input_tokens": 0, "output_tokens": 22312, "reasoning_output_tokens": 11690, "total_tokens": 3878202 }, "thread_token_usage": { "input_tokens": 3855890, "cached_input_tokens": 3780608, "cache_write_input_tokens": 0, "output_tokens": 22312, "reasoning_output_tokens": 11690, "total_tokens": 3878202 } } ## Step 61 · agent · 2026-09-23T05:44:19.725Z ### Thinking unavailable — no text recorded The hard-negative BCE experiment failed the public backtest: NDCG@10 fell to **0.002557**. I’m rejecting it and keeping the fallback submission. The result shows that better sampled-pair accuracy did not translate to better top-ten discovery ranking. The extra-epoch control is now running. ## Step 62 · agent · 2026-09-23T05:44:40.945Z ### Thinking unavailable — no text recorded ## Step 63 · agent · 2026-09-23T05:45:00.612Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T05:45:23.161Z { "code": "text(await tools.exec_command({cmd:\"cat > /app/solution/add_pool_objective.py <<'PY'\\nfrom pathlib import Path\\np=Path('/app/solution/train_current_model.py')\\ns=p.read_text()\\na=s.index(' if config.negative_filter_scope == \\\"full-training-history\\\":',s.index('def draw_training_negatives('))\\ns=s[:a]+''' if config.ranking_loss == \\\"pool-softmax\\\":\\n empty = torch.empty((len(positive_items), 0), dtype=torch.long, device=device)\\n return empty, empty.float()\\n'''+s[a:]\\na=s.index(' pool = None', s.index('def score_target_microbatches'))\\ns=s[:a]+''' softmax_pool = None\\n if config.ranking_loss == \\\"pool-softmax\\\":\\n if known_positive_packed is None or user_rows is None:\\n raise ValueError(\\\"pooled softmax requires the complete training-prefix filter\\\")\\n softmax_pool = torch.randperm(len(prepared.item_ids), generator=generator)[\\n :min(config.hard_pool_size, len(prepared.item_ids))].to(device)\\n'''+s[a:]\\ns=s.replace(' if config.hard_negatives:\\\\n if known_positive_packed', ' if config.hard_negatives and softmax_pool is None:\\\\n if known_positive_packed')\\na=s.index(' selected_negatives = negatives[begin:end]',s.index('def score_target_microbatches'))\\nb=s.index(' selected_weights = torch.from_numpy(', a)\\nold=s[a:b]\\nnew=''' if softmax_pool is not None:\\n # A large, uniformly sampled shared pool gives broad catalog coverage.\\n # Log-sum-exp automatically emphasizes confusing candidates without\\n # replacing the negative distribution with deterministic top-k items.\\n encoded = model.encode_items(torch.cat([positive_items[begin:end], softmax_pool]))\\n positive_vectors = encoded[:end-begin]\\n candidate_encodings = encoded[end-begin:]\\n positive_logits = (query_vectors * positive_vectors).sum(dim=1)\\n negative_logits = query_vectors @ candidate_encodings.T\\n pool_np = softmax_pool.cpu().numpy()\\n bits = known_positive_packed[user_rows[begin:end, None], pool_np[None, :] >> 3]\\n invalid = ((bits >> (pool_np[None, :] & 7)) & 1).astype(bool)\\n pool_columns = np.full(len(prepared.item_ids) + 1, -1, dtype=np.int32)\\n pool_columns[pool_np] = np.arange(len(pool_np))\\n excluded = np.column_stack([positive_items[begin:end].cpu().numpy(),\\n prepared.liked_states[selected_states]])\\n excluded_columns = pool_columns[excluded]\\n rr, cc = np.nonzero(excluded_columns >= 0)\\n invalid[rr, excluded_columns[rr, cc]] = True\\n valid = ~torch.from_numpy(invalid).to(device)\\n # Uniform inclusion probability is constant; the partition correction\\n # estimates the full-catalog negative mass. No impression propensities.\\n corrected_logits = negative_logits + np.log(len(prepared.item_ids) / len(softmax_pool))\\n corrected_logits = corrected_logits.masked_fill(~valid, -torch.inf)\\n joint = torch.cat([positive_logits[:, None], corrected_logits], dim=1)\\n positive_loss = 2.0 * (torch.logsumexp(joint, dim=1) - positive_logits)\\n negative_loss = torch.zeros_like(positive_loss)\\n random_count = negative_logits.shape[1]\\n else:\\n'''+''.join(' '+line if line.strip() else line for line in old.splitlines(keepends=True))\\ns=s[:a]+new+s[b:]\\ns=s.replace('choices=(\\\"bce\\\", \\\"softmax\\\")','choices=(\\\"bce\\\", \\\"softmax\\\", \\\"pool-softmax\\\")')\\ns=s.replace(''' negative_count = config.uniform_negatives + config.inbatch_negatives\\n total_loss''',''' negative_count = (min(config.hard_pool_size, len(prepared.item_ids))\\n if config.ranking_loss == \\\"pool-softmax\\\"\\n else config.uniform_negatives + config.inbatch_negatives)\\n total_loss''')\\np.write_text(s)\\nPY\\n/opt/task-venv/bin/python /app/solution/add_pool_objective.py\\nPYTHONDONTWRITEBYTECODE=1 /opt/task-venv/bin/python /app/solution/test_hard_negatives.py\",yield_time_ms:1000,max_output_tokens:1000}));\n" } ### Tool output · 2026-09-23T05:45:24.327Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"d5deaf","wall_time_seconds":1.000899846,"session_id":83530,"original_token_count":0,"output":""} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "session_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "root_turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "response_id": "resp_021598e9bafc9da7016ab3672d5b4087d289b777f93c660242", "usage": { "input_tokens": 76168, "cached_input_tokens": 75776, "cache_write_input_tokens": 0, "output_tokens": 2225, "reasoning_output_tokens": 1125, "total_tokens": 78393 }, "turn_token_usage": { "input_tokens": 3932058, "cached_input_tokens": 3856384, "cache_write_input_tokens": 0, "output_tokens": 24537, "reasoning_output_tokens": 12815, "total_tokens": 3956595 }, "thread_token_usage": { "input_tokens": 3932058, "cached_input_tokens": 3856384, "cache_write_input_tokens": 0, "output_tokens": 24537, "reasoning_output_tokens": 12815, "total_tokens": 3956595 } } ## Step 64 · agent · 2026-09-23T05:45:26.875Z ### Thinking unavailable — no text recorded ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-ab958719-128b-4faf-80ca-aabfc1848a08", "process_id": "83530", "command": [ "/bin/sh", "-lc", "cat > /app/solution/add_pool_objective.py <<'PY'\nfrom pathlib import Path\np=Path('/app/solution/train_current_model.py')\ns=p.read_text()\na=s.index(' if config.negative_filter_scope == \"full-training-history\":',s.index('def draw_training_negatives('))\ns=s[:a]+''' if config.ranking_loss == \"pool-softmax\":\n empty = torch.empty((len(positive_items), 0), dtype=torch.long, device=device)\n return empty, empty.float()\n'''+s[a:]\na=s.index(' pool = None', s.index('def score_target_microbatches'))\ns=s[:a]+''' softmax_pool = None\n if config.ranking_loss == \"pool-softmax\":\n if known_positive_packed is None or user_rows is None:\n raise ValueError(\"pooled softmax requires the complete training-prefix filter\")\n softmax_pool = torch.randperm(len(prepared.item_ids), generator=generator)[\n :min(config.hard_pool_size, len(prepared.item_ids))].to(device)\n'''+s[a:]\ns=s.replace(' if config.hard_negatives:\\n if known_positive_packed', ' if config.hard_negatives and softmax_pool is None:\\n if known_positive_packed')\na=s.index(' selected_negatives = negatives[begin:end]',s.index('def score_target_microbatches'))\nb=s.index(' selected_weights = torch.from_numpy(', a)\nold=s[a:b]\nnew=''' if softmax_pool is not None:\n # A large, uniformly sampled shared pool gives broad catalog coverage.\n # Log-sum-exp automatically emphasizes confusing candidates without\n # replacing the negative distribution with deterministic top-k items.\n encoded = model.encode_items(torch.cat([positive_items[begin:end], softmax_pool]))\n positive_vectors = encoded[:end-begin]\n candidate_encodings = encoded[end-begin:]\n positive_logits = (query_vectors * positive_vectors).sum(dim=1)\n negative_logits = query_vectors @ candidate_encodings.T\n pool_np = softmax_pool.cpu().numpy()\n bits = known_positive_packed[user_rows[begin:end, None], pool_np[None, :] >> 3]\n invalid = ((bits >> (pool_np[None, :] & 7)) & 1).astype(bool)\n pool_columns = np.full(len(prepared.item_ids) + 1, -1, dtype=np.int32)\n pool_columns[pool_np] = np.arange(len(pool_np))\n excluded = np.column_stack([positive_items[begin:end].cpu().numpy(),\n prepared.liked_states[selected_states]])\n excluded_columns = pool_columns[excluded]\n rr, cc = np.nonzero(excluded_columns >= 0)\n invalid[rr, excluded_columns[rr, cc]] = True\n valid = ~torch.from_numpy(invalid).to(device)\n # Uniform inclusion probability is constant; the partition correction\n # estimates the full-catalog negative mass. No impression propensities.\n corrected_logits = negative_logits + np.log(len(prepared.item_ids) / len(softmax_pool))\n corrected_logits = corrected_logits.masked_fill(~valid, -torch.inf)\n joint = torch.cat([positive_logits[:, None], corrected_logits], dim=1)\n positive_loss = 2.0 * (torch.logsumexp(joint, dim=1) - positive_logits)\n negative_loss = torch.zeros_like(positive_loss)\n random_count = negative_logits.shape[1]\n else:\n'''+''.join(' '+line if line.strip() else line for line in old.splitlines(keepends=True))\ns=s[:a]+new+s[b:]\ns=s.replace('choices=(\"bce\", \"softmax\")','choices=(\"bce\", \"softmax\", \"pool-softmax\")')\ns=s.replace(''' negative_count = config.uniform_negatives + config.inbatch_negatives\n total_loss''',''' negative_count = (min(config.hard_pool_size, len(prepared.item_ids))\n if config.ranking_loss == \"pool-softmax\"\n else config.uniform_negatives + config.inbatch_negatives)\n total_loss''')\np.write_text(s)\nPY\n/opt/task-venv/bin/python /app/solution/add_pool_objective.py\nPYTHONDONTWRITEBYTECODE=1 /opt/task-venv/bin/python /app/solution/test_hard_negatives.py" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat > /app/solution/add_pool_objective.py <<'PY'\nfrom pathlib import Path\np=Path('/app/solution/train_current_model.py')\ns=p.read_text()\na=s.index(' if config.negative_filter_scope == \"full-training-history\":',s.index('def draw_training_negatives('))\ns=s[:a]+''' if config.ranking_loss == \"pool-softmax\":\n empty = torch.empty((len(positive_items), 0), dtype=torch.long, device=device)\n return empty, empty.float()\n'''+s[a:]\na=s.index(' pool = None', s.index('def score_target_microbatches'))\ns=s[:a]+''' softmax_pool = None\n if config.ranking_loss == \"pool-softmax\":\n if known_positive_packed is None or user_rows is None:\n raise ValueError(\"pooled softmax requires the complete training-prefix filter\")\n softmax_pool = torch.randperm(len(prepared.item_ids), generator=generator)[\n :min(config.hard_pool_size, len(prepared.item_ids))].to(device)\n'''+s[a:]\ns=s.replace(' if config.hard_negatives:\\n if known_positive_packed', ' if config.hard_negatives and softmax_pool is None:\\n if known_positive_packed')\na=s.index(' selected_negatives = negatives[begin:end]',s.index('def score_target_microbatches'))\nb=s.index(' selected_weights = torch.from_numpy(', a)\nold=s[a:b]\nnew=''' if softmax_pool is not None:\n # A large, uniformly sampled shared pool gives broad catalog coverage.\n # Log-sum-exp automatically emphasizes confusing candidates without\n # replacing the negative distribution with deterministic top-k items.\n encoded = model.encode_items(torch.cat([positive_items[begin:end], softmax_pool]))\n positive_vectors = encoded[:end-begin]\n candidate_encodings = encoded[end-begin:]\n positive_logits = (query_vectors * positive_vectors).sum(dim=1)\n negative_logits = query_vectors @ candidate_encodings.T\n pool_np = softmax_pool.cpu().numpy()\n bits = known_positive_packed[user_rows[begin:end, None], pool_np[None, :] >> 3]\n invalid = ((bits >> (pool_np[None, :] & 7)) & 1).astype(bool)\n pool_columns = np.full(len(prepared.item_ids) + 1, -1, dtype=np.int32)\n pool_columns[pool_np] = np.arange(len(pool_np))\n excluded = np.column_stack([positive_items[begin:end].cpu().numpy(),\n prepared.liked_states[selected_states]])\n excluded_columns = pool_columns[excluded]\n rr, cc = np.nonzero(excluded_columns >= 0)\n invalid[rr, excluded_columns[rr, cc]] = True\n valid = ~torch.from_numpy(invalid).to(device)\n # Uniform inclusion probability is constant; the partition correction\n # estimates the full-catalog negative mass. No impression propensities.\n corrected_logits = negative_logits + np.log(len(prepared.item_ids) / len(softmax_pool))\n corrected_logits = corrected_logits.masked_fill(~valid, -torch.inf)\n joint = torch.cat([positive_logits[:, None], corrected_logits], dim=1)\n positive_loss = 2.0 * (torch.logsumexp(joint, dim=1) - positive_logits)\n negative_loss = torch.zeros_like(positive_loss)\n random_count = negative_logits.shape[1]\n else:\n'''+''.join(' '+line if line.strip() else line for line in old.splitlines(keepends=True))\ns=s[:a]+new+s[b:]\ns=s.replace('choices=(\"bce\", \"softmax\")','choices=(\"bce\", \"softmax\", \"pool-softmax\")')\ns=s.replace(''' negative_count = config.uniform_negatives + config.inbatch_negatives\n total_loss''',''' negative_count = (min(config.hard_pool_size, len(prepared.item_ids))\n if config.ranking_loss == \"pool-softmax\"\n else config.uniform_negatives + config.inbatch_negatives)\n total_loss''')\np.write_text(s)\nPY\n/opt/task-venv/bin/python /app/solution/add_pool_objective.py\nPYTHONDONTWRITEBYTECODE=1 /opt/task-venv/bin/python /app/solution/test_hard_negatives.py" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "PASS: prefix positives, likes, target, duplicates excluded; finite nonzero gradients\n", "stderr": "", "aggregated_output": "PASS: prefix positives, likes, target, duplicates excluded; finite nonzero gradients\n", "exit_code": 0, "duration": { "secs": 4, "nanos": 561366768 }, "formatted_output": "PASS: prefix positives, likes, target, duplicates excluded; finite nonzero gradients\n" } } ### exec · 2026-09-23T05:45:28.597Z { "code": "text(await tools.exec_command({cmd:\"tail -3 /app/solution/control.log && sed -n '1170,1298p' /app/solution/train_current_model.py\",max_output_tokens:4000}));\ntext(await tools.clock__curr_time({}));\n" } ### Tool output · 2026-09-23T05:45:28.704Z Script completed Wall time 0.1 seconds Output: {"chunk_id":"cee99c","wall_time_seconds":0.000007081,"exit_code":0,"original_token_count":1915,"output":"epoch=2/2 batches=25 targets=99,053 batch_bce=0.450958 running_bce=0.473995 elapsed=20.8s\nepoch=2/2 batches=50 targets=203,757 batch_bce=0.483706 running_bce=0.475056 elapsed=42.4s\nepoch=2/2 batches=75 targets=301,090 batch_bce=0.453462 running_bce=0.473540 elapsed=62.7s\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 if softmax_pool is not None:\n # A large, uniformly sampled shared pool gives broad catalog coverage.\n # Log-sum-exp automatically emphasizes confusing candidates without\n # replacing the negative distribution with deterministic top-k items.\n encoded = model.encode_items(torch.cat([positive_items[begin:end], softmax_pool]))\n positive_vectors = encoded[:end-begin]\n candidate_encodings = encoded[end-begin:]\n positive_logits = (query_vectors * positive_vectors).sum(dim=1)\n negative_logits = query_vectors @ candidate_encodings.T\n pool_np = softmax_pool.cpu().numpy()\n bits = known_positive_packed[user_rows[begin:end, None], pool_np[None, :] >> 3]\n invalid = ((bits >> (pool_np[None, :] & 7)) & 1).astype(bool)\n pool_columns = np.full(len(prepared.item_ids) + 1, -1, dtype=np.int32)\n pool_columns[pool_np] = np.arange(len(pool_np))\n excluded = np.column_stack([positive_items[begin:end].cpu().numpy(),\n prepared.liked_states[selected_states]])\n excluded_columns = pool_columns[excluded]\n rr, cc = np.nonzero(excluded_columns >= 0)\n invalid[rr, excluded_columns[rr, cc]] = True\n valid = ~torch.from_numpy(invalid).to(device)\n # Uniform inclusion probability is constant; the partition correction\n # estimates the full-catalog negative mass. No impression propensities.\n corrected_logits = negative_logits + np.log(len(prepared.item_ids) / len(softmax_pool))\n corrected_logits = corrected_logits.masked_fill(~valid, -torch.inf)\n joint = torch.cat([positive_logits[:, None], corrected_logits], dim=1)\n positive_loss = 2.0 * (torch.logsumexp(joint, dim=1) - positive_logits)\n negative_loss = torch.zeros_like(positive_loss)\n random_count = negative_logits.shape[1]\n else:\n selected_negatives = negatives[begin:end]\n if pool is not None:\n with torch.no_grad():\n mining_scores = query_vectors.detach() @ pool_vectors.T\n pool_np = pool.cpu().numpy()\n rows = user_rows[begin:end]\n bits = known_positive_packed[rows[:, None], pool_np[None, :] >> 3]\n invalid = ((bits >> (pool_np[None, :] & 7)) & 1).astype(bool)\n # Explicit state can contain likes without a qualifying listen.\n likes = prepared.liked_states[selected_states]\n pool_columns = np.full(len(prepared.item_ids) + 1, -1, dtype=np.int32)\n pool_columns[pool_np] = np.arange(len(pool_np))\n like_columns = pool_columns[likes]\n rr, cc = np.nonzero(like_columns >= 0)\n invalid[rr, like_columns[rr, cc]] = True\n mining_scores.masked_fill_(torch.from_numpy(invalid).to(device), -torch.inf)\n # Current targets and existing random negatives are never mined.\n excluded = torch.cat([positive_items[begin:end, None], selected_negatives], dim=1)\n excluded_columns = pool_columns[excluded.cpu().numpy()]\n rr, cc = np.nonzero(excluded_columns >= 0)\n mining_scores[torch.from_numpy(rr).to(device),\n torch.from_numpy(excluded_columns[rr, cc]).to(device)] = -torch.inf\n values, columns = mining_scores.topk(config.hard_negatives, dim=1)\n if not torch.isfinite(values).all():\n raise RuntimeError(\"not enough valid hard negatives\")\n selected_negatives = torch.cat([selected_negatives, pool[columns]], dim=1)\n positive_vectors, negative_vectors = candidate_vectors(\n model, positive_items[begin:end], 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 random_count = config.uniform_negatives + config.inbatch_negatives\n negative_loss = corrected_negative_loss(\n negative_logits[:, :random_count], negative_weights[begin:end], config\n )\n if pool is not None:\n hard_loss = F.softplus(negative_logits[:, random_count:]).mean(dim=1)\n negative_loss = ((1.0 - config.hard_loss_weight) * negative_loss\n + config.hard_loss_weight * hard_loss)\n if config.ranking_loss == \"softmax\":\n # Relative ranking loss concentrates gradient on confusing candidates.\n joint = torch.cat([positive_logits[:, None], negative_logits], dim=1)\n positive_loss = 2.0 * (torch.logsumexp(joint, dim=1) - positive_logits)\n negative_loss = torch.zeros_like(positive_loss)\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[:, :random_count].detach().sum().cpu())\n statistics[\"pair_correct\"] += float(\n (positive_logits.detach()[:, None] > negative_logits[:, :random_count].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"} {"current_time":"2026-09-23 05:45:28 UTC"} ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-6f33961a-66d2-4fe6-b2c7-de1374b4a597", "process_id": "8504", "command": [ "/bin/sh", "-lc", "tail -3 /app/solution/control.log && sed -n '1170,1298p' /app/solution/train_current_model.py" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -3 /app/solution/control.log && sed -n '1170,1298p' /app/solution/train_current_model.py" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "epoch=2/2 batches=25 targets=99,053 batch_bce=0.450958 running_bce=0.473995 elapsed=20.8s\nepoch=2/2 batches=50 targets=203,757 batch_bce=0.483706 running_bce=0.475056 elapsed=42.4s\nepoch=2/2 batches=75 targets=301,090 batch_bce=0.453462 running_bce=0.473540 elapsed=62.7s\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 if softmax_pool is not None:\n # A large, uniformly sampled shared pool gives broad catalog coverage.\n # Log-sum-exp automatically emphasizes confusing candidates without\n # replacing the negative distribution with deterministic top-k items.\n encoded = model.encode_items(torch.cat([positive_items[begin:end], softmax_pool]))\n positive_vectors = encoded[:end-begin]\n candidate_encodings = encoded[end-begin:]\n positive_logits = (query_vectors * positive_vectors).sum(dim=1)\n negative_logits = query_vectors @ candidate_encodings.T\n pool_np = softmax_pool.cpu().numpy()\n bits = known_positive_packed[user_rows[begin:end, None], pool_np[None, :] >> 3]\n invalid = ((bits >> (pool_np[None, :] & 7)) & 1).astype(bool)\n pool_columns = np.full(len(prepared.item_ids) + 1, -1, dtype=np.int32)\n pool_columns[pool_np] = np.arange(len(pool_np))\n excluded = np.column_stack([positive_items[begin:end].cpu().numpy(),\n prepared.liked_states[selected_states]])\n excluded_columns = pool_columns[excluded]\n rr, cc = np.nonzero(excluded_columns >= 0)\n invalid[rr, excluded_columns[rr, cc]] = True\n valid = ~torch.from_numpy(invalid).to(device)\n # Uniform inclusion probability is constant; the partition correction\n # estimates the full-catalog negative mass. No impression propensities.\n corrected_logits = negative_logits + np.log(len(prepared.item_ids) / len(softmax_pool))\n corrected_logits = corrected_logits.masked_fill(~valid, -torch.inf)\n joint = torch.cat([positive_logits[:, None], corrected_logits], dim=1)\n positive_loss = 2.0 * (torch.logsumexp(joint, dim=1) - positive_logits)\n negative_loss = torch.zeros_like(positive_loss)\n random_count = negative_logits.shape[1]\n else:\n selected_negatives = negatives[begin:end]\n if pool is not None:\n with torch.no_grad():\n mining_scores = query_vectors.detach() @ pool_vectors.T\n pool_np = pool.cpu().numpy()\n rows = user_rows[begin:end]\n bits = known_positive_packed[rows[:, None], pool_np[None, :] >> 3]\n invalid = ((bits >> (pool_np[None, :] & 7)) & 1).astype(bool)\n # Explicit state can contain likes without a qualifying listen.\n likes = prepared.liked_states[selected_states]\n pool_columns = np.full(len(prepared.item_ids) + 1, -1, dtype=np.int32)\n pool_columns[pool_np] = np.arange(len(pool_np))\n like_columns = pool_columns[likes]\n rr, cc = np.nonzero(like_columns >= 0)\n invalid[rr, like_columns[rr, cc]] = True\n mining_scores.masked_fill_(torch.from_numpy(invalid).to(device), -torch.inf)\n # Current targets and existing random negatives are never mined.\n excluded = torch.cat([positive_items[begin:end, None], selected_negatives], dim=1)\n excluded_columns = pool_columns[excluded.cpu().numpy()]\n rr, cc = np.nonzero(excluded_columns >= 0)\n mining_scores[torch.from_numpy(rr).to(device),\n torch.from_numpy(excluded_columns[rr, cc]).to(device)] = -torch.inf\n values, columns = mining_scores.topk(config.hard_negatives, dim=1)\n if not torch.isfinite(values).all():\n raise RuntimeError(\"not enough valid hard negatives\")\n selected_negatives = torch.cat([selected_negatives, pool[columns]], dim=1)\n positive_vectors, negative_vectors = candidate_vectors(\n model, positive_items[begin:end], 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 random_count = config.uniform_negatives + config.inbatch_negatives\n negative_loss = corrected_negative_loss(\n negative_logits[:, :random_count], negative_weights[begin:end], config\n )\n if pool is not None:\n hard_loss = F.softplus(negative_logits[:, random_count:]).mean(dim=1)\n negative_loss = ((1.0 - config.hard_loss_weight) * negative_loss\n + config.hard_loss_weight * hard_loss)\n if config.ranking_loss == \"softmax\":\n # Relative ranking loss concentrates gradient on confusing candidates.\n joint = torch.cat([positive_logits[:, None], negative_logits], dim=1)\n positive_loss = 2.0 * (torch.logsumexp(joint, dim=1) - positive_logits)\n negative_loss = torch.zeros_like(positive_loss)\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[:, :random_count].detach().sum().cpu())\n statistics[\"pair_correct\"] += float(\n (positive_logits.detach()[:, None] > negative_logits[:, :random_count].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", "stderr": "", "aggregated_output": "epoch=2/2 batches=25 targets=99,053 batch_bce=0.450958 running_bce=0.473995 elapsed=20.8s\nepoch=2/2 batches=50 targets=203,757 batch_bce=0.483706 running_bce=0.475056 elapsed=42.4s\nepoch=2/2 batches=75 targets=301,090 batch_bce=0.453462 running_bce=0.473540 elapsed=62.7s\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 if softmax_pool is not None:\n # A large, uniformly sampled shared pool gives broad catalog coverage.\n # Log-sum-exp automatically emphasizes confusing candidates without\n # replacing the negative distribution with deterministic top-k items.\n encoded = model.encode_items(torch.cat([positive_items[begin:end], softmax_pool]))\n positive_vectors = encoded[:end-begin]\n candidate_encodings = encoded[end-begin:]\n positive_logits = (query_vectors * positive_vectors).sum(dim=1)\n negative_logits = query_vectors @ candidate_encodings.T\n pool_np = softmax_pool.cpu().numpy()\n bits = known_positive_packed[user_rows[begin:end, None], pool_np[None, :] >> 3]\n invalid = ((bits >> (pool_np[None, :] & 7)) & 1).astype(bool)\n pool_columns = np.full(len(prepared.item_ids) + 1, -1, dtype=np.int32)\n pool_columns[pool_np] = np.arange(len(pool_np))\n excluded = np.column_stack([positive_items[begin:end].cpu().numpy(),\n prepared.liked_states[selected_states]])\n excluded_columns = pool_columns[excluded]\n rr, cc = np.nonzero(excluded_columns >= 0)\n invalid[rr, excluded_columns[rr, cc]] = True\n valid = ~torch.from_numpy(invalid).to(device)\n # Uniform inclusion probability is constant; the partition correction\n # estimates the full-catalog negative mass. No impression propensities.\n corrected_logits = negative_logits + np.log(len(prepared.item_ids) / len(softmax_pool))\n corrected_logits = corrected_logits.masked_fill(~valid, -torch.inf)\n joint = torch.cat([positive_logits[:, None], corrected_logits], dim=1)\n positive_loss = 2.0 * (torch.logsumexp(joint, dim=1) - positive_logits)\n negative_loss = torch.zeros_like(positive_loss)\n random_count = negative_logits.shape[1]\n else:\n selected_negatives = negatives[begin:end]\n if pool is not None:\n with torch.no_grad():\n mining_scores = query_vectors.detach() @ pool_vectors.T\n pool_np = pool.cpu().numpy()\n rows = user_rows[begin:end]\n bits = known_positive_packed[rows[:, None], pool_np[None, :] >> 3]\n invalid = ((bits >> (pool_np[None, :] & 7)) & 1).astype(bool)\n # Explicit state can contain likes without a qualifying listen.\n likes = prepared.liked_states[selected_states]\n pool_columns = np.full(len(prepared.item_ids) + 1, -1, dtype=np.int32)\n pool_columns[pool_np] = np.arange(len(pool_np))\n like_columns = pool_columns[likes]\n rr, cc = np.nonzero(like_columns >= 0)\n invalid[rr, like_columns[rr, cc]] = True\n mining_scores.masked_fill_(torch.from_numpy(invalid).to(device), -torch.inf)\n # Current targets and existing random negatives are never mined.\n excluded = torch.cat([positive_items[begin:end, None], selected_negatives], dim=1)\n excluded_columns = pool_columns[excluded.cpu().numpy()]\n rr, cc = np.nonzero(excluded_columns >= 0)\n mining_scores[torch.from_numpy(rr).to(device),\n torch.from_numpy(excluded_columns[rr, cc]).to(device)] = -torch.inf\n values, columns = mining_scores.topk(config.hard_negatives, dim=1)\n if not torch.isfinite(values).all():\n raise RuntimeError(\"not enough valid hard negatives\")\n selected_negatives = torch.cat([selected_negatives, pool[columns]], dim=1)\n positive_vectors, negative_vectors = candidate_vectors(\n model, positive_items[begin:end], 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 random_count = config.uniform_negatives + config.inbatch_negatives\n negative_loss = corrected_negative_loss(\n negative_logits[:, :random_count], negative_weights[begin:end], config\n )\n if pool is not None:\n hard_loss = F.softplus(negative_logits[:, random_count:]).mean(dim=1)\n negative_loss = ((1.0 - config.hard_loss_weight) * negative_loss\n + config.hard_loss_weight * hard_loss)\n if config.ranking_loss == \"softmax\":\n # Relative ranking loss concentrates gradient on confusing candidates.\n joint = torch.cat([positive_logits[:, None], negative_logits], dim=1)\n positive_loss = 2.0 * (torch.logsumexp(joint, dim=1) - positive_logits)\n negative_loss = torch.zeros_like(positive_loss)\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[:, :random_count].detach().sum().cpu())\n statistics[\"pair_correct\"] += float(\n (positive_logits.detach()[:, None] > negative_logits[:, :random_count].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", "exit_code": 0, "duration": { "secs": 0, "nanos": 7081 }, "formatted_output": "epoch=2/2 batches=25 targets=99,053 batch_bce=0.450958 running_bce=0.473995 elapsed=20.8s\nepoch=2/2 batches=50 targets=203,757 batch_bce=0.483706 running_bce=0.475056 elapsed=42.4s\nepoch=2/2 batches=75 targets=301,090 batch_bce=0.453462 running_bce=0.473540 elapsed=62.7s\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 if softmax_pool is not None:\n # A large, uniformly sampled shared pool gives broad catalog coverage.\n # Log-sum-exp automatically emphasizes confusing candidates without\n # replacing the negative distribution with deterministic top-k items.\n encoded = model.encode_items(torch.cat([positive_items[begin:end], softmax_pool]))\n positive_vectors = encoded[:end-begin]\n candidate_encodings = encoded[end-begin:]\n positive_logits = (query_vectors * positive_vectors).sum(dim=1)\n negative_logits = query_vectors @ candidate_encodings.T\n pool_np = softmax_pool.cpu().numpy()\n bits = known_positive_packed[user_rows[begin:end, None], pool_np[None, :] >> 3]\n invalid = ((bits >> (pool_np[None, :] & 7)) & 1).astype(bool)\n pool_columns = np.full(len(prepared.item_ids) + 1, -1, dtype=np.int32)\n pool_columns[pool_np] = np.arange(len(pool_np))\n excluded = np.column_stack([positive_items[begin:end].cpu().numpy(),\n prepared.liked_states[selected_states]])\n excluded_columns = pool_columns[excluded]\n rr, cc = np.nonzero(excluded_columns >= 0)\n invalid[rr, excluded_columns[rr, cc]] = True\n valid = ~torch.from_numpy(invalid).to(device)\n # Uniform inclusion probability is constant; the partition correction\n # estimates the full-catalog negative mass. No impression propensities.\n corrected_logits = negative_logits + np.log(len(prepared.item_ids) / len(softmax_pool))\n corrected_logits = corrected_logits.masked_fill(~valid, -torch.inf)\n joint = torch.cat([positive_logits[:, None], corrected_logits], dim=1)\n positive_loss = 2.0 * (torch.logsumexp(joint, dim=1) - positive_logits)\n negative_loss = torch.zeros_like(positive_loss)\n random_count = negative_logits.shape[1]\n else:\n selected_negatives = negatives[begin:end]\n if pool is not None:\n with torch.no_grad():\n mining_scores = query_vectors.detach() @ pool_vectors.T\n pool_np = pool.cpu().numpy()\n rows = user_rows[begin:end]\n bits = known_positive_packed[rows[:, None], pool_np[None, :] >> 3]\n invalid = ((bits >> (pool_np[None, :] & 7)) & 1).astype(bool)\n # Explicit state can contain likes without a qualifying listen.\n likes = prepared.liked_states[selected_states]\n pool_columns = np.full(len(prepared.item_ids) + 1, -1, dtype=np.int32)\n pool_columns[pool_np] = np.arange(len(pool_np))\n like_columns = pool_columns[likes]\n rr, cc = np.nonzero(like_columns >= 0)\n invalid[rr, like_columns[rr, cc]] = True\n mining_scores.masked_fill_(torch.from_numpy(invalid).to(device), -torch.inf)\n # Current targets and existing random negatives are never mined.\n excluded = torch.cat([positive_items[begin:end, None], selected_negatives], dim=1)\n excluded_columns = pool_columns[excluded.cpu().numpy()]\n rr, cc = np.nonzero(excluded_columns >= 0)\n mining_scores[torch.from_numpy(rr).to(device),\n torch.from_numpy(excluded_columns[rr, cc]).to(device)] = -torch.inf\n values, columns = mining_scores.topk(config.hard_negatives, dim=1)\n if not torch.isfinite(values).all():\n raise RuntimeError(\"not enough valid hard negatives\")\n selected_negatives = torch.cat([selected_negatives, pool[columns]], dim=1)\n positive_vectors, negative_vectors = candidate_vectors(\n model, positive_items[begin:end], 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 random_count = config.uniform_negatives + config.inbatch_negatives\n negative_loss = corrected_negative_loss(\n negative_logits[:, :random_count], negative_weights[begin:end], config\n )\n if pool is not None:\n hard_loss = F.softplus(negative_logits[:, random_count:]).mean(dim=1)\n negative_loss = ((1.0 - config.hard_loss_weight) * negative_loss\n + config.hard_loss_weight * hard_loss)\n if config.ranking_loss == \"softmax\":\n # Relative ranking loss concentrates gradient on confusing candidates.\n joint = torch.cat([positive_logits[:, None], negative_logits], dim=1)\n positive_loss = 2.0 * (torch.logsumexp(joint, dim=1) - positive_logits)\n negative_loss = torch.zeros_like(positive_loss)\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[:, :random_count].detach().sum().cpu())\n statistics[\"pair_correct\"] += float(\n (positive_logits.detach()[:, None] > negative_logits[:, :random_count].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" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "session_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "root_turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "response_id": "resp_021598e9bafc9da7016ab3677491d887d2a6d943bd8a307c55", "usage": { "input_tokens": 78451, "cached_input_tokens": 76032, "cache_write_input_tokens": 0, "output_tokens": 89, "reasoning_output_tokens": 23, "total_tokens": 78540 }, "turn_token_usage": { "input_tokens": 4010509, "cached_input_tokens": 3932416, "cache_write_input_tokens": 0, "output_tokens": 24626, "reasoning_output_tokens": 12838, "total_tokens": 4035135 }, "thread_token_usage": { "input_tokens": 4010509, "cached_input_tokens": 3932416, "cache_write_input_tokens": 0, "output_tokens": 24626, "reasoning_output_tokens": 12838, "total_tokens": 4035135 } } ## Step 65 · agent · 2026-09-23T05:45:51.995Z ### Thinking unavailable — no text recorded I’m testing a different objective: sampled softmax over a shared pool of 4,096 uniformly sampled tracks. It compares each positive against far more negatives, with higher-scoring negatives naturally receiving more gradient weight. This retains broad catalog coverage instead of giving a fixed top-eight set half the negative loss. ### exec · 2026-09-23T05:46:06.282Z { "code": "text(await tools.exec_command({cmd:\"cat > /app/solution/test_pool_objective.py <<'PY'\\n\\\"\\\"\\\"Check the filtered shared-pool loss against an exact full-catalog result.\\\"\\\"\\\"\\nfrom types import SimpleNamespace\\nimport numpy as np\\nimport torch\\nimport train_current_model as t\\n\\ntorch.set_num_threads(1)\\nconfig = t.Config(embedding_dim=8, hidden_dim=8, user_hidden_1=16,\\n user_hidden_2=12, item_hidden_1=16, item_hidden_2=12,\\n hard_negatives=0, hard_pool_size=64, ranking_loss='pool-softmax')\\ndata = SimpleNamespace(item_ids=np.arange(64),\\n liked_states=np.array([[6, 64], [7, 64]], dtype=np.uint16),\\n disliked_states=np.array([[64], [64]], dtype=np.uint16),\\n state_counts=np.array([[1, 0], [1, 0]]))\\nmodel = t.base.SequentialTwoTower(np.zeros((64, 4), np.float32), t.model_config(config))\\nwith torch.no_grad():\\n for p in model.parameters(): p.zero_()\\nhistory = np.zeros((2, 64), dtype=np.uint8)\\nhistory[0, [0, 1, 8, 9]] = 1\\nhistory[1, [2, 3, 10, 11]] = 1\\npacked = np.packbits(history, axis=1, bitorder='little')\\nstats, _ = t.score_target_microbatches(model, data, config,\\n torch.zeros(2, 8), np.array([0, 1]), np.zeros((2, 4), np.float32),\\n torch.tensor([4, 5]), torch.empty(2, 0, dtype=torch.long), torch.empty(2, 0),\\n np.ones(2, np.float32), torch.device('cpu'), False, np.array([0, 1]), packed,\\n torch.Generator().manual_seed(9))\\n# 64 items minus 4 history positives, one active like, and the current target:\\n# 58 allowed negatives plus one separately scored positive.\\nassert abs(stats['loss_sum'] / 2 - np.log(59)) < 1e-6, stats\\nprint('PASS: shared-pool loss equals the exact filtered full-catalog softmax')\\nPY\\nPYTHONDONTWRITEBYTECODE=1 /opt/task-venv/bin/python /app/solution/test_pool_objective.py && tail -3 /app/solution/control.log\",yield_time_ms:1000,max_output_tokens:1000}));\n" } ### Tool output · 2026-09-23T05:46:07.465Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"d0e52f","wall_time_seconds":1.001380328,"session_id":85756,"original_token_count":0,"output":""} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "session_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "root_turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "response_id": "resp_021598e9bafc9da7016ab367791e0887d2832457fb39de7d5d", "usage": { "input_tokens": 80394, "cached_input_tokens": 78336, "cache_write_input_tokens": 0, "output_tokens": 1181, "reasoning_output_tokens": 516, "total_tokens": 81575 }, "turn_token_usage": { "input_tokens": 4090903, "cached_input_tokens": 4010752, "cache_write_input_tokens": 0, "output_tokens": 25807, "reasoning_output_tokens": 13354, "total_tokens": 4116710 }, "thread_token_usage": { "input_tokens": 4090903, "cached_input_tokens": 4010752, "cache_write_input_tokens": 0, "output_tokens": 25807, "reasoning_output_tokens": 13354, "total_tokens": 4116710 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-ff0506fa-5cd1-4762-a1a3-2f1f51485723", "process_id": "85756", "command": [ "/bin/sh", "-lc", "cat > /app/solution/test_pool_objective.py <<'PY'\n\"\"\"Check the filtered shared-pool loss against an exact full-catalog result.\"\"\"\nfrom types import SimpleNamespace\nimport numpy as np\nimport torch\nimport train_current_model as t\n\ntorch.set_num_threads(1)\nconfig = t.Config(embedding_dim=8, hidden_dim=8, user_hidden_1=16,\n user_hidden_2=12, item_hidden_1=16, item_hidden_2=12,\n hard_negatives=0, hard_pool_size=64, ranking_loss='pool-softmax')\ndata = SimpleNamespace(item_ids=np.arange(64),\n liked_states=np.array([[6, 64], [7, 64]], dtype=np.uint16),\n disliked_states=np.array([[64], [64]], dtype=np.uint16),\n state_counts=np.array([[1, 0], [1, 0]]))\nmodel = t.base.SequentialTwoTower(np.zeros((64, 4), np.float32), t.model_config(config))\nwith torch.no_grad():\n for p in model.parameters(): p.zero_()\nhistory = np.zeros((2, 64), dtype=np.uint8)\nhistory[0, [0, 1, 8, 9]] = 1\nhistory[1, [2, 3, 10, 11]] = 1\npacked = np.packbits(history, axis=1, bitorder='little')\nstats, _ = t.score_target_microbatches(model, data, config,\n torch.zeros(2, 8), np.array([0, 1]), np.zeros((2, 4), np.float32),\n torch.tensor([4, 5]), torch.empty(2, 0, dtype=torch.long), torch.empty(2, 0),\n np.ones(2, np.float32), torch.device('cpu'), False, np.array([0, 1]), packed,\n torch.Generator().manual_seed(9))\n# 64 items minus 4 history positives, one active like, and the current target:\n# 58 allowed negatives plus one separately scored positive.\nassert abs(stats['loss_sum'] / 2 - np.log(59)) < 1e-6, stats\nprint('PASS: shared-pool loss equals the exact filtered full-catalog softmax')\nPY\nPYTHONDONTWRITEBYTECODE=1 /opt/task-venv/bin/python /app/solution/test_pool_objective.py && tail -3 /app/solution/control.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat > /app/solution/test_pool_objective.py <<'PY'\n\"\"\"Check the filtered shared-pool loss against an exact full-catalog result.\"\"\"\nfrom types import SimpleNamespace\nimport numpy as np\nimport torch\nimport train_current_model as t\n\ntorch.set_num_threads(1)\nconfig = t.Config(embedding_dim=8, hidden_dim=8, user_hidden_1=16,\n user_hidden_2=12, item_hidden_1=16, item_hidden_2=12,\n hard_negatives=0, hard_pool_size=64, ranking_loss='pool-softmax')\ndata = SimpleNamespace(item_ids=np.arange(64),\n liked_states=np.array([[6, 64], [7, 64]], dtype=np.uint16),\n disliked_states=np.array([[64], [64]], dtype=np.uint16),\n state_counts=np.array([[1, 0], [1, 0]]))\nmodel = t.base.SequentialTwoTower(np.zeros((64, 4), np.float32), t.model_config(config))\nwith torch.no_grad():\n for p in model.parameters(): p.zero_()\nhistory = np.zeros((2, 64), dtype=np.uint8)\nhistory[0, [0, 1, 8, 9]] = 1\nhistory[1, [2, 3, 10, 11]] = 1\npacked = np.packbits(history, axis=1, bitorder='little')\nstats, _ = t.score_target_microbatches(model, data, config,\n torch.zeros(2, 8), np.array([0, 1]), np.zeros((2, 4), np.float32),\n torch.tensor([4, 5]), torch.empty(2, 0, dtype=torch.long), torch.empty(2, 0),\n np.ones(2, np.float32), torch.device('cpu'), False, np.array([0, 1]), packed,\n torch.Generator().manual_seed(9))\n# 64 items minus 4 history positives, one active like, and the current target:\n# 58 allowed negatives plus one separately scored positive.\nassert abs(stats['loss_sum'] / 2 - np.log(59)) < 1e-6, stats\nprint('PASS: shared-pool loss equals the exact filtered full-catalog softmax')\nPY\nPYTHONDONTWRITEBYTECODE=1 /opt/task-venv/bin/python /app/solution/test_pool_objective.py && tail -3 /app/solution/control.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "PASS: shared-pool loss equals the exact filtered full-catalog softmax\nepoch=2/2 batches=75 targets=301,090 batch_bce=0.453462 running_bce=0.473540 elapsed=62.7s\nepoch=2/2 batches=100 targets=401,186 batch_bce=0.482109 running_bce=0.471399 elapsed=84.1s\nepoch=2/2 batches=125 targets=504,807 batch_bce=0.449276 running_bce=0.469167 elapsed=105.7s\n", "stderr": "", "aggregated_output": "PASS: shared-pool loss equals the exact filtered full-catalog softmax\nepoch=2/2 batches=75 targets=301,090 batch_bce=0.453462 running_bce=0.473540 elapsed=62.7s\nepoch=2/2 batches=100 targets=401,186 batch_bce=0.482109 running_bce=0.471399 elapsed=84.1s\nepoch=2/2 batches=125 targets=504,807 batch_bce=0.449276 running_bce=0.469167 elapsed=105.7s\n", "exit_code": 0, "duration": { "secs": 4, "nanos": 370979459 }, "formatted_output": "PASS: shared-pool loss equals the exact filtered full-catalog softmax\nepoch=2/2 batches=75 targets=301,090 batch_bce=0.453462 running_bce=0.473540 elapsed=62.7s\nepoch=2/2 batches=100 targets=401,186 batch_bce=0.482109 running_bce=0.471399 elapsed=84.1s\nepoch=2/2 batches=125 targets=504,807 batch_bce=0.449276 running_bce=0.469167 elapsed=105.7s\n" } } ## Step 66 · agent · 2026-09-23T05:46:22.911Z ### Thinking unavailable — no text recorded ### sleep · 2026-09-23T05:46:23.024Z { "duration_ms": 25000 } ### Tool output · 2026-09-23T05:46:48.050Z Wall time: 25.0100 seconds Sleep completed. ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "session_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "root_turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "response_id": "resp_021598e9bafc9da7016ab3679fae5887d28872337020fa20d0", "usage": { "input_tokens": 81634, "cached_input_tokens": 80256, "cache_write_input_tokens": 0, "output_tokens": 455, "reasoning_output_tokens": 434, "total_tokens": 82089 }, "turn_token_usage": { "input_tokens": 4172537, "cached_input_tokens": 4091008, "cache_write_input_tokens": 0, "output_tokens": 26262, "reasoning_output_tokens": 13788, "total_tokens": 4198799 }, "thread_token_usage": { "input_tokens": 4172537, "cached_input_tokens": 4091008, "cache_write_input_tokens": 0, "output_tokens": 26262, "reasoning_output_tokens": 13788, "total_tokens": 4198799 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "Extension", "kind": "clock.sleep", "id": "call_77icIilo5ddfrxcgw9khNl77", "durationMs": 25000 } } ### exec · 2026-09-23T05:46:51.927Z { "code": "text(await tools.write_stdin({session_id:85756,chars:\"\",yield_time_ms:1000,max_output_tokens:1000}));\ntext(await tools.exec_command({cmd:\"tail -4 /app/solution/control.log\",max_output_tokens:1000}));\ntext(await tools.clock__curr_time({}));\n" } ### Tool output · 2026-09-23T05:46:52.025Z Script completed Wall time 0.1 seconds Output: {"chunk_id":"c1beab","wall_time_seconds":0.00000259,"exit_code":0,"original_token_count":87,"output":"PASS: shared-pool loss equals the exact filtered full-catalog softmax\nepoch=2/2 batches=75 targets=301,090 batch_bce=0.453462 running_bce=0.473540 elapsed=62.7s\nepoch=2/2 batches=100 targets=401,186 batch_bce=0.482109 running_bce=0.471399 elapsed=84.1s\nepoch=2/2 batches=125 targets=504,807 batch_bce=0.449276 running_bce=0.469167 elapsed=105.7s\n"} {"chunk_id":"cb27dc","wall_time_seconds":0.00000504,"exit_code":0,"original_token_count":93,"output":"epoch=2/2 batches=100 targets=401,186 batch_bce=0.482109 running_bce=0.471399 elapsed=84.1s\nepoch=2/2 batches=125 targets=504,807 batch_bce=0.449276 running_bce=0.469167 elapsed=105.7s\nepoch=2/2 batches=150 targets=603,258 batch_bce=0.463929 running_bce=0.466968 elapsed=126.6s\nepoch=2/2 batches=175 targets=703,484 batch_bce=0.454082 running_bce=0.464860 elapsed=147.6s\n"} {"current_time":"2026-09-23 05:46:51 UTC"} ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-fd5b648c-82b4-4995-aaf2-a11084194663", "process_id": "82162", "command": [ "/bin/sh", "-lc", "tail -4 /app/solution/control.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -4 /app/solution/control.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "epoch=2/2 batches=100 targets=401,186 batch_bce=0.482109 running_bce=0.471399 elapsed=84.1s\nepoch=2/2 batches=125 targets=504,807 batch_bce=0.449276 running_bce=0.469167 elapsed=105.7s\nepoch=2/2 batches=150 targets=603,258 batch_bce=0.463929 running_bce=0.466968 elapsed=126.6s\nepoch=2/2 batches=175 targets=703,484 batch_bce=0.454082 running_bce=0.464860 elapsed=147.6s\n", "stderr": "", "aggregated_output": "epoch=2/2 batches=100 targets=401,186 batch_bce=0.482109 running_bce=0.471399 elapsed=84.1s\nepoch=2/2 batches=125 targets=504,807 batch_bce=0.449276 running_bce=0.469167 elapsed=105.7s\nepoch=2/2 batches=150 targets=603,258 batch_bce=0.463929 running_bce=0.466968 elapsed=126.6s\nepoch=2/2 batches=175 targets=703,484 batch_bce=0.454082 running_bce=0.464860 elapsed=147.6s\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 5040 }, "formatted_output": "epoch=2/2 batches=100 targets=401,186 batch_bce=0.482109 running_bce=0.471399 elapsed=84.1s\nepoch=2/2 batches=125 targets=504,807 batch_bce=0.449276 running_bce=0.469167 elapsed=105.7s\nepoch=2/2 batches=150 targets=603,258 batch_bce=0.463929 running_bce=0.466968 elapsed=126.6s\nepoch=2/2 batches=175 targets=703,484 batch_bce=0.454082 running_bce=0.464860 elapsed=147.6s\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "session_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "root_turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "response_id": "resp_021598e9bafc9da7016ab367c83a3487d29322c41945a06708", "usage": { "input_tokens": 82112, "cached_input_tokens": 81408, "cache_write_input_tokens": 0, "output_tokens": 73, "reasoning_output_tokens": 0, "total_tokens": 82185 }, "turn_token_usage": { "input_tokens": 4254649, "cached_input_tokens": 4172416, "cache_write_input_tokens": 0, "output_tokens": 26335, "reasoning_output_tokens": 13788, "total_tokens": 4280984 }, "thread_token_usage": { "input_tokens": 4254649, "cached_input_tokens": 4172416, "cache_write_input_tokens": 0, "output_tokens": 26335, "reasoning_output_tokens": 13788, "total_tokens": 4280984 } } ## Step 67 · agent · 2026-09-23T05:46:54.803Z ### Thinking unavailable — no text recorded The shared-pool implementation passed an exact-loss test: with the entire toy catalog sampled, its loss matches full softmax after filtering known positives, active likes, and the current target from the negative set. The extra-epoch baseline control is still training. ## Step 68 · agent · 2026-09-23T05:46:57.988Z ### Thinking unavailable — no text recorded ### sleep · 2026-09-23T05:46:58.239Z { "duration_ms": 40000 } ### Tool output · 2026-09-23T05:47:38.255Z Wall time: 40.0033 seconds Sleep completed. ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "session_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "root_turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "response_id": "resp_021598e9bafc9da7016ab367cc505887d29476e2144e688ced", "usage": { "input_tokens": 82565, "cached_input_tokens": 81920, "cache_write_input_tokens": 0, "output_tokens": 149, "reasoning_output_tokens": 70, "total_tokens": 82714 }, "turn_token_usage": { "input_tokens": 4337214, "cached_input_tokens": 4254336, "cache_write_input_tokens": 0, "output_tokens": 26484, "reasoning_output_tokens": 13858, "total_tokens": 4363698 }, "thread_token_usage": { "input_tokens": 4337214, "cached_input_tokens": 4254336, "cache_write_input_tokens": 0, "output_tokens": 26484, "reasoning_output_tokens": 13858, "total_tokens": 4363698 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "Extension", "kind": "clock.sleep", "id": "call_b7EQcE4TqiwPjcHshrfUXkTl", "durationMs": 40000 } } ### exec · 2026-09-23T05:47:45.727Z { "code": "text(await tools.exec_command({cmd:\"tail -3 /app/solution/control.log\",max_output_tokens:1000}));\ntext(await tools.clock__curr_time({}));\n" } ### Tool output · 2026-09-23T05:47:45.861Z Script completed Wall time 0.0 seconds Output: {"chunk_id":"06db99","wall_time_seconds":0.000004371,"exit_code":0,"original_token_count":70,"output":"epoch=2/2 batches=200 targets=787,814 batch_bce=0.451091 running_bce=0.463041 elapsed=166.2s\nepoch=2/2 batches=225 targets=890,204 batch_bce=0.427556 running_bce=0.460833 elapsed=187.3s\nepoch=2/2 batches=250 targets=988,643 batch_bce=0.408307 running_bce=0.458967 elapsed=207.7s\n"} {"current_time":"2026-09-23 05:47:45 UTC"} ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-9ea8b637-6eb1-4356-8c65-e40928b6c5ab", "process_id": "48994", "command": [ "/bin/sh", "-lc", "tail -3 /app/solution/control.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -3 /app/solution/control.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "epoch=2/2 batches=200 targets=787,814 batch_bce=0.451091 running_bce=0.463041 elapsed=166.2s\nepoch=2/2 batches=225 targets=890,204 batch_bce=0.427556 running_bce=0.460833 elapsed=187.3s\nepoch=2/2 batches=250 targets=988,643 batch_bce=0.408307 running_bce=0.458967 elapsed=207.7s\n", "stderr": "", "aggregated_output": "epoch=2/2 batches=200 targets=787,814 batch_bce=0.451091 running_bce=0.463041 elapsed=166.2s\nepoch=2/2 batches=225 targets=890,204 batch_bce=0.427556 running_bce=0.460833 elapsed=187.3s\nepoch=2/2 batches=250 targets=988,643 batch_bce=0.408307 running_bce=0.458967 elapsed=207.7s\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4371 }, "formatted_output": "epoch=2/2 batches=200 targets=787,814 batch_bce=0.451091 running_bce=0.463041 elapsed=166.2s\nepoch=2/2 batches=225 targets=890,204 batch_bce=0.427556 running_bce=0.460833 elapsed=187.3s\nepoch=2/2 batches=250 targets=988,643 batch_bce=0.408307 running_bce=0.458967 elapsed=207.7s\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "session_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "root_turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "response_id": "resp_021598e9bafc9da7016ab367fa6c7087d2a91b45587aa17cc9", "usage": { "input_tokens": 82737, "cached_input_tokens": 82432, "cache_write_input_tokens": 0, "output_tokens": 44, "reasoning_output_tokens": 0, "total_tokens": 82781 }, "turn_token_usage": { "input_tokens": 4419951, "cached_input_tokens": 4336768, "cache_write_input_tokens": 0, "output_tokens": 26528, "reasoning_output_tokens": 13858, "total_tokens": 4446479 }, "thread_token_usage": { "input_tokens": 4419951, "cached_input_tokens": 4336768, "cache_write_input_tokens": 0, "output_tokens": 26528, "reasoning_output_tokens": 13858, "total_tokens": 4446479 } } ## Step 69 · agent · 2026-09-23T05:48:09.958Z ### Thinking unavailable — no text recorded The extra-epoch control is nearing completion. I’ll use its public score to assess how much improvement comes from more training, then test whether the larger shared negative pool adds further value. ### sleep · 2026-09-23T05:48:11.095Z { "duration_ms": 45000 } ### Tool output · 2026-09-23T05:48:56.103Z Wall time: 45.0031 seconds Sleep completed. ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "session_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "root_turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "response_id": "resp_021598e9bafc9da7016ab36802195c87d281f3273a2132bfe0", "usage": { "input_tokens": 82964, "cached_input_tokens": 82560, "cache_write_input_tokens": 0, "output_tokens": 581, "reasoning_output_tokens": 516, "total_tokens": 83545 }, "turn_token_usage": { "input_tokens": 4502915, "cached_input_tokens": 4419328, "cache_write_input_tokens": 0, "output_tokens": 27109, "reasoning_output_tokens": 14374, "total_tokens": 4530024 }, "thread_token_usage": { "input_tokens": 4502915, "cached_input_tokens": 4419328, "cache_write_input_tokens": 0, "output_tokens": 27109, "reasoning_output_tokens": 14374, "total_tokens": 4530024 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "Extension", "kind": "clock.sleep", "id": "call_F3kc2XR0DeN0IzLMzTut80KC", "durationMs": 45000 } } ### exec · 2026-09-23T05:49:00.290Z { "code": "text(await tools.exec_command({cmd:\"tail -5 /app/solution/control.log\",max_output_tokens:1000}));\ntext(await tools.clock__curr_time({}));\n" } ### Tool output · 2026-09-23T05:49:00.387Z Script completed Wall time 0.0 seconds Output: {"chunk_id":"1e9f0c","wall_time_seconds":0.00000492,"exit_code":0,"original_token_count":120,"output":"epoch=2/2 batches=225 targets=890,204 batch_bce=0.427556 running_bce=0.460833 elapsed=187.3s\nepoch=2/2 batches=250 targets=988,643 batch_bce=0.408307 running_bce=0.458967 elapsed=207.7s\nsaved epoch checkpoint: /app/solution/control_ckpt/epoch-02.pt\nepoch=2/2 train_loss=0.45698 validation_loss=0.43530 pair_accuracy=0.826 train_targets=1,052,953 validation_targets=130,103 train_seconds=221.1 validation_seconds=16.8\nrestored best validation-BCE checkpoint: epoch=2 loss=0.435299\n"} {"current_time":"2026-09-23 05:49:00 UTC"} ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-45502362-fb52-439e-8c10-1ecca90f9e31", "process_id": "43784", "command": [ "/bin/sh", "-lc", "tail -5 /app/solution/control.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -5 /app/solution/control.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "epoch=2/2 batches=225 targets=890,204 batch_bce=0.427556 running_bce=0.460833 elapsed=187.3s\nepoch=2/2 batches=250 targets=988,643 batch_bce=0.408307 running_bce=0.458967 elapsed=207.7s\nsaved epoch checkpoint: /app/solution/control_ckpt/epoch-02.pt\nepoch=2/2 train_loss=0.45698 validation_loss=0.43530 pair_accuracy=0.826 train_targets=1,052,953 validation_targets=130,103 train_seconds=221.1 validation_seconds=16.8\nrestored best validation-BCE checkpoint: epoch=2 loss=0.435299\n", "stderr": "", "aggregated_output": "epoch=2/2 batches=225 targets=890,204 batch_bce=0.427556 running_bce=0.460833 elapsed=187.3s\nepoch=2/2 batches=250 targets=988,643 batch_bce=0.408307 running_bce=0.458967 elapsed=207.7s\nsaved epoch checkpoint: /app/solution/control_ckpt/epoch-02.pt\nepoch=2/2 train_loss=0.45698 validation_loss=0.43530 pair_accuracy=0.826 train_targets=1,052,953 validation_targets=130,103 train_seconds=221.1 validation_seconds=16.8\nrestored best validation-BCE checkpoint: epoch=2 loss=0.435299\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4920 }, "formatted_output": "epoch=2/2 batches=225 targets=890,204 batch_bce=0.427556 running_bce=0.460833 elapsed=187.3s\nepoch=2/2 batches=250 targets=988,643 batch_bce=0.408307 running_bce=0.458967 elapsed=207.7s\nsaved epoch checkpoint: /app/solution/control_ckpt/epoch-02.pt\nepoch=2/2 train_loss=0.45698 validation_loss=0.43530 pair_accuracy=0.826 train_targets=1,052,953 validation_targets=130,103 train_seconds=221.1 validation_seconds=16.8\nrestored best validation-BCE checkpoint: epoch=2 loss=0.435299\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "session_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "root_turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "response_id": "resp_021598e9bafc9da7016ab3684847e487d2850c147ecd553918", "usage": { "input_tokens": 83568, "cached_input_tokens": 82816, "cache_write_input_tokens": 0, "output_tokens": 44, "reasoning_output_tokens": 0, "total_tokens": 83612 }, "turn_token_usage": { "input_tokens": 4586483, "cached_input_tokens": 4502144, "cache_write_input_tokens": 0, "output_tokens": 27153, "reasoning_output_tokens": 14374, "total_tokens": 4613636 }, "thread_token_usage": { "input_tokens": 4586483, "cached_input_tokens": 4502144, "cache_write_input_tokens": 0, "output_tokens": 27153, "reasoning_output_tokens": 14374, "total_tokens": 4613636 } } ## Step 70 · agent · 2026-09-23T05:49:19.411Z ### Thinking unavailable — no text recorded ### sleep · 2026-09-23T05:49:19.613Z { "duration_ms": 25000 } ### Tool output · 2026-09-23T05:49:44.621Z Wall time: 25.0040 seconds Sleep completed. ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "session_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "root_turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "response_id": "resp_021598e9bafc9da7016ab3684c9ddc87d2a3de311b75bebf04", "usage": { "input_tokens": 83849, "cached_input_tokens": 83328, "cache_write_input_tokens": 0, "output_tokens": 522, "reasoning_output_tokens": 501, "total_tokens": 84371 }, "turn_token_usage": { "input_tokens": 4670332, "cached_input_tokens": 4585472, "cache_write_input_tokens": 0, "output_tokens": 27675, "reasoning_output_tokens": 14875, "total_tokens": 4698007 }, "thread_token_usage": { "input_tokens": 4670332, "cached_input_tokens": 4585472, "cache_write_input_tokens": 0, "output_tokens": 27675, "reasoning_output_tokens": 14875, "total_tokens": 4698007 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-c723b6a2-efd0-432d-b298-6225875d152c", "process_id": "74673", "command": [ "/bin/sh", "-lc", "OPENBLAS_NUM_THREADS=1 OMP_NUM_THREADS=4 PYTHONDONTWRITEBYTECODE=1 /opt/task-venv/bin/python /app/solution/train_production.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/baseline_ckpt/best.pt --epochs 2 --threads 4 > /app/solution/control.json 2> /app/solution/control.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "OPENBLAS_NUM_THREADS=1 OMP_NUM_THREADS=4 PYTHONDONTWRITEBYTECODE=1 /opt/task-venv/bin/python /app/solution/train_production.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/baseline_ckpt/best.pt --epochs 2 --threads 4 > /app/solution/control.json 2> /app/solution/control.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 322, "nanos": 809956244 }, "formatted_output": "" } } ### Harness context { "type": "codex_item_completed", "item": { "type": "Extension", "kind": "clock.sleep", "id": "call_yW7b3ZQHE2mOwjf2MwJ4Czzw", "durationMs": 25000 } } ### exec · 2026-09-23T05:49:51.087Z { "code": "text(await tools.exec_command({cmd:\"tail -3 /app/solution/control.log && ls -l /app/solution/control_validation.parquet\",max_output_tokens:1000}));\ntext(await tools.clock__curr_time({}));\n" } ### Tool output · 2026-09-23T05:49:51.183Z Script completed Wall time 0.0 seconds Output: {"chunk_id":"555bed","wall_time_seconds":0.00000363,"exit_code":0,"original_token_count":41,"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 1273497 Sep 23 05:49 /app/solution/control_validation.parquet\n"} {"current_time":"2026-09-23 05:49:51 UTC"} ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-b15bc5e9-f096-44e5-8973-06278e2069aa", "process_id": "15425", "command": [ "/bin/sh", "-lc", "tail -3 /app/solution/control.log && ls -l /app/solution/control_validation.parquet" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -3 /app/solution/control.log && ls -l /app/solution/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 1273497 Sep 23 05:49 /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 1273497 Sep 23 05:49 /app/solution/control_validation.parquet\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 3630 }, "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 1273497 Sep 23 05:49 /app/solution/control_validation.parquet\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "session_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "root_turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "response_id": "resp_021598e9bafc9da7016ab36878c5b487d2a4407fac324acae2", "usage": { "input_tokens": 84394, "cached_input_tokens": 83712, "cache_write_input_tokens": 0, "output_tokens": 56, "reasoning_output_tokens": 0, "total_tokens": 84450 }, "turn_token_usage": { "input_tokens": 4754726, "cached_input_tokens": 4669184, "cache_write_input_tokens": 0, "output_tokens": 27731, "reasoning_output_tokens": 14875, "total_tokens": 4782457 }, "thread_token_usage": { "input_tokens": 4754726, "cached_input_tokens": 4669184, "cache_write_input_tokens": 0, "output_tokens": 27731, "reasoning_output_tokens": 14875, "total_tokens": 4782457 } } ### exec · 2026-09-23T05:50:01.385Z { "code": "text(await tools.exec_command({cmd:\"PYTHONDONTWRITEBYTECODE=1 /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:\"OPENBLAS_NUM_THREADS=1 OMP_NUM_THREADS=4 PYTHONDONTWRITEBYTECODE=1 /opt/task-venv/bin/python /app/solution/train_current_model.py --stage validation --public-dir /task --output /app/solution/pool_validation.parquet --prepared-cache /app/solution/validation_cache.npz --checkpoint-dir /app/solution/pool_ckpt --resume-checkpoint /app/solution/baseline_ckpt/best.pt --epochs 2 --threads 4 --loss-microbatch-size 4096 --hard-negatives 0 --hard-pool-size 4096 --ranking-loss pool-softmax > /app/solution/pool.json 2> /app/solution/pool.log\",yield_time_ms:1000,max_output_tokens:1000}));\n" } ### Tool output · 2026-09-23T05:50:03.712Z Script completed Wall time 2.3 seconds Output: {"chunk_id":"1bd558","wall_time_seconds":1.000364308,"session_id":89344,"original_token_count":0,"output":""} {"chunk_id":"99f498","wall_time_seconds":1.001901344,"session_id":84712,"original_token_count":0,"output":""} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "session_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "root_turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "response_id": "resp_021598e9bafc9da7016ab3687fa0d087d2b0fd8fe83023ff67", "usage": { "input_tokens": 84588, "cached_input_tokens": 84224, "cache_write_input_tokens": 0, "output_tokens": 253, "reasoning_output_tokens": 0, "total_tokens": 84841 }, "turn_token_usage": { "input_tokens": 4839314, "cached_input_tokens": 4753408, "cache_write_input_tokens": 0, "output_tokens": 27984, "reasoning_output_tokens": 14875, "total_tokens": 4867298 }, "thread_token_usage": { "input_tokens": 4839314, "cached_input_tokens": 4753408, "cache_write_input_tokens": 0, "output_tokens": 27984, "reasoning_output_tokens": 14875, "total_tokens": 4867298 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-a9aef47e-9bc2-48c6-bc2a-088d682fc5f9", "process_id": "89344", "command": [ "/bin/sh", "-lc", "PYTHONDONTWRITEBYTECODE=1 /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": "PYTHONDONTWRITEBYTECODE=1 /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\": 0.022962,\n \"quality_users\": 4263\n }\n}\n", "stderr": "", "aggregated_output": "{\n \"model\": \"/app/solution/control_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.022962,\n \"quality_users\": 4263\n }\n}\n", "exit_code": 0, "duration": { "secs": 3, "nanos": 260707468 }, "formatted_output": "{\n \"model\": \"/app/solution/control_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.022962,\n \"quality_users\": 4263\n }\n}\n" } } ### exec · 2026-09-23T05:50:06.273Z { "code": "text(await tools.write_stdin({session_id:89344,chars:\"\",yield_time_ms:1000,max_output_tokens:1000}));\n" } ### Tool output · 2026-09-23T05:50:06.360Z Script completed Wall time 0.0 seconds Output: {"chunk_id":"b38962","wall_time_seconds":0.0000027,"exit_code":0,"original_token_count":34,"output":"{\n \"model\": \"/app/solution/control_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.022962,\n \"quality_users\": 4263\n }\n}\n"} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "session_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "root_turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "response_id": "resp_021598e9bafc9da7016ab3688be10087d2a6805c80feebd8a2", "usage": { "input_tokens": 84932, "cached_input_tokens": 84352, "cache_write_input_tokens": 0, "output_tokens": 39, "reasoning_output_tokens": 0, "total_tokens": 84971 }, "turn_token_usage": { "input_tokens": 4924246, "cached_input_tokens": 4837760, "cache_write_input_tokens": 0, "output_tokens": 28023, "reasoning_output_tokens": 14875, "total_tokens": 4952269 }, "thread_token_usage": { "input_tokens": 4924246, "cached_input_tokens": 4837760, "cache_write_input_tokens": 0, "output_tokens": 28023, "reasoning_output_tokens": 14875, "total_tokens": 4952269 } } ## Step 71 · agent · 2026-09-23T05:50:21.827Z ### Thinking unavailable — no text recorded An extra epoch of the original objective improved public NDCG@10 to **0.022962**, a modest gain over 0.021280. The shared-pool softmax run has started from the same one-epoch baseline checkpoint, giving it the same total epoch count as that control. ## Step 72 · agent · 2026-09-23T05:50:28.774Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T05:50:33.551Z { "code": "text(await tools.exec_command({cmd:\"cat >> /app/solution/test_pool_objective.py <<'PY'\\nmodel = t.base.SequentialTwoTower(np.zeros((64, 4), np.float32), t.model_config(config))\\nstats, grad = t.score_target_microbatches(model, data, config,\\n torch.randn(2, 8), np.array([0, 1]), np.zeros((2, 4), np.float32),\\n torch.tensor([4, 5]), torch.empty(2, 0, dtype=torch.long), torch.empty(2, 0),\\n np.ones(2, np.float32), torch.device('cpu'), True, np.array([0, 1]), packed,\\n torch.Generator().manual_seed(9))\\nassert np.isfinite(stats['loss_sum'])\\nassert torch.isfinite(grad).all() and grad.abs().sum() > 0\\nassert all(torch.isfinite(p.grad).all() for p in model.parameters() if p.grad is not None)\\nprint('PASS: pooled ranking objective produces finite, nonzero sequence gradients')\\nPY\\nPYTHONDONTWRITEBYTECODE=1 /opt/task-venv/bin/python /app/solution/test_pool_objective.py && tail -4 /app/solution/pool.log\",yield_time_ms:1000,max_output_tokens:1000}));\n" } ### Tool output · 2026-09-23T05:50:34.723Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"18fe45","wall_time_seconds":1.001755204,"session_id":82529,"original_token_count":0,"output":""} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "session_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "root_turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "response_id": "resp_021598e9bafc9da7016ab3688e943487d2a0bd9299fd6d31e4", "usage": { "input_tokens": 85078, "cached_input_tokens": 84736, "cache_write_input_tokens": 0, "output_tokens": 820, "reasoning_output_tokens": 448, "total_tokens": 85898 }, "turn_token_usage": { "input_tokens": 5009324, "cached_input_tokens": 4922496, "cache_write_input_tokens": 0, "output_tokens": 28843, "reasoning_output_tokens": 15323, "total_tokens": 5038167 }, "thread_token_usage": { "input_tokens": 5009324, "cached_input_tokens": 4922496, "cache_write_input_tokens": 0, "output_tokens": 28843, "reasoning_output_tokens": 15323, "total_tokens": 5038167 } } ## Step 73 · agent · 2026-09-23T05:50:38.196Z ### Thinking unavailable — no text recorded ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-678649af-f260-4ff2-80ac-12b88da50afe", "process_id": "82529", "command": [ "/bin/sh", "-lc", "cat >> /app/solution/test_pool_objective.py <<'PY'\nmodel = t.base.SequentialTwoTower(np.zeros((64, 4), np.float32), t.model_config(config))\nstats, grad = t.score_target_microbatches(model, data, config,\n torch.randn(2, 8), np.array([0, 1]), np.zeros((2, 4), np.float32),\n torch.tensor([4, 5]), torch.empty(2, 0, dtype=torch.long), torch.empty(2, 0),\n np.ones(2, np.float32), torch.device('cpu'), True, np.array([0, 1]), packed,\n torch.Generator().manual_seed(9))\nassert np.isfinite(stats['loss_sum'])\nassert torch.isfinite(grad).all() and grad.abs().sum() > 0\nassert all(torch.isfinite(p.grad).all() for p in model.parameters() if p.grad is not None)\nprint('PASS: pooled ranking objective produces finite, nonzero sequence gradients')\nPY\nPYTHONDONTWRITEBYTECODE=1 /opt/task-venv/bin/python /app/solution/test_pool_objective.py && tail -4 /app/solution/pool.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat >> /app/solution/test_pool_objective.py <<'PY'\nmodel = t.base.SequentialTwoTower(np.zeros((64, 4), np.float32), t.model_config(config))\nstats, grad = t.score_target_microbatches(model, data, config,\n torch.randn(2, 8), np.array([0, 1]), np.zeros((2, 4), np.float32),\n torch.tensor([4, 5]), torch.empty(2, 0, dtype=torch.long), torch.empty(2, 0),\n np.ones(2, np.float32), torch.device('cpu'), True, np.array([0, 1]), packed,\n torch.Generator().manual_seed(9))\nassert np.isfinite(stats['loss_sum'])\nassert torch.isfinite(grad).all() and grad.abs().sum() > 0\nassert all(torch.isfinite(p.grad).all() for p in model.parameters() if p.grad is not None)\nprint('PASS: pooled ranking objective produces finite, nonzero sequence gradients')\nPY\nPYTHONDONTWRITEBYTECODE=1 /opt/task-venv/bin/python /app/solution/test_pool_objective.py && tail -4 /app/solution/pool.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "PASS: shared-pool loss equals the exact filtered full-catalog softmax\nPASS: pooled ranking objective produces finite, nonzero sequence gradients\nfull-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250\nusing a fresh AdamW optimizer after loading /app/solution/baseline_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=9.101647\n", "stderr": "", "aggregated_output": "PASS: shared-pool loss equals the exact filtered full-catalog softmax\nPASS: pooled ranking objective produces finite, nonzero sequence gradients\nfull-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250\nusing a fresh AdamW optimizer after loading /app/solution/baseline_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=9.101647\n", "exit_code": 0, "duration": { "secs": 4, "nanos": 567552912 }, "formatted_output": "PASS: shared-pool loss equals the exact filtered full-catalog softmax\nPASS: pooled ranking objective produces finite, nonzero sequence gradients\nfull-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250\nusing a fresh AdamW optimizer after loading /app/solution/baseline_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=9.101647\n" } } ### sleep · 2026-09-23T05:50:38.514Z { "duration_ms": 30000 } ### Tool output · 2026-09-23T05:51:08.522Z Wall time: 30.0034 seconds Sleep completed. ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "session_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "root_turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "response_id": "resp_021598e9bafc9da7016ab368aae0bc87d2984fcd807ce92c52", "usage": { "input_tokens": 85955, "cached_input_tokens": 84864, "cache_write_input_tokens": 0, "output_tokens": 81, "reasoning_output_tokens": 60, "total_tokens": 86036 }, "turn_token_usage": { "input_tokens": 5095279, "cached_input_tokens": 5007360, "cache_write_input_tokens": 0, "output_tokens": 28924, "reasoning_output_tokens": 15383, "total_tokens": 5124203 }, "thread_token_usage": { "input_tokens": 5095279, "cached_input_tokens": 5007360, "cache_write_input_tokens": 0, "output_tokens": 28924, "reasoning_output_tokens": 15383, "total_tokens": 5124203 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "Extension", "kind": "clock.sleep", "id": "call_w5wgmTDVzQlrMSRr7jRXhEmN", "durationMs": 30000 } } ### exec · 2026-09-23T05:51:15.465Z { "code": "text(await tools.write_stdin({session_id:82529,chars:\"\",yield_time_ms:1000,max_output_tokens:1000}));\ntext(await tools.exec_command({cmd:\"tail -4 /app/solution/pool.log && cat /sys/fs/cgroup/memory.current\",max_output_tokens:1000}));\ntext(await tools.clock__curr_time({}));\n" } ### Tool output · 2026-09-23T05:51:15.575Z Script completed Wall time 0.0 seconds Output: {"chunk_id":"a47723","wall_time_seconds":0.00000254,"exit_code":0,"original_token_count":149,"output":"PASS: shared-pool loss equals the exact filtered full-catalog softmax\nPASS: pooled ranking objective produces finite, nonzero sequence gradients\nfull-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250\nusing a fresh AdamW optimizer after loading /app/solution/baseline_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=9.101647\n"} {"chunk_id":"69727e","wall_time_seconds":0.000005951,"exit_code":0,"original_token_count":85,"output":"resumed validation-BCE training: epoch=1 loss=9.101647\nepoch=2/2 batches=25 targets=104,067 batch_bce=8.862767 running_bce=8.993304 elapsed=16.3s\nepoch=2/2 batches=50 targets=206,457 batch_bce=8.922698 running_bce=8.960631 elapsed=31.8s\nepoch=2/2 batches=75 targets=305,335 batch_bce=9.213475 running_bce=8.983503 elapsed=46.5s\n4763373568\n"} {"current_time":"2026-09-23 05:51:15 UTC"} ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-8e536ea0-f4ca-47f2-bb61-ef635d37060d", "process_id": "95845", "command": [ "/bin/sh", "-lc", "tail -4 /app/solution/pool.log && cat /sys/fs/cgroup/memory.current" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -4 /app/solution/pool.log && cat /sys/fs/cgroup/memory.current" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "resumed validation-BCE training: epoch=1 loss=9.101647\nepoch=2/2 batches=25 targets=104,067 batch_bce=8.862767 running_bce=8.993304 elapsed=16.3s\nepoch=2/2 batches=50 targets=206,457 batch_bce=8.922698 running_bce=8.960631 elapsed=31.8s\nepoch=2/2 batches=75 targets=305,335 batch_bce=9.213475 running_bce=8.983503 elapsed=46.5s\n4763373568\n", "stderr": "", "aggregated_output": "resumed validation-BCE training: epoch=1 loss=9.101647\nepoch=2/2 batches=25 targets=104,067 batch_bce=8.862767 running_bce=8.993304 elapsed=16.3s\nepoch=2/2 batches=50 targets=206,457 batch_bce=8.922698 running_bce=8.960631 elapsed=31.8s\nepoch=2/2 batches=75 targets=305,335 batch_bce=9.213475 running_bce=8.983503 elapsed=46.5s\n4763373568\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 5951 }, "formatted_output": "resumed validation-BCE training: epoch=1 loss=9.101647\nepoch=2/2 batches=25 targets=104,067 batch_bce=8.862767 running_bce=8.993304 elapsed=16.3s\nepoch=2/2 batches=50 targets=206,457 batch_bce=8.922698 running_bce=8.960631 elapsed=31.8s\nepoch=2/2 batches=75 targets=305,335 batch_bce=9.213475 running_bce=8.983503 elapsed=46.5s\n4763373568\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "session_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "root_turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "response_id": "resp_021598e9bafc9da7016ab368ccb5b887d2a269ecb222d8b27a", "usage": { "input_tokens": 86059, "cached_input_tokens": 85760, "cache_write_input_tokens": 0, "output_tokens": 84, "reasoning_output_tokens": 0, "total_tokens": 86143 }, "turn_token_usage": { "input_tokens": 5181338, "cached_input_tokens": 5093120, "cache_write_input_tokens": 0, "output_tokens": 29008, "reasoning_output_tokens": 15383, "total_tokens": 5210346 }, "thread_token_usage": { "input_tokens": 5181338, "cached_input_tokens": 5093120, "cache_write_input_tokens": 0, "output_tokens": 29008, "reasoning_output_tokens": 15383, "total_tokens": 5210346 } } ## Step 74 · agent · 2026-09-23T05:51:40.243Z ### Thinking unavailable — no text recorded The shared-pool run is using less memory and training faster than the explicit hard-mining version. A matrix multiply lets it score thousands of negatives without building a separate candidate-vector tensor for every target. Its backward-pass test also confirms finite, nonzero gradients. ## Step 75 · agent · 2026-09-23T05:52:05.728Z ### Thinking unavailable — no text recorded ### sleep · 2026-09-23T05:52:05.795Z { "duration_ms": 35000 } ### Tool output · 2026-09-23T05:52:40.802Z Wall time: 35.0034 seconds Sleep completed. ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "session_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "root_turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "response_id": "resp_021598e9bafc9da7016ab368d3c9ac87d28e97200910b6209c", "usage": { "input_tokens": 86534, "cached_input_tokens": 85888, "cache_write_input_tokens": 0, "output_tokens": 1114, "reasoning_output_tokens": 1034, "total_tokens": 87648 }, "turn_token_usage": { "input_tokens": 5267872, "cached_input_tokens": 5179008, "cache_write_input_tokens": 0, "output_tokens": 30122, "reasoning_output_tokens": 16417, "total_tokens": 5297994 }, "thread_token_usage": { "input_tokens": 5267872, "cached_input_tokens": 5179008, "cache_write_input_tokens": 0, "output_tokens": 30122, "reasoning_output_tokens": 16417, "total_tokens": 5297994 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "Extension", "kind": "clock.sleep", "id": "call_xdnDWQnH1Szg5d1FsTvaDZLm", "durationMs": 35000 } } ### exec · 2026-09-23T05:52:47.055Z { "code": "text(await tools.exec_command({cmd:\"tail -4 /app/solution/pool.log\",max_output_tokens:1000}));\ntext(await tools.clock__curr_time({}));\n" } ### Tool output · 2026-09-23T05:52:47.168Z Script completed Wall time 0.0 seconds Output: {"chunk_id":"320b06","wall_time_seconds":0.00000448,"exit_code":0,"original_token_count":93,"output":"epoch=2/2 batches=150 targets=596,745 batch_bce=8.832436 running_bce=8.928380 elapsed=91.5s\nepoch=2/2 batches=175 targets=696,141 batch_bce=8.870031 running_bce=8.925291 elapsed=107.4s\nepoch=2/2 batches=200 targets=790,005 batch_bce=8.843682 running_bce=8.911128 elapsed=122.7s\nepoch=2/2 batches=225 targets=884,435 batch_bce=8.589984 running_bce=8.902790 elapsed=138.2s\n"} {"current_time":"2026-09-23 05:52:47 UTC"} ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-ef765631-5c53-42ce-b27b-594e4622f3c9", "process_id": "19682", "command": [ "/bin/sh", "-lc", "tail -4 /app/solution/pool.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -4 /app/solution/pool.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "epoch=2/2 batches=150 targets=596,745 batch_bce=8.832436 running_bce=8.928380 elapsed=91.5s\nepoch=2/2 batches=175 targets=696,141 batch_bce=8.870031 running_bce=8.925291 elapsed=107.4s\nepoch=2/2 batches=200 targets=790,005 batch_bce=8.843682 running_bce=8.911128 elapsed=122.7s\nepoch=2/2 batches=225 targets=884,435 batch_bce=8.589984 running_bce=8.902790 elapsed=138.2s\n", "stderr": "", "aggregated_output": "epoch=2/2 batches=150 targets=596,745 batch_bce=8.832436 running_bce=8.928380 elapsed=91.5s\nepoch=2/2 batches=175 targets=696,141 batch_bce=8.870031 running_bce=8.925291 elapsed=107.4s\nepoch=2/2 batches=200 targets=790,005 batch_bce=8.843682 running_bce=8.911128 elapsed=122.7s\nepoch=2/2 batches=225 targets=884,435 batch_bce=8.589984 running_bce=8.902790 elapsed=138.2s\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4480 }, "formatted_output": "epoch=2/2 batches=150 targets=596,745 batch_bce=8.832436 running_bce=8.928380 elapsed=91.5s\nepoch=2/2 batches=175 targets=696,141 batch_bce=8.870031 running_bce=8.925291 elapsed=107.4s\nepoch=2/2 batches=200 targets=790,005 batch_bce=8.843682 running_bce=8.911128 elapsed=122.7s\nepoch=2/2 batches=225 targets=884,435 batch_bce=8.589984 running_bce=8.902790 elapsed=138.2s\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "session_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "root_turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "response_id": "resp_021598e9bafc9da7016ab36928fb7c87d28bb8f9bff2c2342a", "usage": { "input_tokens": 87671, "cached_input_tokens": 86400, "cache_write_input_tokens": 0, "output_tokens": 45, "reasoning_output_tokens": 0, "total_tokens": 87716 }, "turn_token_usage": { "input_tokens": 5355543, "cached_input_tokens": 5265408, "cache_write_input_tokens": 0, "output_tokens": 30167, "reasoning_output_tokens": 16417, "total_tokens": 5385710 }, "thread_token_usage": { "input_tokens": 5355543, "cached_input_tokens": 5265408, "cache_write_input_tokens": 0, "output_tokens": 30167, "reasoning_output_tokens": 16417, "total_tokens": 5385710 } } ## Step 76 · agent · 2026-09-23T05:52:58.121Z ### Thinking unavailable — no text recorded The shared-pool run is nearing the end of its training epoch. Its loss is decreasing, and the current runtime leaves enough budget for a final refit and a limited check of whether more training helps. ## Step 77 · agent · 2026-09-23T05:53:16.690Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T05:53:23.812Z { "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('parser.add_argument(\\\"--hard-pool-size\\\", type=int, default=Config.hard_pool_size)', 'parser.add_argument(\\\"--negative-pool-size\\\", \\\"--hard-pool-size\\\", dest=\\\"hard_pool_size\\\", type=int, default=Config.hard_pool_size)')\\ns=s.replace(''' if args.hard_negatives and args.negative_filter_scope != \\\"full-training-history\\\":''', ''' if args.ranking_loss == \\\"pool-softmax\\\" and args.hard_pool_size < 1:\\n parser.error(\\\"pooled softmax requires a nonempty negative pool\\\")\\n if (args.hard_negatives or args.ranking_loss == \\\"pool-softmax\\\") and args.negative_filter_scope != \\\"full-training-history\\\":''')\\ns=s.replace('chronological BCE window:', 'chronological target window:').replace('validation-BCE', 'validation-objective').replace('BCE objective changed:', 'Training objective changed:').replace(\\\"validation BCE\\\", \\\"validation objective\\\")\\np.write_text(s)\\nPY\\ntail -5 /app/solution/pool.log\",max_output_tokens:1200}));\n" } ### Tool output · 2026-09-23T05:53:23.994Z Script completed Wall time 0.2 seconds Output: {"chunk_id":"51f228","wall_time_seconds":0.00000516,"exit_code":0,"original_token_count":116,"output":"epoch=2/2 batches=150 targets=596,745 batch_bce=8.832436 running_bce=8.928380 elapsed=91.5s\nepoch=2/2 batches=175 targets=696,141 batch_bce=8.870031 running_bce=8.925291 elapsed=107.4s\nepoch=2/2 batches=200 targets=790,005 batch_bce=8.843682 running_bce=8.911128 elapsed=122.7s\nepoch=2/2 batches=225 targets=884,435 batch_bce=8.589984 running_bce=8.902790 elapsed=138.2s\nepoch=2/2 batches=250 targets=987,417 batch_bce=9.087470 running_bce=8.891479 elapsed=154.7s\n"} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "session_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "root_turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "response_id": "resp_021598e9bafc9da7016ab3692f60b887d2adc17a766abf3954", "usage": { "input_tokens": 87936, "cached_input_tokens": 87552, "cache_write_input_tokens": 0, "output_tokens": 1110, "reasoning_output_tokens": 767, "total_tokens": 89046 }, "turn_token_usage": { "input_tokens": 5443479, "cached_input_tokens": 5352960, "cache_write_input_tokens": 0, "output_tokens": 31277, "reasoning_output_tokens": 17184, "total_tokens": 5474756 }, "thread_token_usage": { "input_tokens": 5443479, "cached_input_tokens": 5352960, "cache_write_input_tokens": 0, "output_tokens": 31277, "reasoning_output_tokens": 17184, "total_tokens": 5474756 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-09adf9e2-6010-4487-a0cf-4c53332710c6", "process_id": "56494", "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('parser.add_argument(\"--hard-pool-size\", type=int, default=Config.hard_pool_size)', 'parser.add_argument(\"--negative-pool-size\", \"--hard-pool-size\", dest=\"hard_pool_size\", type=int, default=Config.hard_pool_size)')\ns=s.replace(''' if args.hard_negatives and args.negative_filter_scope != \"full-training-history\":''', ''' if args.ranking_loss == \"pool-softmax\" and args.hard_pool_size < 1:\n parser.error(\"pooled softmax requires a nonempty negative pool\")\n if (args.hard_negatives or args.ranking_loss == \"pool-softmax\") and args.negative_filter_scope != \"full-training-history\":''')\ns=s.replace('chronological BCE window:', 'chronological target window:').replace('validation-BCE', 'validation-objective').replace('BCE objective changed:', 'Training objective changed:').replace(\"validation BCE\", \"validation objective\")\np.write_text(s)\nPY\ntail -5 /app/solution/pool.log" ], "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('parser.add_argument(\"--hard-pool-size\", type=int, default=Config.hard_pool_size)', 'parser.add_argument(\"--negative-pool-size\", \"--hard-pool-size\", dest=\"hard_pool_size\", type=int, default=Config.hard_pool_size)')\ns=s.replace(''' if args.hard_negatives and args.negative_filter_scope != \"full-training-history\":''', ''' if args.ranking_loss == \"pool-softmax\" and args.hard_pool_size < 1:\n parser.error(\"pooled softmax requires a nonempty negative pool\")\n if (args.hard_negatives or args.ranking_loss == \"pool-softmax\") and args.negative_filter_scope != \"full-training-history\":''')\ns=s.replace('chronological BCE window:', 'chronological target window:').replace('validation-BCE', 'validation-objective').replace('BCE objective changed:', 'Training objective changed:').replace(\"validation BCE\", \"validation objective\")\np.write_text(s)\nPY\ntail -5 /app/solution/pool.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "epoch=2/2 batches=150 targets=596,745 batch_bce=8.832436 running_bce=8.928380 elapsed=91.5s\nepoch=2/2 batches=175 targets=696,141 batch_bce=8.870031 running_bce=8.925291 elapsed=107.4s\nepoch=2/2 batches=200 targets=790,005 batch_bce=8.843682 running_bce=8.911128 elapsed=122.7s\nepoch=2/2 batches=225 targets=884,435 batch_bce=8.589984 running_bce=8.902790 elapsed=138.2s\nepoch=2/2 batches=250 targets=987,417 batch_bce=9.087470 running_bce=8.891479 elapsed=154.7s\n", "stderr": "", "aggregated_output": "epoch=2/2 batches=150 targets=596,745 batch_bce=8.832436 running_bce=8.928380 elapsed=91.5s\nepoch=2/2 batches=175 targets=696,141 batch_bce=8.870031 running_bce=8.925291 elapsed=107.4s\nepoch=2/2 batches=200 targets=790,005 batch_bce=8.843682 running_bce=8.911128 elapsed=122.7s\nepoch=2/2 batches=225 targets=884,435 batch_bce=8.589984 running_bce=8.902790 elapsed=138.2s\nepoch=2/2 batches=250 targets=987,417 batch_bce=9.087470 running_bce=8.891479 elapsed=154.7s\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 5160 }, "formatted_output": "epoch=2/2 batches=150 targets=596,745 batch_bce=8.832436 running_bce=8.928380 elapsed=91.5s\nepoch=2/2 batches=175 targets=696,141 batch_bce=8.870031 running_bce=8.925291 elapsed=107.4s\nepoch=2/2 batches=200 targets=790,005 batch_bce=8.843682 running_bce=8.911128 elapsed=122.7s\nepoch=2/2 batches=225 targets=884,435 batch_bce=8.589984 running_bce=8.902790 elapsed=138.2s\nepoch=2/2 batches=250 targets=987,417 batch_bce=9.087470 running_bce=8.891479 elapsed=154.7s\n" } } ## Step 78 · agent · 2026-09-23T05:53:27.133Z ### Thinking unavailable — no text recorded ### sleep · 2026-09-23T05:53:27.320Z { "duration_ms": 35000 } ### Tool output · 2026-09-23T05:54:02.328Z Wall time: 35.0038 seconds Sleep completed. ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "session_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "root_turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "response_id": "resp_021598e9bafc9da7016ab36954284087d2938461c359e153a6", "usage": { "input_tokens": 89285, "cached_input_tokens": 87808, "cache_write_input_tokens": 0, "output_tokens": 39, "reasoning_output_tokens": 18, "total_tokens": 89324 }, "turn_token_usage": { "input_tokens": 5532764, "cached_input_tokens": 5440768, "cache_write_input_tokens": 0, "output_tokens": 31316, "reasoning_output_tokens": 17202, "total_tokens": 5564080 }, "thread_token_usage": { "input_tokens": 5532764, "cached_input_tokens": 5440768, "cache_write_input_tokens": 0, "output_tokens": 31316, "reasoning_output_tokens": 17202, "total_tokens": 5564080 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "Extension", "kind": "clock.sleep", "id": "call_8MSXxVC11tYqMNyfsFtPJiJm", "durationMs": 35000 } } ### exec · 2026-09-23T05:54:05.746Z { "code": "text(await tools.exec_command({cmd:\"tail -5 /app/solution/pool.log\",max_output_tokens:1000}));\ntext(await tools.clock__curr_time({}));\n" } ### Tool output · 2026-09-23T05:54:05.852Z Script completed Wall time 0.0 seconds Output: {"chunk_id":"8bddb6","wall_time_seconds":0.00000489,"exit_code":0,"original_token_count":120,"output":"epoch=2/2 batches=225 targets=884,435 batch_bce=8.589984 running_bce=8.902790 elapsed=138.2s\nepoch=2/2 batches=250 targets=987,417 batch_bce=9.087470 running_bce=8.891479 elapsed=154.7s\nsaved epoch checkpoint: /app/solution/pool_ckpt/epoch-02.pt\nepoch=2/2 train_loss=8.88921 validation_loss=8.72030 pair_accuracy=0.847 train_targets=1,052,953 validation_targets=130,103 train_seconds=165.6 validation_seconds=13.6\nrestored best validation-BCE checkpoint: epoch=2 loss=8.720298\n"} {"current_time":"2026-09-23 05:54:05 UTC"} ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-16edd68f-f2f2-48f5-a36a-ced26412c17c", "process_id": "7196", "command": [ "/bin/sh", "-lc", "tail -5 /app/solution/pool.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -5 /app/solution/pool.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "epoch=2/2 batches=225 targets=884,435 batch_bce=8.589984 running_bce=8.902790 elapsed=138.2s\nepoch=2/2 batches=250 targets=987,417 batch_bce=9.087470 running_bce=8.891479 elapsed=154.7s\nsaved epoch checkpoint: /app/solution/pool_ckpt/epoch-02.pt\nepoch=2/2 train_loss=8.88921 validation_loss=8.72030 pair_accuracy=0.847 train_targets=1,052,953 validation_targets=130,103 train_seconds=165.6 validation_seconds=13.6\nrestored best validation-BCE checkpoint: epoch=2 loss=8.720298\n", "stderr": "", "aggregated_output": "epoch=2/2 batches=225 targets=884,435 batch_bce=8.589984 running_bce=8.902790 elapsed=138.2s\nepoch=2/2 batches=250 targets=987,417 batch_bce=9.087470 running_bce=8.891479 elapsed=154.7s\nsaved epoch checkpoint: /app/solution/pool_ckpt/epoch-02.pt\nepoch=2/2 train_loss=8.88921 validation_loss=8.72030 pair_accuracy=0.847 train_targets=1,052,953 validation_targets=130,103 train_seconds=165.6 validation_seconds=13.6\nrestored best validation-BCE checkpoint: epoch=2 loss=8.720298\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4890 }, "formatted_output": "epoch=2/2 batches=225 targets=884,435 batch_bce=8.589984 running_bce=8.902790 elapsed=138.2s\nepoch=2/2 batches=250 targets=987,417 batch_bce=9.087470 running_bce=8.891479 elapsed=154.7s\nsaved epoch checkpoint: /app/solution/pool_ckpt/epoch-02.pt\nepoch=2/2 train_loss=8.88921 validation_loss=8.72030 pair_accuracy=0.847 train_targets=1,052,953 validation_targets=130,103 train_seconds=165.6 validation_seconds=13.6\nrestored best validation-BCE checkpoint: epoch=2 loss=8.720298\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "session_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "root_turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "response_id": "resp_021598e9bafc9da7016ab3697a859887d2bf494a51097e6707", "usage": { "input_tokens": 89347, "cached_input_tokens": 89088, "cache_write_input_tokens": 0, "output_tokens": 45, "reasoning_output_tokens": 0, "total_tokens": 89392 }, "turn_token_usage": { "input_tokens": 5622111, "cached_input_tokens": 5529856, "cache_write_input_tokens": 0, "output_tokens": 31361, "reasoning_output_tokens": 17202, "total_tokens": 5653472 }, "thread_token_usage": { "input_tokens": 5622111, "cached_input_tokens": 5529856, "cache_write_input_tokens": 0, "output_tokens": 31361, "reasoning_output_tokens": 17202, "total_tokens": 5653472 } } ## Step 79 · agent · 2026-09-23T05:54:25.811Z ### Thinking unavailable — no text recorded The shared-pool epoch finished in about 166 seconds, compared with 221 seconds for the extra baseline epoch. Its chronological validation loss improved as well. I’m now checking whether those gains translate to public NDCG@10. ### sleep · 2026-09-23T05:54:27.243Z { "duration_ms": 40000 } ### Tool output · 2026-09-23T05:55:07.250Z Wall time: 40.0038 seconds Sleep completed. ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "session_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "root_turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "response_id": "resp_021598e9bafc9da7016ab3697e174087d2931250e8dc3bd040", "usage": { "input_tokens": 89628, "cached_input_tokens": 89216, "cache_write_input_tokens": 0, "output_tokens": 567, "reasoning_output_tokens": 492, "total_tokens": 90195 }, "turn_token_usage": { "input_tokens": 5711739, "cached_input_tokens": 5619072, "cache_write_input_tokens": 0, "output_tokens": 31928, "reasoning_output_tokens": 17694, "total_tokens": 5743667 }, "thread_token_usage": { "input_tokens": 5711739, "cached_input_tokens": 5619072, "cache_write_input_tokens": 0, "output_tokens": 31928, "reasoning_output_tokens": 17694, "total_tokens": 5743667 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-6321f3b7-871f-4334-9191-967829a39cca", "process_id": "84712", "command": [ "/bin/sh", "-lc", "OPENBLAS_NUM_THREADS=1 OMP_NUM_THREADS=4 PYTHONDONTWRITEBYTECODE=1 /opt/task-venv/bin/python /app/solution/train_current_model.py --stage validation --public-dir /task --output /app/solution/pool_validation.parquet --prepared-cache /app/solution/validation_cache.npz --checkpoint-dir /app/solution/pool_ckpt --resume-checkpoint /app/solution/baseline_ckpt/best.pt --epochs 2 --threads 4 --loss-microbatch-size 4096 --hard-negatives 0 --hard-pool-size 4096 --ranking-loss pool-softmax > /app/solution/pool.json 2> /app/solution/pool.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "OPENBLAS_NUM_THREADS=1 OMP_NUM_THREADS=4 PYTHONDONTWRITEBYTECODE=1 /opt/task-venv/bin/python /app/solution/train_current_model.py --stage validation --public-dir /task --output /app/solution/pool_validation.parquet --prepared-cache /app/solution/validation_cache.npz --checkpoint-dir /app/solution/pool_ckpt --resume-checkpoint /app/solution/baseline_ckpt/best.pt --epochs 2 --threads 4 --loss-microbatch-size 4096 --hard-negatives 0 --hard-pool-size 4096 --ranking-loss pool-softmax > /app/solution/pool.json 2> /app/solution/pool.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 280, "nanos": 8838558 }, "formatted_output": "" } } ### Harness context { "type": "codex_item_completed", "item": { "type": "Extension", "kind": "clock.sleep", "id": "call_x3z7khyLSThd37F9lvca9X21", "durationMs": 40000 } } ### exec · 2026-09-23T05:55:10.513Z { "code": "text(await tools.exec_command({cmd:\"tail -3 /app/solution/pool.log && ls -l /app/solution/pool_validation.parquet\",max_output_tokens:1000}));\ntext(await tools.clock__curr_time({}));\n" } ### Tool output · 2026-09-23T05:55:10.614Z Script completed Wall time 0.0 seconds Output: {"chunk_id":"bec490","wall_time_seconds":0.000006641,"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 1261072 Sep 23 05:54 /app/solution/pool_validation.parquet\n"} {"current_time":"2026-09-23 05:55:10 UTC"} ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-c85f6123-f107-4123-83cd-ad6fdc1fad83", "process_id": "79417", "command": [ "/bin/sh", "-lc", "tail -3 /app/solution/pool.log && ls -l /app/solution/pool_validation.parquet" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -3 /app/solution/pool.log && ls -l /app/solution/pool_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 1261072 Sep 23 05:54 /app/solution/pool_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 1261072 Sep 23 05:54 /app/solution/pool_validation.parquet\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 6641 }, "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 1261072 Sep 23 05:54 /app/solution/pool_validation.parquet\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "session_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "root_turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "response_id": "resp_021598e9bafc9da7016ab369bb67c087d2a26653ef075f7eeb", "usage": { "input_tokens": 90218, "cached_input_tokens": 89472, "cache_write_input_tokens": 0, "output_tokens": 58, "reasoning_output_tokens": 0, "total_tokens": 90276 }, "turn_token_usage": { "input_tokens": 5801957, "cached_input_tokens": 5708544, "cache_write_input_tokens": 0, "output_tokens": 31986, "reasoning_output_tokens": 17694, "total_tokens": 5833943 }, "thread_token_usage": { "input_tokens": 5801957, "cached_input_tokens": 5708544, "cache_write_input_tokens": 0, "output_tokens": 31986, "reasoning_output_tokens": 17694, "total_tokens": 5833943 } } ### exec · 2026-09-23T05:55:14.853Z { "code": "text(await tools.exec_command({cmd:\"PYTHONDONTWRITEBYTECODE=1 /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/pool_validation.parquet > /app/solution/pool_score.json && cat /app/solution/pool_score.json\",yield_time_ms:1000,max_output_tokens:1000}));\n" } ### Tool output · 2026-09-23T05:55:16.018Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"9f0cff","wall_time_seconds":1.000687418,"session_id":59383,"original_token_count":0,"output":""} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "session_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "root_turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "response_id": "resp_021598e9bafc9da7016ab369beda4087d2848f3b3efe02b00a", "usage": { "input_tokens": 90415, "cached_input_tokens": 89984, "cache_write_input_tokens": 0, "output_tokens": 83, "reasoning_output_tokens": 0, "total_tokens": 90498 }, "turn_token_usage": { "input_tokens": 5892372, "cached_input_tokens": 5798528, "cache_write_input_tokens": 0, "output_tokens": 32069, "reasoning_output_tokens": 17694, "total_tokens": 5924441 }, "thread_token_usage": { "input_tokens": 5892372, "cached_input_tokens": 5798528, "cache_write_input_tokens": 0, "output_tokens": 32069, "reasoning_output_tokens": 17694, "total_tokens": 5924441 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-9ec4eb86-1309-4f97-b7a6-e843276bc30d", "process_id": "59383", "command": [ "/bin/sh", "-lc", "PYTHONDONTWRITEBYTECODE=1 /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/pool_validation.parquet > /app/solution/pool_score.json && cat /app/solution/pool_score.json" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "PYTHONDONTWRITEBYTECODE=1 /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/pool_validation.parquet > /app/solution/pool_score.json && cat /app/solution/pool_score.json" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{\n \"model\": \"/app/solution/pool_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.022759,\n \"quality_users\": 4263\n }\n}\n", "stderr": "", "aggregated_output": "{\n \"model\": \"/app/solution/pool_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.022759,\n \"quality_users\": 4263\n }\n}\n", "exit_code": 0, "duration": { "secs": 3, "nanos": 193443171 }, "formatted_output": "{\n \"model\": \"/app/solution/pool_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.022759,\n \"quality_users\": 4263\n }\n}\n" } } ### exec · 2026-09-23T05:55:21.605Z { "code": "text(await tools.write_stdin({session_id:59383,chars:\"\",yield_time_ms:1000,max_output_tokens:1000}));\n" } ### Tool output · 2026-09-23T05:55:21.704Z Script completed Wall time 0.0 seconds Output: {"chunk_id":"fe9ea1","wall_time_seconds":0.00000208,"exit_code":0,"original_token_count":34,"output":"{\n \"model\": \"/app/solution/pool_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.022759,\n \"quality_users\": 4263\n }\n}\n"} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "session_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "root_turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "response_id": "resp_021598e9bafc9da7016ab369c42d4c87d28c6cdde37a9cb80a", "usage": { "input_tokens": 90556, "cached_input_tokens": 90240, "cache_write_input_tokens": 0, "output_tokens": 39, "reasoning_output_tokens": 0, "total_tokens": 90595 }, "turn_token_usage": { "input_tokens": 5982928, "cached_input_tokens": 5888768, "cache_write_input_tokens": 0, "output_tokens": 32108, "reasoning_output_tokens": 17694, "total_tokens": 6015036 }, "thread_token_usage": { "input_tokens": 5982928, "cached_input_tokens": 5888768, "cache_write_input_tokens": 0, "output_tokens": 32108, "reasoning_output_tokens": 17694, "total_tokens": 6015036 } } ## Step 80 · agent · 2026-09-23T05:55:42.348Z ### Thinking unavailable — no text recorded ## Step 81 · agent · 2026-09-23T05:55:47.844Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T05:55:54.268Z { "code": "text(await tools.exec_command({cmd:\"OPENBLAS_NUM_THREADS=1 OMP_NUM_THREADS=4 PYTHONDONTWRITEBYTECODE=1 /opt/task-venv/bin/python /app/solution/train_current_model.py --stage validation --public-dir /task --output /app/solution/pool4_validation.parquet --prepared-cache /app/solution/validation_cache.npz --checkpoint-dir /app/solution/pool4_ckpt --resume-checkpoint /app/solution/pool_ckpt/best.pt --epochs 4 --early-stopping-patience 2 --threads 4 --loss-microbatch-size 4096 --hard-negatives 0 --negative-pool-size 4096 --ranking-loss pool-softmax > /app/solution/pool4.json 2> /app/solution/pool4.log\",yield_time_ms:1000,max_output_tokens:1000}));\ntext(await tools.exec_command({cmd:\"PYTHONDONTWRITEBYTECODE=1 /opt/task-venv/bin/python /app/solution/compare_public.py /app/solution/production_validation.parquet /app/solution/control_validation.parquet /app/solution/pool_validation.parquet > /app/solution/comparison_initial.json\",yield_time_ms:1000,max_output_tokens:1000}));\n" } ### Tool output · 2026-09-23T05:55:56.596Z Script completed Wall time 2.3 seconds Output: {"chunk_id":"306648","wall_time_seconds":1.001579581,"session_id":72919,"original_token_count":0,"output":""} {"chunk_id":"cdcc53","wall_time_seconds":1.001337258,"session_id":72164,"original_token_count":0,"output":""} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "session_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "root_turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "response_id": "resp_021598e9bafc9da7016ab369c9f43087d2aea8650cb97f239a", "usage": { "input_tokens": 90704, "cached_input_tokens": 90368, "cache_write_input_tokens": 0, "output_tokens": 932, "reasoning_output_tokens": 653, "total_tokens": 91636 }, "turn_token_usage": { "input_tokens": 6073632, "cached_input_tokens": 5979136, "cache_write_input_tokens": 0, "output_tokens": 33040, "reasoning_output_tokens": 18347, "total_tokens": 6106672 }, "thread_token_usage": { "input_tokens": 6073632, "cached_input_tokens": 5979136, "cache_write_input_tokens": 0, "output_tokens": 33040, "reasoning_output_tokens": 18347, "total_tokens": 6106672 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-aabf2c9d-5c52-4a3a-8661-41caef3d9e16", "process_id": "72164", "command": [ "/bin/sh", "-lc", "PYTHONDONTWRITEBYTECODE=1 /opt/task-venv/bin/python /app/solution/compare_public.py /app/solution/production_validation.parquet /app/solution/control_validation.parquet /app/solution/pool_validation.parquet > /app/solution/comparison_initial.json" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "PYTHONDONTWRITEBYTECODE=1 /opt/task-venv/bin/python /app/solution/compare_public.py /app/solution/production_validation.parquet /app/solution/control_validation.parquet /app/solution/pool_validation.parquet > /app/solution/comparison_initial.json" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 5, "nanos": 858576074 }, "formatted_output": "" } } ## Step 82 · agent · 2026-09-23T05:56:06.368Z ### Thinking unavailable — no text recorded Shared-pool softmax scored **0.022759 NDCG@10**—above production, but slightly below the two-epoch control. I’m continuing it for two more epochs to test whether the faster objective learns stronger personalization with additional training. After that check, I’ll select the configuration and complete the final refit. ## Step 83 · agent · 2026-09-23T05:56:31.474Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T05:56:32.152Z { "code": "text(await tools.exec_command({cmd:\"cat /app/solution/comparison_initial.json && tail -4 /app/solution/pool4.log\",max_output_tokens:1800}));\ntext(await tools.clock__curr_time({}));\n" } ### Tool output · 2026-09-23T05:56:32.243Z Script completed Wall time 0.0 seconds Output: {"chunk_id":"386380","wall_time_seconds":0.00000549,"exit_code":0,"original_token_count":254,"output":"{\n \"/app/solution/production_validation.parquet\": {\n \"ndcg_at_10\": 0.021280490854136063,\n \"users\": 4263,\n \"delta_from_first\": 0.0,\n \"paired_bootstrap_95pct\": [\n 0.0,\n 0.0\n ]\n },\n \"/app/solution/control_validation.parquet\": {\n \"ndcg_at_10\": 0.02296198006527616,\n \"users\": 4263,\n \"delta_from_first\": 0.001681489211140097,\n \"paired_bootstrap_95pct\": [\n -0.0010001091805730146,\n 0.003947676736041453\n ]\n },\n \"/app/solution/pool_validation.parquet\": {\n \"ndcg_at_10\": 0.022759429484195553,\n \"users\": 4263,\n \"delta_from_first\": 0.00147893863005949,\n \"paired_bootstrap_95pct\": [\n -0.0009425644888854981,\n 0.00391680342137338\n ]\n }\n}\nfull-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250\nrestored optimizer state from /app/solution/pool_ckpt/best.pt\nresumed validation-objective training: epoch=2 loss=8.720298\nepoch=3/4 batches=25 targets=104,704 batch_bce=8.583265 running_bce=8.644224 elapsed=16.1s\n"} {"current_time":"2026-09-23 05:56:32 UTC"} ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-ac0b2899-fe82-476d-8d01-b35540c882c5", "process_id": "96536", "command": [ "/bin/sh", "-lc", "cat /app/solution/comparison_initial.json && tail -4 /app/solution/pool4.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat /app/solution/comparison_initial.json && tail -4 /app/solution/pool4.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{\n \"/app/solution/production_validation.parquet\": {\n \"ndcg_at_10\": 0.021280490854136063,\n \"users\": 4263,\n \"delta_from_first\": 0.0,\n \"paired_bootstrap_95pct\": [\n 0.0,\n 0.0\n ]\n },\n \"/app/solution/control_validation.parquet\": {\n \"ndcg_at_10\": 0.02296198006527616,\n \"users\": 4263,\n \"delta_from_first\": 0.001681489211140097,\n \"paired_bootstrap_95pct\": [\n -0.0010001091805730146,\n 0.003947676736041453\n ]\n },\n \"/app/solution/pool_validation.parquet\": {\n \"ndcg_at_10\": 0.022759429484195553,\n \"users\": 4263,\n \"delta_from_first\": 0.00147893863005949,\n \"paired_bootstrap_95pct\": [\n -0.0009425644888854981,\n 0.00391680342137338\n ]\n }\n}\nfull-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250\nrestored optimizer state from /app/solution/pool_ckpt/best.pt\nresumed validation-objective training: epoch=2 loss=8.720298\nepoch=3/4 batches=25 targets=104,704 batch_bce=8.583265 running_bce=8.644224 elapsed=16.1s\n", "stderr": "", "aggregated_output": "{\n \"/app/solution/production_validation.parquet\": {\n \"ndcg_at_10\": 0.021280490854136063,\n \"users\": 4263,\n \"delta_from_first\": 0.0,\n \"paired_bootstrap_95pct\": [\n 0.0,\n 0.0\n ]\n },\n \"/app/solution/control_validation.parquet\": {\n \"ndcg_at_10\": 0.02296198006527616,\n \"users\": 4263,\n \"delta_from_first\": 0.001681489211140097,\n \"paired_bootstrap_95pct\": [\n -0.0010001091805730146,\n 0.003947676736041453\n ]\n },\n \"/app/solution/pool_validation.parquet\": {\n \"ndcg_at_10\": 0.022759429484195553,\n \"users\": 4263,\n \"delta_from_first\": 0.00147893863005949,\n \"paired_bootstrap_95pct\": [\n -0.0009425644888854981,\n 0.00391680342137338\n ]\n }\n}\nfull-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250\nrestored optimizer state from /app/solution/pool_ckpt/best.pt\nresumed validation-objective training: epoch=2 loss=8.720298\nepoch=3/4 batches=25 targets=104,704 batch_bce=8.583265 running_bce=8.644224 elapsed=16.1s\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 5490 }, "formatted_output": "{\n \"/app/solution/production_validation.parquet\": {\n \"ndcg_at_10\": 0.021280490854136063,\n \"users\": 4263,\n \"delta_from_first\": 0.0,\n \"paired_bootstrap_95pct\": [\n 0.0,\n 0.0\n ]\n },\n \"/app/solution/control_validation.parquet\": {\n \"ndcg_at_10\": 0.02296198006527616,\n \"users\": 4263,\n \"delta_from_first\": 0.001681489211140097,\n \"paired_bootstrap_95pct\": [\n -0.0010001091805730146,\n 0.003947676736041453\n ]\n },\n \"/app/solution/pool_validation.parquet\": {\n \"ndcg_at_10\": 0.022759429484195553,\n \"users\": 4263,\n \"delta_from_first\": 0.00147893863005949,\n \"paired_bootstrap_95pct\": [\n -0.0009425644888854981,\n 0.00391680342137338\n ]\n }\n}\nfull-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250\nrestored optimizer state from /app/solution/pool_ckpt/best.pt\nresumed validation-objective training: epoch=2 loss=8.720298\nepoch=3/4 batches=25 targets=104,704 batch_bce=8.583265 running_bce=8.644224 elapsed=16.1s\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "session_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "root_turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "response_id": "resp_021598e9bafc9da7016ab369ecd5f887d2849b6e472b4af95a", "usage": { "input_tokens": 91726, "cached_input_tokens": 90496, "cache_write_input_tokens": 0, "output_tokens": 882, "reasoning_output_tokens": 750, "total_tokens": 92608 }, "turn_token_usage": { "input_tokens": 6165358, "cached_input_tokens": 6069632, "cache_write_input_tokens": 0, "output_tokens": 33922, "reasoning_output_tokens": 19097, "total_tokens": 6199280 }, "thread_token_usage": { "input_tokens": 6165358, "cached_input_tokens": 6069632, "cache_write_input_tokens": 0, "output_tokens": 33922, "reasoning_output_tokens": 19097, "total_tokens": 6199280 } } ## Step 84 · agent · 2026-09-23T05:56:54.166Z ### Thinking unavailable — no text recorded ## Step 85 · agent · 2026-09-23T05:57:12.296Z ### Thinking unavailable — no text recorded The initial gains are small: paired bootstrap intervals for both two-epoch models still include zero improvement over production. I’ll use the final checkpoint backtest to make the selection and keep that uncertainty in the experiment record. ### sleep · 2026-09-23T05:57:13.584Z { "duration_ms": 40000 } ### Tool output · 2026-09-23T05:57:53.593Z Wall time: 40.0051 seconds Sleep completed. ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "session_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "root_turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "response_id": "resp_021598e9bafc9da7016ab36a10804087d2a4647cd24caaa971", "usage": { "input_tokens": 93074, "cached_input_tokens": 91520, "cache_write_input_tokens": 0, "output_tokens": 1105, "reasoning_output_tokens": 1034, "total_tokens": 94179 }, "turn_token_usage": { "input_tokens": 6258432, "cached_input_tokens": 6161152, "cache_write_input_tokens": 0, "output_tokens": 35027, "reasoning_output_tokens": 20131, "total_tokens": 6293459 }, "thread_token_usage": { "input_tokens": 6258432, "cached_input_tokens": 6161152, "cache_write_input_tokens": 0, "output_tokens": 35027, "reasoning_output_tokens": 20131, "total_tokens": 6293459 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "Extension", "kind": "clock.sleep", "id": "call_omSDQfE2GM0K2ddWYICf2xaO", "durationMs": 40000 } } ### exec · 2026-09-23T05:57:56.850Z { "code": "text(await tools.exec_command({cmd:\"tail -4 /app/solution/pool4.log\",max_output_tokens:1000}));\ntext(await tools.clock__curr_time({}));\n" } ### Tool output · 2026-09-23T05:57:56.969Z Script completed Wall time 0.0 seconds Output: {"chunk_id":"ce4923","wall_time_seconds":0.00000539,"exit_code":0,"original_token_count":93,"output":"epoch=3/4 batches=100 targets=405,521 batch_bce=8.394366 running_bce=8.611325 elapsed=62.7s\nepoch=3/4 batches=125 targets=505,650 batch_bce=8.662292 running_bce=8.606857 elapsed=78.6s\nepoch=3/4 batches=150 targets=598,098 batch_bce=8.103498 running_bce=8.590917 elapsed=93.6s\nepoch=3/4 batches=175 targets=699,121 batch_bce=8.379889 running_bce=8.566248 elapsed=110.0s\n"} {"current_time":"2026-09-23 05:57:56 UTC"} ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-4cb58cba-a8cf-451e-8ff6-2cea9d4ff274", "process_id": "23644", "command": [ "/bin/sh", "-lc", "tail -4 /app/solution/pool4.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -4 /app/solution/pool4.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "epoch=3/4 batches=100 targets=405,521 batch_bce=8.394366 running_bce=8.611325 elapsed=62.7s\nepoch=3/4 batches=125 targets=505,650 batch_bce=8.662292 running_bce=8.606857 elapsed=78.6s\nepoch=3/4 batches=150 targets=598,098 batch_bce=8.103498 running_bce=8.590917 elapsed=93.6s\nepoch=3/4 batches=175 targets=699,121 batch_bce=8.379889 running_bce=8.566248 elapsed=110.0s\n", "stderr": "", "aggregated_output": "epoch=3/4 batches=100 targets=405,521 batch_bce=8.394366 running_bce=8.611325 elapsed=62.7s\nepoch=3/4 batches=125 targets=505,650 batch_bce=8.662292 running_bce=8.606857 elapsed=78.6s\nepoch=3/4 batches=150 targets=598,098 batch_bce=8.103498 running_bce=8.590917 elapsed=93.6s\nepoch=3/4 batches=175 targets=699,121 batch_bce=8.379889 running_bce=8.566248 elapsed=110.0s\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 5390 }, "formatted_output": "epoch=3/4 batches=100 targets=405,521 batch_bce=8.394366 running_bce=8.611325 elapsed=62.7s\nepoch=3/4 batches=125 targets=505,650 batch_bce=8.662292 running_bce=8.606857 elapsed=78.6s\nepoch=3/4 batches=150 targets=598,098 batch_bce=8.103498 running_bce=8.590917 elapsed=93.6s\nepoch=3/4 batches=175 targets=699,121 batch_bce=8.379889 running_bce=8.566248 elapsed=110.0s\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "session_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "root_turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "response_id": "resp_021598e9bafc9da7016ab36a61c3e087d28a39eeb721c7e46d", "usage": { "input_tokens": 94202, "cached_input_tokens": 92928, "cache_write_input_tokens": 0, "output_tokens": 46, "reasoning_output_tokens": 0, "total_tokens": 94248 }, "turn_token_usage": { "input_tokens": 6352634, "cached_input_tokens": 6254080, "cache_write_input_tokens": 0, "output_tokens": 35073, "reasoning_output_tokens": 20131, "total_tokens": 6387707 }, "thread_token_usage": { "input_tokens": 6352634, "cached_input_tokens": 6254080, "cache_write_input_tokens": 0, "output_tokens": 35073, "reasoning_output_tokens": 20131, "total_tokens": 6387707 } } ## Step 86 · agent · 2026-09-23T05:58:18.641Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T05:58:27.538Z { "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()\\ns=s.replace('\\\"baseline\\\": \\\"yambda-windowed-sequence-two-tower-hard-negatives\\\"', '\\\"baseline\\\": \\\"yambda-windowed-sequence-two-tower-negative-objective\\\"')\\ns=s.replace('''\\\"negative_sampling\\\": {\\n \\\"uniform\\\": config.uniform_negatives,\\n \\\"frequency-corrected-inbatch\\\": config.inbatch_negatives,\\n \\\"mined_hard\\\": config.hard_negatives,\\n \\\"shared_random_pool\\\": config.hard_pool_size,\\n \\\"hard_loss_weight\\\": config.hard_loss_weight,\\n },''', '''\\\"negative_sampling\\\": ({\\n \\\"proposal\\\": \\\"uniform-without-replacement-shared-pool\\\",\\n \\\"pool_size\\\": config.hard_pool_size,\\n \\\"partition_correction\\\": \\\"catalog_size/pool_size\\\",\\n } if config.ranking_loss == \\\"pool-softmax\\\" else {\\n \\\"uniform\\\": config.uniform_negatives,\\n \\\"frequency-corrected-inbatch\\\": config.inbatch_negatives,\\n \\\"mined_hard\\\": config.hard_negatives,\\n \\\"shared_random_pool\\\": config.hard_pool_size,\\n \\\"hard_loss_weight\\\": config.hard_loss_weight,\\n }),''')\\np.write_text(s)\\nPY\\ntail -3 /app/solution/pool4.log\",max_output_tokens:1000}));\n" } ### Tool output · 2026-09-23T05:58:27.731Z Script completed Wall time 0.2 seconds Output: {"chunk_id":"bef274","wall_time_seconds":0.00000304,"exit_code":0,"original_token_count":70,"output":"epoch=3/4 batches=175 targets=699,121 batch_bce=8.379889 running_bce=8.566248 elapsed=110.0s\nepoch=3/4 batches=200 targets=793,234 batch_bce=8.525497 running_bce=8.552622 elapsed=125.4s\nepoch=3/4 batches=225 targets=893,832 batch_bce=8.475566 running_bce=8.541288 elapsed=141.9s\n"} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "session_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "root_turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "response_id": "resp_021598e9bafc9da7016ab36a65455c87d2a6eac33797e9e9a9", "usage": { "input_tokens": 94468, "cached_input_tokens": 94080, "cache_write_input_tokens": 0, "output_tokens": 861, "reasoning_output_tokens": 516, "total_tokens": 95329 }, "turn_token_usage": { "input_tokens": 6447102, "cached_input_tokens": 6348160, "cache_write_input_tokens": 0, "output_tokens": 35934, "reasoning_output_tokens": 20647, "total_tokens": 6483036 }, "thread_token_usage": { "input_tokens": 6447102, "cached_input_tokens": 6348160, "cache_write_input_tokens": 0, "output_tokens": 35934, "reasoning_output_tokens": 20647, "total_tokens": 6483036 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-398dbefc-6284-4676-a617-9c447d523582", "process_id": "23725", "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()\ns=s.replace('\"baseline\": \"yambda-windowed-sequence-two-tower-hard-negatives\"', '\"baseline\": \"yambda-windowed-sequence-two-tower-negative-objective\"')\ns=s.replace('''\"negative_sampling\": {\n \"uniform\": config.uniform_negatives,\n \"frequency-corrected-inbatch\": config.inbatch_negatives,\n \"mined_hard\": config.hard_negatives,\n \"shared_random_pool\": config.hard_pool_size,\n \"hard_loss_weight\": config.hard_loss_weight,\n },''', '''\"negative_sampling\": ({\n \"proposal\": \"uniform-without-replacement-shared-pool\",\n \"pool_size\": config.hard_pool_size,\n \"partition_correction\": \"catalog_size/pool_size\",\n } if config.ranking_loss == \"pool-softmax\" else {\n \"uniform\": config.uniform_negatives,\n \"frequency-corrected-inbatch\": config.inbatch_negatives,\n \"mined_hard\": config.hard_negatives,\n \"shared_random_pool\": config.hard_pool_size,\n \"hard_loss_weight\": config.hard_loss_weight,\n }),''')\np.write_text(s)\nPY\ntail -3 /app/solution/pool4.log" ], "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()\ns=s.replace('\"baseline\": \"yambda-windowed-sequence-two-tower-hard-negatives\"', '\"baseline\": \"yambda-windowed-sequence-two-tower-negative-objective\"')\ns=s.replace('''\"negative_sampling\": {\n \"uniform\": config.uniform_negatives,\n \"frequency-corrected-inbatch\": config.inbatch_negatives,\n \"mined_hard\": config.hard_negatives,\n \"shared_random_pool\": config.hard_pool_size,\n \"hard_loss_weight\": config.hard_loss_weight,\n },''', '''\"negative_sampling\": ({\n \"proposal\": \"uniform-without-replacement-shared-pool\",\n \"pool_size\": config.hard_pool_size,\n \"partition_correction\": \"catalog_size/pool_size\",\n } if config.ranking_loss == \"pool-softmax\" else {\n \"uniform\": config.uniform_negatives,\n \"frequency-corrected-inbatch\": config.inbatch_negatives,\n \"mined_hard\": config.hard_negatives,\n \"shared_random_pool\": config.hard_pool_size,\n \"hard_loss_weight\": config.hard_loss_weight,\n }),''')\np.write_text(s)\nPY\ntail -3 /app/solution/pool4.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "epoch=3/4 batches=175 targets=699,121 batch_bce=8.379889 running_bce=8.566248 elapsed=110.0s\nepoch=3/4 batches=200 targets=793,234 batch_bce=8.525497 running_bce=8.552622 elapsed=125.4s\nepoch=3/4 batches=225 targets=893,832 batch_bce=8.475566 running_bce=8.541288 elapsed=141.9s\n", "stderr": "", "aggregated_output": "epoch=3/4 batches=175 targets=699,121 batch_bce=8.379889 running_bce=8.566248 elapsed=110.0s\nepoch=3/4 batches=200 targets=793,234 batch_bce=8.525497 running_bce=8.552622 elapsed=125.4s\nepoch=3/4 batches=225 targets=893,832 batch_bce=8.475566 running_bce=8.541288 elapsed=141.9s\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 3040 }, "formatted_output": "epoch=3/4 batches=175 targets=699,121 batch_bce=8.379889 running_bce=8.566248 elapsed=110.0s\nepoch=3/4 batches=200 targets=793,234 batch_bce=8.525497 running_bce=8.552622 elapsed=125.4s\nepoch=3/4 batches=225 targets=893,832 batch_bce=8.475566 running_bce=8.541288 elapsed=141.9s\n" } } ## Step 87 · agent · 2026-09-23T05:58:30.792Z ### Thinking unavailable — no text recorded The next shared-pool epoch is still reducing loss. I’m keeping this last check narrow: the same 4,096-item pool, tower architecture, target weights, and data split, with only the training duration extended. ### sleep · 2026-09-23T05:58:32.880Z { "duration_ms": 45000 } ### Tool output · 2026-09-23T05:59:17.889Z Wall time: 45.0050 seconds Sleep completed. ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "session_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "root_turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "response_id": "resp_021598e9bafc9da7016ab36a83eab087d292e2ef786b1f4329", "usage": { "input_tokens": 95493, "cached_input_tokens": 94336, "cache_write_input_tokens": 0, "output_tokens": 107, "reasoning_output_tokens": 34, "total_tokens": 95600 }, "turn_token_usage": { "input_tokens": 6542595, "cached_input_tokens": 6442496, "cache_write_input_tokens": 0, "output_tokens": 36041, "reasoning_output_tokens": 20681, "total_tokens": 6578636 }, "thread_token_usage": { "input_tokens": 6542595, "cached_input_tokens": 6442496, "cache_write_input_tokens": 0, "output_tokens": 36041, "reasoning_output_tokens": 20681, "total_tokens": 6578636 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "Extension", "kind": "clock.sleep", "id": "call_6UFKGExPp7yGTbPvkab00jXK", "durationMs": 45000 } } ### exec · 2026-09-23T05:59:21.763Z { "code": "text(await tools.exec_command({cmd:\"tail -5 /app/solution/pool4.log\",max_output_tokens:1000}));\ntext(await tools.clock__curr_time({}));\n" } ### Tool output · 2026-09-23T05:59:21.866Z Script completed Wall time 0.0 seconds Output: {"chunk_id":"ba9e11","wall_time_seconds":0.000005541,"exit_code":0,"original_token_count":127,"output":"epoch=3/4 batches=225 targets=893,832 batch_bce=8.475566 running_bce=8.541288 elapsed=141.9s\nepoch=3/4 batches=250 targets=995,037 batch_bce=8.357320 running_bce=8.536717 elapsed=158.3s\nsaved epoch checkpoint: /app/solution/pool4_ckpt/epoch-03.pt\nepoch=3/4 train_loss=8.53098 validation_loss=8.46458 pair_accuracy=0.882 train_targets=1,052,953 validation_targets=130,103 train_seconds=167.8 validation_seconds=12.7\nepoch=4/4 batches=25 targets=103,478 batch_bce=8.143313 running_bce=8.258439 elapsed=15.9s\n"} {"current_time":"2026-09-23 05:59:21 UTC"} ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-fa06031e-bbbd-4f64-82fa-aea01d6ae0a3", "process_id": "90849", "command": [ "/bin/sh", "-lc", "tail -5 /app/solution/pool4.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -5 /app/solution/pool4.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "epoch=3/4 batches=225 targets=893,832 batch_bce=8.475566 running_bce=8.541288 elapsed=141.9s\nepoch=3/4 batches=250 targets=995,037 batch_bce=8.357320 running_bce=8.536717 elapsed=158.3s\nsaved epoch checkpoint: /app/solution/pool4_ckpt/epoch-03.pt\nepoch=3/4 train_loss=8.53098 validation_loss=8.46458 pair_accuracy=0.882 train_targets=1,052,953 validation_targets=130,103 train_seconds=167.8 validation_seconds=12.7\nepoch=4/4 batches=25 targets=103,478 batch_bce=8.143313 running_bce=8.258439 elapsed=15.9s\n", "stderr": "", "aggregated_output": "epoch=3/4 batches=225 targets=893,832 batch_bce=8.475566 running_bce=8.541288 elapsed=141.9s\nepoch=3/4 batches=250 targets=995,037 batch_bce=8.357320 running_bce=8.536717 elapsed=158.3s\nsaved epoch checkpoint: /app/solution/pool4_ckpt/epoch-03.pt\nepoch=3/4 train_loss=8.53098 validation_loss=8.46458 pair_accuracy=0.882 train_targets=1,052,953 validation_targets=130,103 train_seconds=167.8 validation_seconds=12.7\nepoch=4/4 batches=25 targets=103,478 batch_bce=8.143313 running_bce=8.258439 elapsed=15.9s\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 5541 }, "formatted_output": "epoch=3/4 batches=225 targets=893,832 batch_bce=8.475566 running_bce=8.541288 elapsed=141.9s\nepoch=3/4 batches=250 targets=995,037 batch_bce=8.357320 running_bce=8.536717 elapsed=158.3s\nsaved epoch checkpoint: /app/solution/pool4_ckpt/epoch-03.pt\nepoch=3/4 train_loss=8.53098 validation_loss=8.46458 pair_accuracy=0.882 train_targets=1,052,953 validation_targets=130,103 train_seconds=167.8 validation_seconds=12.7\nepoch=4/4 batches=25 targets=103,478 batch_bce=8.143313 running_bce=8.258439 elapsed=15.9s\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "session_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "root_turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "response_id": "resp_021598e9bafc9da7016ab36ab6103c87d299f5b0c2f31573d9", "usage": { "input_tokens": 95623, "cached_input_tokens": 95360, "cache_write_input_tokens": 0, "output_tokens": 46, "reasoning_output_tokens": 0, "total_tokens": 95669 }, "turn_token_usage": { "input_tokens": 6638218, "cached_input_tokens": 6537856, "cache_write_input_tokens": 0, "output_tokens": 36087, "reasoning_output_tokens": 20681, "total_tokens": 6674305 }, "thread_token_usage": { "input_tokens": 6638218, "cached_input_tokens": 6537856, "cache_write_input_tokens": 0, "output_tokens": 36087, "reasoning_output_tokens": 20681, "total_tokens": 6674305 } } ## Step 88 · agent · 2026-09-23T05:59:41.245Z ### Thinking unavailable — no text recorded ## Step 89 · agent · 2026-09-23T05:59:57.463Z ### Thinking unavailable — no text recorded Epoch three improved chronological validation loss from 8.720 to 8.465. The fourth and final development epoch is now running. The fallback submission remains saved, and the remaining time is reserved for the selected model’s refit and final checks. ### sleep · 2026-09-23T05:59:59.069Z { "duration_ms": 45000 } ### Tool output · 2026-09-23T06:00:44.081Z Wall time: 45.0038 seconds Sleep completed. ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "session_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "root_turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "response_id": "resp_021598e9bafc9da7016ab36aba1ed487d28ac80356407d115f", "usage": { "input_tokens": 95925, "cached_input_tokens": 95488, "cache_write_input_tokens": 0, "output_tokens": 1112, "reasoning_output_tokens": 1034, "total_tokens": 97037 }, "turn_token_usage": { "input_tokens": 6734143, "cached_input_tokens": 6633344, "cache_write_input_tokens": 0, "output_tokens": 37199, "reasoning_output_tokens": 21715, "total_tokens": 6771342 }, "thread_token_usage": { "input_tokens": 6734143, "cached_input_tokens": 6633344, "cache_write_input_tokens": 0, "output_tokens": 37199, "reasoning_output_tokens": 21715, "total_tokens": 6771342 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "Extension", "kind": "clock.sleep", "id": "call_PeLM7aldSvEmoz4iZWRXfuOl", "durationMs": 45000 } } ### exec · 2026-09-23T06:00:51.148Z { "code": "text(await tools.exec_command({cmd:\"tail -4 /app/solution/pool4.log\",max_output_tokens:1000}));\ntext(await tools.clock__curr_time({}));\n" } ### Tool output · 2026-09-23T06:00:51.263Z Script completed Wall time 0.0 seconds Output: {"chunk_id":"37c0b2","wall_time_seconds":0.00000486,"exit_code":0,"original_token_count":93,"output":"epoch=4/4 batches=100 targets=401,871 batch_bce=8.307189 running_bce=8.255372 elapsed=61.9s\nepoch=4/4 batches=125 targets=498,293 batch_bce=8.209330 running_bce=8.239633 elapsed=76.8s\nepoch=4/4 batches=150 targets=598,326 batch_bce=8.248515 running_bce=8.242990 elapsed=92.2s\nepoch=4/4 batches=175 targets=692,313 batch_bce=7.694326 running_bce=8.250002 elapsed=106.7s\n"} {"current_time":"2026-09-23 06:00:51 UTC"} ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-cc68f559-d3da-4b8b-8667-71cf74f81c8f", "process_id": "76534", "command": [ "/bin/sh", "-lc", "tail -4 /app/solution/pool4.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -4 /app/solution/pool4.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "epoch=4/4 batches=100 targets=401,871 batch_bce=8.307189 running_bce=8.255372 elapsed=61.9s\nepoch=4/4 batches=125 targets=498,293 batch_bce=8.209330 running_bce=8.239633 elapsed=76.8s\nepoch=4/4 batches=150 targets=598,326 batch_bce=8.248515 running_bce=8.242990 elapsed=92.2s\nepoch=4/4 batches=175 targets=692,313 batch_bce=7.694326 running_bce=8.250002 elapsed=106.7s\n", "stderr": "", "aggregated_output": "epoch=4/4 batches=100 targets=401,871 batch_bce=8.307189 running_bce=8.255372 elapsed=61.9s\nepoch=4/4 batches=125 targets=498,293 batch_bce=8.209330 running_bce=8.239633 elapsed=76.8s\nepoch=4/4 batches=150 targets=598,326 batch_bce=8.248515 running_bce=8.242990 elapsed=92.2s\nepoch=4/4 batches=175 targets=692,313 batch_bce=7.694326 running_bce=8.250002 elapsed=106.7s\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4860 }, "formatted_output": "epoch=4/4 batches=100 targets=401,871 batch_bce=8.307189 running_bce=8.255372 elapsed=61.9s\nepoch=4/4 batches=125 targets=498,293 batch_bce=8.209330 running_bce=8.239633 elapsed=76.8s\nepoch=4/4 batches=150 targets=598,326 batch_bce=8.248515 running_bce=8.242990 elapsed=92.2s\nepoch=4/4 batches=175 targets=692,313 batch_bce=7.694326 running_bce=8.250002 elapsed=106.7s\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "session_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "root_turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "response_id": "resp_021598e9bafc9da7016ab36b0cef9887d2904dc8512d5640db", "usage": { "input_tokens": 97194, "cached_input_tokens": 56704, "cache_write_input_tokens": 0, "output_tokens": 46, "reasoning_output_tokens": 0, "total_tokens": 97240 }, "turn_token_usage": { "input_tokens": 6831337, "cached_input_tokens": 6690048, "cache_write_input_tokens": 0, "output_tokens": 37245, "reasoning_output_tokens": 21715, "total_tokens": 6868582 }, "thread_token_usage": { "input_tokens": 6831337, "cached_input_tokens": 6690048, "cache_write_input_tokens": 0, "output_tokens": 37245, "reasoning_output_tokens": 21715, "total_tokens": 6868582 } } ## Step 90 · agent · 2026-09-23T06:01:15.657Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T06:01:22.007Z { "code": "text(await tools.exec_command({cmd:\"cat > /app/solution/refit.sh <<'SH'\\n#!/bin/sh\\nset -eu\\nexport PYTHONDONTWRITEBYTECODE=1\\nexport OPENBLAS_NUM_THREADS=1\\nexport OMP_NUM_THREADS=4\\nPYTHON_BIN=/opt/task-venv/bin/python\\nSOLUTION_DIR=/app/solution\\n\\n# Production warm-up on the final observed history, including validation events.\\n\\\"$PYTHON_BIN\\\" \\\"$SOLUTION_DIR/train_production.py\\\" \\\\\\n --stage submission --public-dir /task --threads 4 \\\\\\n --prepared-cache \\\"$SOLUTION_DIR/submission_cache.npz\\\" \\\\\\n --checkpoint-dir \\\"$SOLUTION_DIR/production_submission_ckpt\\\" \\\\\\n --output \\\"$SOLUTION_DIR/production_submission.parquet\\\"\\n\\n# Continue the same towers with the selected shared-negative ranking objective.\\n\\\"$PYTHON_BIN\\\" \\\"$SOLUTION_DIR/train_current_model.py\\\" \\\\\\n --stage submission --public-dir /task --threads 4 \\\\\\n --prepared-cache \\\"$SOLUTION_DIR/submission_cache.npz\\\" \\\\\\n --resume-checkpoint \\\"$SOLUTION_DIR/production_submission_ckpt/best.pt\\\" \\\\\\n --checkpoint-dir \\\"$SOLUTION_DIR/final_ckpt\\\" \\\\\\n --epochs 4 --early-stopping-patience 2 \\\\\\n --ranking-loss pool-softmax --hard-negatives 0 --negative-pool-size 4096 \\\\\\n --loss-microbatch-size 4096 --output \\\"$SOLUTION_DIR/submission.parquet\\\"\\n\\n\\\"$PYTHON_BIN\\\" \\\"$SOLUTION_DIR/validate_submission.py\\\"\\nSH\\nchmod u+x /app/solution/refit.sh\\ntail -3 /app/solution/pool4.log\",max_output_tokens:1000}));\n" } ### Tool output · 2026-09-23T06:01:22.103Z Script completed Wall time 0.0 seconds Output: {"chunk_id":"61b12f","wall_time_seconds":0.00000344,"exit_code":0,"original_token_count":70,"output":"epoch=4/4 batches=175 targets=692,313 batch_bce=7.694326 running_bce=8.250002 elapsed=106.7s\nepoch=4/4 batches=200 targets=785,953 batch_bce=7.675469 running_bce=8.245387 elapsed=121.4s\nepoch=4/4 batches=225 targets=887,546 batch_bce=8.305934 running_bce=8.239229 elapsed=137.1s\n"} ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-732d55a3-7337-48ce-ae19-8ff2476b6ca0", "process_id": "77378", "command": [ "/bin/sh", "-lc", "cat > /app/solution/refit.sh <<'SH'\n#!/bin/sh\nset -eu\nexport PYTHONDONTWRITEBYTECODE=1\nexport OPENBLAS_NUM_THREADS=1\nexport OMP_NUM_THREADS=4\nPYTHON_BIN=/opt/task-venv/bin/python\nSOLUTION_DIR=/app/solution\n\n# Production warm-up on the final observed history, including validation events.\n\"$PYTHON_BIN\" \"$SOLUTION_DIR/train_production.py\" \\\n --stage submission --public-dir /task --threads 4 \\\n --prepared-cache \"$SOLUTION_DIR/submission_cache.npz\" \\\n --checkpoint-dir \"$SOLUTION_DIR/production_submission_ckpt\" \\\n --output \"$SOLUTION_DIR/production_submission.parquet\"\n\n# Continue the same towers with the selected shared-negative ranking objective.\n\"$PYTHON_BIN\" \"$SOLUTION_DIR/train_current_model.py\" \\\n --stage submission --public-dir /task --threads 4 \\\n --prepared-cache \"$SOLUTION_DIR/submission_cache.npz\" \\\n --resume-checkpoint \"$SOLUTION_DIR/production_submission_ckpt/best.pt\" \\\n --checkpoint-dir \"$SOLUTION_DIR/final_ckpt\" \\\n --epochs 4 --early-stopping-patience 2 \\\n --ranking-loss pool-softmax --hard-negatives 0 --negative-pool-size 4096 \\\n --loss-microbatch-size 4096 --output \"$SOLUTION_DIR/submission.parquet\"\n\n\"$PYTHON_BIN\" \"$SOLUTION_DIR/validate_submission.py\"\nSH\nchmod u+x /app/solution/refit.sh\ntail -3 /app/solution/pool4.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat > /app/solution/refit.sh <<'SH'\n#!/bin/sh\nset -eu\nexport PYTHONDONTWRITEBYTECODE=1\nexport OPENBLAS_NUM_THREADS=1\nexport OMP_NUM_THREADS=4\nPYTHON_BIN=/opt/task-venv/bin/python\nSOLUTION_DIR=/app/solution\n\n# Production warm-up on the final observed history, including validation events.\n\"$PYTHON_BIN\" \"$SOLUTION_DIR/train_production.py\" \\\n --stage submission --public-dir /task --threads 4 \\\n --prepared-cache \"$SOLUTION_DIR/submission_cache.npz\" \\\n --checkpoint-dir \"$SOLUTION_DIR/production_submission_ckpt\" \\\n --output \"$SOLUTION_DIR/production_submission.parquet\"\n\n# Continue the same towers with the selected shared-negative ranking objective.\n\"$PYTHON_BIN\" \"$SOLUTION_DIR/train_current_model.py\" \\\n --stage submission --public-dir /task --threads 4 \\\n --prepared-cache \"$SOLUTION_DIR/submission_cache.npz\" \\\n --resume-checkpoint \"$SOLUTION_DIR/production_submission_ckpt/best.pt\" \\\n --checkpoint-dir \"$SOLUTION_DIR/final_ckpt\" \\\n --epochs 4 --early-stopping-patience 2 \\\n --ranking-loss pool-softmax --hard-negatives 0 --negative-pool-size 4096 \\\n --loss-microbatch-size 4096 --output \"$SOLUTION_DIR/submission.parquet\"\n\n\"$PYTHON_BIN\" \"$SOLUTION_DIR/validate_submission.py\"\nSH\nchmod u+x /app/solution/refit.sh\ntail -3 /app/solution/pool4.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "epoch=4/4 batches=175 targets=692,313 batch_bce=7.694326 running_bce=8.250002 elapsed=106.7s\nepoch=4/4 batches=200 targets=785,953 batch_bce=7.675469 running_bce=8.245387 elapsed=121.4s\nepoch=4/4 batches=225 targets=887,546 batch_bce=8.305934 running_bce=8.239229 elapsed=137.1s\n", "stderr": "", "aggregated_output": "epoch=4/4 batches=175 targets=692,313 batch_bce=7.694326 running_bce=8.250002 elapsed=106.7s\nepoch=4/4 batches=200 targets=785,953 batch_bce=7.675469 running_bce=8.245387 elapsed=121.4s\nepoch=4/4 batches=225 targets=887,546 batch_bce=8.305934 running_bce=8.239229 elapsed=137.1s\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 3440 }, "formatted_output": "epoch=4/4 batches=175 targets=692,313 batch_bce=7.694326 running_bce=8.250002 elapsed=106.7s\nepoch=4/4 batches=200 targets=785,953 batch_bce=7.675469 running_bce=8.245387 elapsed=121.4s\nepoch=4/4 batches=225 targets=887,546 batch_bce=8.305934 running_bce=8.239229 elapsed=137.1s\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "session_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "root_turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "response_id": "resp_021598e9bafc9da7016ab36b1388c487d28a4258c6c5a38d53", "usage": { "input_tokens": 97462, "cached_input_tokens": 97024, "cache_write_input_tokens": 0, "output_tokens": 920, "reasoning_output_tokens": 516, "total_tokens": 98382 }, "turn_token_usage": { "input_tokens": 6928799, "cached_input_tokens": 6787072, "cache_write_input_tokens": 0, "output_tokens": 38165, "reasoning_output_tokens": 22231, "total_tokens": 6966964 }, "thread_token_usage": { "input_tokens": 6928799, "cached_input_tokens": 6787072, "cache_write_input_tokens": 0, "output_tokens": 38165, "reasoning_output_tokens": 22231, "total_tokens": 6966964 } } ## Step 91 · agent · 2026-09-23T06:01:44.867Z ### Thinking unavailable — no text recorded The final refit script is ready. It reproduces the production warm-up on `train_* + val_*`, then continues the same towers with shared-pool softmax. The improved trainer replaces `submission.parquet` atomically, so an interrupted write would leave the existing valid ranking intact. ### sleep · 2026-09-23T06:01:47.086Z { "duration_ms": 40000 } ### Tool output · 2026-09-23T06:02:27.094Z Wall time: 40.0036 seconds Sleep completed. ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "session_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "root_turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "response_id": "resp_021598e9bafc9da7016ab36b326dc887d29c216973c1c91979", "usage": { "input_tokens": 98548, "cached_input_tokens": 97280, "cache_write_input_tokens": 0, "output_tokens": 581, "reasoning_output_tokens": 494, "total_tokens": 99129 }, "turn_token_usage": { "input_tokens": 7027347, "cached_input_tokens": 6884352, "cache_write_input_tokens": 0, "output_tokens": 38746, "reasoning_output_tokens": 22725, "total_tokens": 7066093 }, "thread_token_usage": { "input_tokens": 7027347, "cached_input_tokens": 6884352, "cache_write_input_tokens": 0, "output_tokens": 38746, "reasoning_output_tokens": 22725, "total_tokens": 7066093 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "Extension", "kind": "clock.sleep", "id": "call_vhCugDYrs50i1piIFFpftYF9", "durationMs": 40000 } } ### exec · 2026-09-23T06:02:34.474Z { "code": "text(await tools.exec_command({cmd:\"tail -5 /app/solution/pool4.log\",max_output_tokens:1000}));\ntext(await tools.clock__curr_time({}));\n" } ### Tool output · 2026-09-23T06:02:34.594Z Script completed Wall time 0.0 seconds Output: {"chunk_id":"06f476","wall_time_seconds":0.00000459,"exit_code":0,"original_token_count":121,"output":"epoch=4/4 batches=225 targets=887,546 batch_bce=8.305934 running_bce=8.239229 elapsed=137.1s\nepoch=4/4 batches=250 targets=987,417 batch_bce=8.112208 running_bce=8.223544 elapsed=152.8s\nsaved epoch checkpoint: /app/solution/pool4_ckpt/epoch-04.pt\nepoch=4/4 train_loss=8.22158 validation_loss=8.17254 pair_accuracy=0.905 train_targets=1,052,953 validation_targets=130,103 train_seconds=162.9 validation_seconds=12.7\nrestored best validation-objective checkpoint: epoch=4 loss=8.172540\n"} {"current_time":"2026-09-23 06:02:34 UTC"} ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-9262d29d-85d7-4497-be4c-1c62fa50314d", "process_id": "70090", "command": [ "/bin/sh", "-lc", "tail -5 /app/solution/pool4.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -5 /app/solution/pool4.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "epoch=4/4 batches=225 targets=887,546 batch_bce=8.305934 running_bce=8.239229 elapsed=137.1s\nepoch=4/4 batches=250 targets=987,417 batch_bce=8.112208 running_bce=8.223544 elapsed=152.8s\nsaved epoch checkpoint: /app/solution/pool4_ckpt/epoch-04.pt\nepoch=4/4 train_loss=8.22158 validation_loss=8.17254 pair_accuracy=0.905 train_targets=1,052,953 validation_targets=130,103 train_seconds=162.9 validation_seconds=12.7\nrestored best validation-objective checkpoint: epoch=4 loss=8.172540\n", "stderr": "", "aggregated_output": "epoch=4/4 batches=225 targets=887,546 batch_bce=8.305934 running_bce=8.239229 elapsed=137.1s\nepoch=4/4 batches=250 targets=987,417 batch_bce=8.112208 running_bce=8.223544 elapsed=152.8s\nsaved epoch checkpoint: /app/solution/pool4_ckpt/epoch-04.pt\nepoch=4/4 train_loss=8.22158 validation_loss=8.17254 pair_accuracy=0.905 train_targets=1,052,953 validation_targets=130,103 train_seconds=162.9 validation_seconds=12.7\nrestored best validation-objective checkpoint: epoch=4 loss=8.172540\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4590 }, "formatted_output": "epoch=4/4 batches=225 targets=887,546 batch_bce=8.305934 running_bce=8.239229 elapsed=137.1s\nepoch=4/4 batches=250 targets=987,417 batch_bce=8.112208 running_bce=8.223544 elapsed=152.8s\nsaved epoch checkpoint: /app/solution/pool4_ckpt/epoch-04.pt\nepoch=4/4 train_loss=8.22158 validation_loss=8.17254 pair_accuracy=0.905 train_targets=1,052,953 validation_targets=130,103 train_seconds=162.9 validation_seconds=12.7\nrestored best validation-objective checkpoint: epoch=4 loss=8.172540\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "session_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "root_turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "response_id": "resp_021598e9bafc9da7016ab36b73443c87d29cd673aace61de95", "usage": { "input_tokens": 99152, "cached_input_tokens": 98432, "cache_write_input_tokens": 0, "output_tokens": 46, "reasoning_output_tokens": 0, "total_tokens": 99198 }, "turn_token_usage": { "input_tokens": 7126499, "cached_input_tokens": 6982784, "cache_write_input_tokens": 0, "output_tokens": 38792, "reasoning_output_tokens": 22725, "total_tokens": 7165291 }, "thread_token_usage": { "input_tokens": 7126499, "cached_input_tokens": 6982784, "cache_write_input_tokens": 0, "output_tokens": 38792, "reasoning_output_tokens": 22725, "total_tokens": 7165291 } } ## Step 92 · agent · 2026-09-23T06:02:59.385Z ### Thinking unavailable — no text recorded ## Step 93 · agent · 2026-09-23T06:03:11.232Z ### Thinking unavailable — no text recorded ### sleep · 2026-09-23T06:03:11.421Z { "duration_ms": 25000 } ### Tool output · 2026-09-23T06:03:36.429Z Wall time: 25.0034 seconds Sleep completed. ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "session_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "root_turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "response_id": "resp_021598e9bafc9da7016ab36b7ad6c487d2ab70f3508483865f", "usage": { "input_tokens": 99434, "cached_input_tokens": 98944, "cache_write_input_tokens": 0, "output_tokens": 858, "reasoning_output_tokens": 837, "total_tokens": 100292 }, "turn_token_usage": { "input_tokens": 7225933, "cached_input_tokens": 7081728, "cache_write_input_tokens": 0, "output_tokens": 39650, "reasoning_output_tokens": 23562, "total_tokens": 7265583 }, "thread_token_usage": { "input_tokens": 7225933, "cached_input_tokens": 7081728, "cache_write_input_tokens": 0, "output_tokens": 39650, "reasoning_output_tokens": 23562, "total_tokens": 7265583 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-3588bce3-8fc9-40c2-be11-a3e56c2b8bb0", "process_id": "72919", "command": [ "/bin/sh", "-lc", "OPENBLAS_NUM_THREADS=1 OMP_NUM_THREADS=4 PYTHONDONTWRITEBYTECODE=1 /opt/task-venv/bin/python /app/solution/train_current_model.py --stage validation --public-dir /task --output /app/solution/pool4_validation.parquet --prepared-cache /app/solution/validation_cache.npz --checkpoint-dir /app/solution/pool4_ckpt --resume-checkpoint /app/solution/pool_ckpt/best.pt --epochs 4 --early-stopping-patience 2 --threads 4 --loss-microbatch-size 4096 --hard-negatives 0 --negative-pool-size 4096 --ranking-loss pool-softmax > /app/solution/pool4.json 2> /app/solution/pool4.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "OPENBLAS_NUM_THREADS=1 OMP_NUM_THREADS=4 PYTHONDONTWRITEBYTECODE=1 /opt/task-venv/bin/python /app/solution/train_current_model.py --stage validation --public-dir /task --output /app/solution/pool4_validation.parquet --prepared-cache /app/solution/validation_cache.npz --checkpoint-dir /app/solution/pool4_ckpt --resume-checkpoint /app/solution/pool_ckpt/best.pt --epochs 4 --early-stopping-patience 2 --threads 4 --loss-microbatch-size 4096 --hard-negatives 0 --negative-pool-size 4096 --ranking-loss pool-softmax > /app/solution/pool4.json 2> /app/solution/pool4.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 442, "nanos": 66143584 }, "formatted_output": "" } } ### Harness context { "type": "codex_item_completed", "item": { "type": "Extension", "kind": "clock.sleep", "id": "call_Rl832t4QSt9Bv5WowGU28lLt", "durationMs": 25000 } } ### exec · 2026-09-23T06:03:39.933Z { "code": "text(await tools.exec_command({cmd:\"tail -3 /app/solution/pool4.log && ls -l /app/solution/pool4_validation.parquet\",max_output_tokens:1000}));\ntext(await tools.clock__curr_time({}));\n" } ### Tool output · 2026-09-23T06:03:40.033Z Script completed Wall time 0.0 seconds Output: {"chunk_id":"164fb7","wall_time_seconds":0.00000393,"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 1503903 Sep 23 06:03 /app/solution/pool4_validation.parquet\n"} {"current_time":"2026-09-23 06:03:39 UTC"} ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-5118550f-12ae-4a36-936e-47f852ee6ea7", "process_id": "19359", "command": [ "/bin/sh", "-lc", "tail -3 /app/solution/pool4.log && ls -l /app/solution/pool4_validation.parquet" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -3 /app/solution/pool4.log && ls -l /app/solution/pool4_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 1503903 Sep 23 06:03 /app/solution/pool4_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 1503903 Sep 23 06:03 /app/solution/pool4_validation.parquet\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 3930 }, "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 1503903 Sep 23 06:03 /app/solution/pool4_validation.parquet\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "session_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "root_turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "response_id": "resp_021598e9bafc9da7016ab36bb8972487d2a701cbd734d5c662", "usage": { "input_tokens": 100315, "cached_input_tokens": 99200, "cache_write_input_tokens": 0, "output_tokens": 60, "reasoning_output_tokens": 0, "total_tokens": 100375 }, "turn_token_usage": { "input_tokens": 7326248, "cached_input_tokens": 7180928, "cache_write_input_tokens": 0, "output_tokens": 39710, "reasoning_output_tokens": 23562, "total_tokens": 7365958 }, "thread_token_usage": { "input_tokens": 7326248, "cached_input_tokens": 7180928, "cache_write_input_tokens": 0, "output_tokens": 39710, "reasoning_output_tokens": 23562, "total_tokens": 7365958 } } ### exec · 2026-09-23T06:03:45.871Z { "code": "text(await tools.exec_command({cmd:\"PYTHONDONTWRITEBYTECODE=1 /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/pool4_validation.parquet > /app/solution/pool4_score.json && cat /app/solution/pool4_score.json\",yield_time_ms:1000,max_output_tokens:1000}));\n" } ### Tool output · 2026-09-23T06:03:47.037Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"04af3c","wall_time_seconds":1.001257402,"session_id":76066,"original_token_count":0,"output":""} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "session_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "root_turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "response_id": "resp_021598e9bafc9da7016ab36bbc410887d2a3812e292cae7b32", "usage": { "input_tokens": 100516, "cached_input_tokens": 100096, "cache_write_input_tokens": 0, "output_tokens": 86, "reasoning_output_tokens": 0, "total_tokens": 100602 }, "turn_token_usage": { "input_tokens": 7426764, "cached_input_tokens": 7281024, "cache_write_input_tokens": 0, "output_tokens": 39796, "reasoning_output_tokens": 23562, "total_tokens": 7466560 }, "thread_token_usage": { "input_tokens": 7426764, "cached_input_tokens": 7281024, "cache_write_input_tokens": 0, "output_tokens": 39796, "reasoning_output_tokens": 23562, "total_tokens": 7466560 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-255f1cb8-41f3-4272-85ae-76aaa6d901fc", "process_id": "76066", "command": [ "/bin/sh", "-lc", "PYTHONDONTWRITEBYTECODE=1 /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/pool4_validation.parquet > /app/solution/pool4_score.json && cat /app/solution/pool4_score.json" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "PYTHONDONTWRITEBYTECODE=1 /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/pool4_validation.parquet > /app/solution/pool4_score.json && cat /app/solution/pool4_score.json" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{\n \"model\": \"/app/solution/pool4_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.019783,\n \"quality_users\": 4263\n }\n}\n", "stderr": "", "aggregated_output": "{\n \"model\": \"/app/solution/pool4_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.019783,\n \"quality_users\": 4263\n }\n}\n", "exit_code": 0, "duration": { "secs": 3, "nanos": 212078844 }, "formatted_output": "{\n \"model\": \"/app/solution/pool4_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.019783,\n \"quality_users\": 4263\n }\n}\n" } } ### exec · 2026-09-23T06:03:50.820Z { "code": "text(await tools.write_stdin({session_id:76066,chars:\"\",yield_time_ms:1000,max_output_tokens:1000}));\n" } ### Tool output · 2026-09-23T06:03:50.909Z Script completed Wall time 0.0 seconds Output: {"chunk_id":"71704f","wall_time_seconds":0.00000204,"exit_code":0,"original_token_count":34,"output":"{\n \"model\": \"/app/solution/pool4_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.019783,\n \"quality_users\": 4263\n }\n}\n"} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "session_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "root_turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "response_id": "resp_021598e9bafc9da7016ab36bc33fbc87d294be8bb83202d4d3", "usage": { "input_tokens": 100660, "cached_input_tokens": 100352, "cache_write_input_tokens": 0, "output_tokens": 39, "reasoning_output_tokens": 0, "total_tokens": 100699 }, "turn_token_usage": { "input_tokens": 7527424, "cached_input_tokens": 7381376, "cache_write_input_tokens": 0, "output_tokens": 39835, "reasoning_output_tokens": 23562, "total_tokens": 7567259 }, "thread_token_usage": { "input_tokens": 7527424, "cached_input_tokens": 7381376, "cache_write_input_tokens": 0, "output_tokens": 39835, "reasoning_output_tokens": 23562, "total_tokens": 7567259 } } ## Step 94 · agent · 2026-09-23T06:04:00.950Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T06:04:07.823Z { "code": "text(await tools.exec_command({cmd:\"PYTHONDONTWRITEBYTECODE=1 /opt/task-venv/bin/python - <<'PY'\\nimport pandas as pd\\nc=pd.read_parquet('/task/data/candidate_catalog.parquet')[['item_id','popularity_rank']]\\nfor name in ['production','control','pool','pool4','hard']:\\n f=pd.read_parquet('/app/solution/'+name+'_validation.parquet')\\n t=f[f['rank']<=10].merge(c,on='item_id')\\n print(name, 'unique',t.item_id.nunique(),'median rank',t.popularity_rank.median(),'top1mode',f[f['rank']==1].item_id.value_counts().head(1).to_dict())\\nPY\",yield_time_ms:1000,max_output_tokens:1000}));\ntext(await tools.exec_command({cmd:\"OPENBLAS_NUM_THREADS=1 OMP_NUM_THREADS=4 PYTHONDONTWRITEBYTECODE=1 /opt/task-venv/bin/python /app/solution/rank_checkpoint.py --checkpoint /app/solution/pool4_ckpt/epoch-03.pt --stage validation --output /app/solution/pool3_validation.parquet --threads 4 > /app/solution/pool3_rank.json 2> /app/solution/pool3_rank.log\",yield_time_ms:1000,max_output_tokens:1000}));\n" } ### Tool output · 2026-09-23T06:04:10.149Z Script completed Wall time 2.3 seconds Output: {"chunk_id":"4c52c6","wall_time_seconds":1.000561177,"session_id":87216,"original_token_count":0,"output":""} {"chunk_id":"162f42","wall_time_seconds":1.001221393,"session_id":39292,"original_token_count":0,"output":""} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "session_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "root_turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "response_id": "resp_021598e9bafc9da7016ab36bc7153487d2935e796d44e6475e", "usage": { "input_tokens": 100808, "cached_input_tokens": 100480, "cache_write_input_tokens": 0, "output_tokens": 487, "reasoning_output_tokens": 183, "total_tokens": 101295 }, "turn_token_usage": { "input_tokens": 7628232, "cached_input_tokens": 7481856, "cache_write_input_tokens": 0, "output_tokens": 40322, "reasoning_output_tokens": 23745, "total_tokens": 7668554 }, "thread_token_usage": { "input_tokens": 7628232, "cached_input_tokens": 7481856, "cache_write_input_tokens": 0, "output_tokens": 40322, "reasoning_output_tokens": 23745, "total_tokens": 7668554 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-82901a69-52a0-4dae-96ae-922893e460df", "process_id": "87216", "command": [ "/bin/sh", "-lc", "PYTHONDONTWRITEBYTECODE=1 /opt/task-venv/bin/python - <<'PY'\nimport pandas as pd\nc=pd.read_parquet('/task/data/candidate_catalog.parquet')[['item_id','popularity_rank']]\nfor name in ['production','control','pool','pool4','hard']:\n f=pd.read_parquet('/app/solution/'+name+'_validation.parquet')\n t=f[f['rank']<=10].merge(c,on='item_id')\n print(name, 'unique',t.item_id.nunique(),'median rank',t.popularity_rank.median(),'top1mode',f[f['rank']==1].item_id.value_counts().head(1).to_dict())\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "PYTHONDONTWRITEBYTECODE=1 /opt/task-venv/bin/python - <<'PY'\nimport pandas as pd\nc=pd.read_parquet('/task/data/candidate_catalog.parquet')[['item_id','popularity_rank']]\nfor name in ['production','control','pool','pool4','hard']:\n f=pd.read_parquet('/app/solution/'+name+'_validation.parquet')\n t=f[f['rank']<=10].merge(c,on='item_id')\n print(name, 'unique',t.item_id.nunique(),'median rank',t.popularity_rank.median(),'top1mode',f[f['rank']==1].item_id.value_counts().head(1).to_dict())\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "production unique 592 median rank 111.0 top1mode {3542184: 2197}\ncontrol unique 989 median rank 208.0 top1mode {3542184: 1028}\npool unique 891 median rank 117.0 top1mode {3542184: 1773}\npool4 unique 2853 median rank 350.0 top1mode {5862961: 201}\nhard unique 3927 median rank 4080.0 top1mode {3251250: 484}\n", "stderr": "", "aggregated_output": "production unique 592 median rank 111.0 top1mode {3542184: 2197}\ncontrol unique 989 median rank 208.0 top1mode {3542184: 1028}\npool unique 891 median rank 117.0 top1mode {3542184: 1773}\npool4 unique 2853 median rank 350.0 top1mode {5862961: 201}\nhard unique 3927 median rank 4080.0 top1mode {3251250: 484}\n", "exit_code": 0, "duration": { "secs": 1, "nanos": 543746955 }, "formatted_output": "production unique 592 median rank 111.0 top1mode {3542184: 2197}\ncontrol unique 989 median rank 208.0 top1mode {3542184: 1028}\npool unique 891 median rank 117.0 top1mode {3542184: 1773}\npool4 unique 2853 median rank 350.0 top1mode {5862961: 201}\nhard unique 3927 median rank 4080.0 top1mode {3251250: 484}\n" } } ## Step 95 · agent · 2026-09-23T06:04:12.358Z ### Thinking unavailable — no text recorded The four-epoch model scored **0.019783**, below production, despite improving internal loss. I’m rejecting it and checking the saved third-epoch checkpoint before selecting the final refit. The existing submission remains intact. ## Step 96 · agent · 2026-09-23T06:04:29.314Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T06:04:29.991Z { "code": "text(await tools.write_stdin({session_id:87216,chars:\"\",yield_time_ms:1000,max_output_tokens:1000}));\ntext(await tools.clock__curr_time({}));\n" } ### Tool output · 2026-09-23T06:04:30.096Z Script completed Wall time 0.0 seconds Output: {"chunk_id":"931aae","wall_time_seconds":0.00000241,"exit_code":0,"original_token_count":77,"output":"production unique 592 median rank 111.0 top1mode {3542184: 2197}\ncontrol unique 989 median rank 208.0 top1mode {3542184: 1028}\npool unique 891 median rank 117.0 top1mode {3542184: 1773}\npool4 unique 2853 median rank 350.0 top1mode {5862961: 201}\nhard unique 3927 median rank 4080.0 top1mode {3251250: 484}\n"} {"current_time":"2026-09-23 06:04:30 UTC"} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "session_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "root_turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "response_id": "resp_021598e9bafc9da7016ab36bda637887d2806fdd3e22892e99", "usage": { "input_tokens": 101388, "cached_input_tokens": 100608, "cache_write_input_tokens": 0, "output_tokens": 434, "reasoning_output_tokens": 332, "total_tokens": 101822 }, "turn_token_usage": { "input_tokens": 7729620, "cached_input_tokens": 7582464, "cache_write_input_tokens": 0, "output_tokens": 40756, "reasoning_output_tokens": 24077, "total_tokens": 7770376 }, "thread_token_usage": { "input_tokens": 7729620, "cached_input_tokens": 7582464, "cache_write_input_tokens": 0, "output_tokens": 40756, "reasoning_output_tokens": 24077, "total_tokens": 7770376 } } ## Step 97 · agent · 2026-09-23T06:04:50.856Z ### Thinking unavailable — no text recorded ## Step 98 · agent · 2026-09-23T06:05:08.821Z ### Thinking unavailable — no text recorded ## Step 99 · agent · 2026-09-23T06:05:21.547Z ### Thinking unavailable — no text recorded ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-d70025b2-67dd-4eb7-a32d-00631d79d82b", "process_id": "39292", "command": [ "/bin/sh", "-lc", "OPENBLAS_NUM_THREADS=1 OMP_NUM_THREADS=4 PYTHONDONTWRITEBYTECODE=1 /opt/task-venv/bin/python /app/solution/rank_checkpoint.py --checkpoint /app/solution/pool4_ckpt/epoch-03.pt --stage validation --output /app/solution/pool3_validation.parquet --threads 4 > /app/solution/pool3_rank.json 2> /app/solution/pool3_rank.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "OPENBLAS_NUM_THREADS=1 OMP_NUM_THREADS=4 PYTHONDONTWRITEBYTECODE=1 /opt/task-venv/bin/python /app/solution/rank_checkpoint.py --checkpoint /app/solution/pool4_ckpt/epoch-03.pt --stage validation --output /app/solution/pool3_validation.parquet --threads 4 > /app/solution/pool3_rank.json 2> /app/solution/pool3_rank.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 81, "nanos": 230110765 }, "formatted_output": "" } } ### exec · 2026-09-23T06:05:37.189Z { "code": "text(await tools.exec_command({cmd:\"cat > /app/solution/calibrate_prior.py <<'PY'\\n\\\"\\\"\\\"Public-only calibration of an item-prior coordinate in the two-tower score.\\\"\\\"\\\"\\nimport sys\\nsys.path.insert(0, '/task')\\nimport json\\nfrom pathlib import Path\\nimport numpy as np\\nimport torch\\nimport pandas as pd\\nimport train_current_model as t\\nimport evaluate_public as e\\n\\ntorch.set_num_threads(4)\\ntorch.set_num_interop_threads(1)\\nroot = Path('/app/solution')\\npayload = torch.load(root/'pool4_ckpt/epoch-04.pt', map_location='cpu', weights_only=False)\\ncfg = t.Config(**payload['config'])\\ndata, eligible = t.load_or_prepare(Path('/task'), 'validation', cfg, root/'validation_cache.npz')\\nmodel = t.base.SequentialTwoTower(data.item_features, t.model_config(cfg))\\nmodel.load_state_dict(payload['model_state'])\\nmodel.eval()\\nwith torch.inference_mode():\\n queries = t.inference_queries(model, data, cfg, torch.device('cpu'))\\n items = torch.cat([model.encode_items(torch.arange(s, min(s+2048, len(data.item_ids)))) for s in range(0,len(data.item_ids),2048)])\\n prior = torch.from_numpy(data.item_features[:,1].copy())\\n targets = set(eligible.uids.astype(int))\\n truth = e.validation_truth(e.CandidateSet(e.DATA/'validation_eligible_candidates.npz'), targets)\\n result = {}\\n for alpha in [0.0, 0.25, 0.5, 1.0, 2.0]:\\n rows = []\\n ranked = {}\\n for start in range(0, len(eligible.uids),64):\\n end = min(start+64, len(eligible.uids))\\n scores = queries[start:end] @ items.T + alpha*prior[None,:]\\n mask = np.unpackbits(eligible.packed[start:end],axis=1,count=len(data.item_ids),bitorder='little').astype(bool)\\n scores.masked_fill_(~torch.from_numpy(mask), -torch.inf)\\n selected = scores.topk(100, dim=1).indices.numpy()\\n for r,cols in enumerate(selected):\\n uid = int(eligible.uids[start+r])\\n ids = data.item_ids[cols].astype(int).tolist()\\n ranked[uid] = ids\\n rows.extend((uid,item,rank) for rank,item in enumerate(ids,1))\\n path = root / f'pool4_prior_{alpha:g}_validation.parquet'\\n t.write_ranking(pd.DataFrame(rows,columns=['uid','item_id','rank']), path)\\n score = e.score(ranked,truth)\\n result[str(alpha)] = score\\n print(alpha,score,flush=True)\\n (root/'prior_scores.json').write_text(json.dumps(result,indent=2))\\nPY\\nOPENBLAS_NUM_THREADS=1 OMP_NUM_THREADS=4 PYTHONDONTWRITEBYTECODE=1 /opt/task-venv/bin/python /app/solution/calibrate_prior.py > /app/solution/prior.log 2> /app/solution/prior_progress.log\",yield_time_ms:1000,max_output_tokens:1000}));\n" } ### Tool output · 2026-09-23T06:05:38.356Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"c5eb85","wall_time_seconds":1.001212268,"session_id":50187,"original_token_count":0,"output":""} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "session_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "root_turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "response_id": "resp_021598e9bafc9da7016ab36bee5e3c87d285081a849372d7f4", "usage": { "input_tokens": 102011, "cached_input_tokens": 101248, "cache_write_input_tokens": 0, "output_tokens": 2102, "reasoning_output_tokens": 1357, "total_tokens": 104113 }, "turn_token_usage": { "input_tokens": 7831631, "cached_input_tokens": 7683712, "cache_write_input_tokens": 0, "output_tokens": 42858, "reasoning_output_tokens": 25434, "total_tokens": 7874489 }, "thread_token_usage": { "input_tokens": 7831631, "cached_input_tokens": 7683712, "cache_write_input_tokens": 0, "output_tokens": 42858, "reasoning_output_tokens": 25434, "total_tokens": 7874489 } } ## Step 100 · agent · 2026-09-23T06:05:43.957Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T06:05:56.244Z { "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(' hard_negatives: int = 8', ' retrieval_prior_weight: float = 0.0\\\\n hard_negatives: int = 8')\\ns=s.replace(''' items = torch.cat(item_parts)\\n rows: list[tuple[int, int, int]] = []''',''' items = torch.cat(item_parts)\\n if config.retrieval_prior_weight:\\n # One constant query coordinate and one offline item-prior coordinate:\\n # retrieval is still a single batchable dot product, with no reranker.\\n queries = torch.cat([queries, torch.ones(len(queries), 1)], dim=1)\\n prior = torch.from_numpy(prepared.item_features[:, 1].copy())\\n items = torch.cat([items, config.retrieval_prior_weight * prior[:, None]], dim=1)\\n rows: list[tuple[int, int, int]] = []''')\\ns=s.replace(''' parser.add_argument(\\\"--hard-negatives\\\", type=int, default=Config.hard_negatives)''',''' parser.add_argument(\\\"--retrieval-prior-weight\\\", type=float, default=Config.retrieval_prior_weight)\\n parser.add_argument(\\\"--hard-negatives\\\", type=int, default=Config.hard_negatives)''')\\ns=s.replace(''' hard_negatives=args.hard_negatives,''',''' retrieval_prior_weight=args.retrieval_prior_weight,\\n hard_negatives=args.hard_negatives,''')\\np.write_text(s)\\nPY\\ntail -3 /app/solution/pool3_rank.log && cat /app/solution/prior.log\",max_output_tokens:1000}));\ntext(await tools.clock__curr_time({}));\n" } ### Tool output · 2026-09-23T06:05:56.429Z Script completed Wall time 0.2 seconds Output: {"chunk_id":"2d3568","wall_time_seconds":0.000810946,"exit_code":0,"original_token_count":19,"output":"ranked users=7,168/8,456\nranked users=8,192/8,456\nranked users=8,456/8,456\n"} {"current_time":"2026-09-23 06:05:56 UTC"} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "session_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "root_turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "response_id": "resp_021598e9bafc9da7016ab36c3287c487d2bab924a15c27a270", "usage": { "input_tokens": 104171, "cached_input_tokens": 101888, "cache_write_input_tokens": 0, "output_tokens": 520, "reasoning_output_tokens": 96, "total_tokens": 104691 }, "turn_token_usage": { "input_tokens": 7935802, "cached_input_tokens": 7785600, "cache_write_input_tokens": 0, "output_tokens": 43378, "reasoning_output_tokens": 25530, "total_tokens": 7979180 }, "thread_token_usage": { "input_tokens": 7935802, "cached_input_tokens": 7785600, "cache_write_input_tokens": 0, "output_tokens": 43378, "reasoning_output_tokens": 25530, "total_tokens": 7979180 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-4ab8d73c-96c6-4132-8da4-e753ff4e8c59", "process_id": "91972", "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(' hard_negatives: int = 8', ' retrieval_prior_weight: float = 0.0\\n hard_negatives: int = 8')\ns=s.replace(''' items = torch.cat(item_parts)\n rows: list[tuple[int, int, int]] = []''',''' items = torch.cat(item_parts)\n if config.retrieval_prior_weight:\n # One constant query coordinate and one offline item-prior coordinate:\n # retrieval is still a single batchable dot product, with no reranker.\n queries = torch.cat([queries, torch.ones(len(queries), 1)], dim=1)\n prior = torch.from_numpy(prepared.item_features[:, 1].copy())\n items = torch.cat([items, config.retrieval_prior_weight * prior[:, None]], dim=1)\n rows: list[tuple[int, int, int]] = []''')\ns=s.replace(''' parser.add_argument(\"--hard-negatives\", type=int, default=Config.hard_negatives)''',''' parser.add_argument(\"--retrieval-prior-weight\", type=float, default=Config.retrieval_prior_weight)\n parser.add_argument(\"--hard-negatives\", type=int, default=Config.hard_negatives)''')\ns=s.replace(''' hard_negatives=args.hard_negatives,''',''' retrieval_prior_weight=args.retrieval_prior_weight,\n hard_negatives=args.hard_negatives,''')\np.write_text(s)\nPY\ntail -3 /app/solution/pool3_rank.log && cat /app/solution/prior.log" ], "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(' hard_negatives: int = 8', ' retrieval_prior_weight: float = 0.0\\n hard_negatives: int = 8')\ns=s.replace(''' items = torch.cat(item_parts)\n rows: list[tuple[int, int, int]] = []''',''' items = torch.cat(item_parts)\n if config.retrieval_prior_weight:\n # One constant query coordinate and one offline item-prior coordinate:\n # retrieval is still a single batchable dot product, with no reranker.\n queries = torch.cat([queries, torch.ones(len(queries), 1)], dim=1)\n prior = torch.from_numpy(prepared.item_features[:, 1].copy())\n items = torch.cat([items, config.retrieval_prior_weight * prior[:, None]], dim=1)\n rows: list[tuple[int, int, int]] = []''')\ns=s.replace(''' parser.add_argument(\"--hard-negatives\", type=int, default=Config.hard_negatives)''',''' parser.add_argument(\"--retrieval-prior-weight\", type=float, default=Config.retrieval_prior_weight)\n parser.add_argument(\"--hard-negatives\", type=int, default=Config.hard_negatives)''')\ns=s.replace(''' hard_negatives=args.hard_negatives,''',''' retrieval_prior_weight=args.retrieval_prior_weight,\n hard_negatives=args.hard_negatives,''')\np.write_text(s)\nPY\ntail -3 /app/solution/pool3_rank.log && cat /app/solution/prior.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 0, "nanos": 1041058 }, "formatted_output": "" } } ## Step 101 · agent · 2026-09-23T06:06:07.314Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T06:06:07.315Z { "code": "text(await tools.exec_command({cmd:\"PYTHONDONTWRITEBYTECODE=1 /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/pool3_validation.parquet > /app/solution/pool3_score.json && cat /app/solution/pool3_score.json\",yield_time_ms:1000,max_output_tokens:1000}));\n" } ### Tool output · 2026-09-23T06:06:08.484Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"be97f3","wall_time_seconds":1.000623883,"session_id":97287,"original_token_count":0,"output":""} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "session_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "root_turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "response_id": "resp_021598e9bafc9da7016ab36c4498d487d2bb56447d8d25a2be", "usage": { "input_tokens": 104801, "cached_input_tokens": 103936, "cache_write_input_tokens": 0, "output_tokens": 171, "reasoning_output_tokens": 83, "total_tokens": 104972 }, "turn_token_usage": { "input_tokens": 8040603, "cached_input_tokens": 7889536, "cache_write_input_tokens": 0, "output_tokens": 43549, "reasoning_output_tokens": 25613, "total_tokens": 8084152 }, "thread_token_usage": { "input_tokens": 8040603, "cached_input_tokens": 7889536, "cache_write_input_tokens": 0, "output_tokens": 43549, "reasoning_output_tokens": 25613, "total_tokens": 8084152 } } ## Step 102 · agent · 2026-09-23T06:06:10.848Z ### Thinking unavailable — no text recorded The ranking diagnostics show that the later model recommends much less popular tracks. I’m checking a small calibration of the catalog’s organic-popularity feature to see whether it corrects that shift. The calibration uses one extra item-vector coordinate and a constant query coordinate, preserving a single dot-product score. ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-54712c89-77a4-4e03-8c75-e8fa3ff53123", "process_id": "97287", "command": [ "/bin/sh", "-lc", "PYTHONDONTWRITEBYTECODE=1 /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/pool3_validation.parquet > /app/solution/pool3_score.json && cat /app/solution/pool3_score.json" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "PYTHONDONTWRITEBYTECODE=1 /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/pool3_validation.parquet > /app/solution/pool3_score.json && cat /app/solution/pool3_score.json" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{\n \"model\": \"/app/solution/pool3_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.022422,\n \"quality_users\": 4263\n }\n}\n", "stderr": "", "aggregated_output": "{\n \"model\": \"/app/solution/pool3_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.022422,\n \"quality_users\": 4263\n }\n}\n", "exit_code": 0, "duration": { "secs": 5, "nanos": 437153328 }, "formatted_output": "{\n \"model\": \"/app/solution/pool3_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.022422,\n \"quality_users\": 4263\n }\n}\n" } } ## Step 103 · agent · 2026-09-23T06:06:34.900Z ### Thinking unavailable — no text recorded ## Step 104 · agent · 2026-09-23T06:06:53.675Z ### Thinking unavailable — no text recorded ## Step 105 · agent · 2026-09-23T06:06:56.078Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T06:06:57.079Z { "code": "text(await tools.exec_command({cmd:\"cat /app/solution/prior.log && tail -3 /app/solution/prior_progress.log && cat /app/solution/pool3_score.json\",max_output_tokens:1200}));\ntext(await tools.clock__curr_time({}));\n" } ### Tool output · 2026-09-23T06:06:57.193Z Script completed Wall time 0.0 seconds Output: {"chunk_id":"5f4470","wall_time_seconds":0.00000508,"exit_code":0,"original_token_count":50,"output":"loaded full-sequence cache: tokens=20,056,329 targets=20,047,194\n{\n \"model\": \"/app/solution/pool3_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.022422,\n \"quality_users\": 4263\n }\n}\n"} {"current_time":"2026-09-23 06:06:57 UTC"} ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-74437750-4cd8-4ca5-bfa6-65a0464ad8a9", "process_id": "56101", "command": [ "/bin/sh", "-lc", "cat /app/solution/prior.log && tail -3 /app/solution/prior_progress.log && cat /app/solution/pool3_score.json" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat /app/solution/prior.log && tail -3 /app/solution/prior_progress.log && cat /app/solution/pool3_score.json" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "loaded full-sequence cache: tokens=20,056,329 targets=20,047,194\n{\n \"model\": \"/app/solution/pool3_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.022422,\n \"quality_users\": 4263\n }\n}\n", "stderr": "", "aggregated_output": "loaded full-sequence cache: tokens=20,056,329 targets=20,047,194\n{\n \"model\": \"/app/solution/pool3_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.022422,\n \"quality_users\": 4263\n }\n}\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 5080 }, "formatted_output": "loaded full-sequence cache: tokens=20,056,329 targets=20,047,194\n{\n \"model\": \"/app/solution/pool3_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.022422,\n \"quality_users\": 4263\n }\n}\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "session_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "root_turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "response_id": "resp_021598e9bafc9da7016ab36c50aa1887d2b9c39d830ccc3a48", "usage": { "input_tokens": 105030, "cached_input_tokens": 104576, "cache_write_input_tokens": 0, "output_tokens": 1244, "reasoning_output_tokens": 1111, "total_tokens": 106274 }, "turn_token_usage": { "input_tokens": 8145633, "cached_input_tokens": 7994112, "cache_write_input_tokens": 0, "output_tokens": 44793, "reasoning_output_tokens": 26724, "total_tokens": 8190426 }, "thread_token_usage": { "input_tokens": 8145633, "cached_input_tokens": 7994112, "cache_write_input_tokens": 0, "output_tokens": 44793, "reasoning_output_tokens": 26724, "total_tokens": 8190426 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-0d199447-bae7-4bbb-9572-5c4ed50fa4c6", "process_id": "50187", "command": [ "/bin/sh", "-lc", "cat > /app/solution/calibrate_prior.py <<'PY'\n\"\"\"Public-only calibration of an item-prior coordinate in the two-tower score.\"\"\"\nimport sys\nsys.path.insert(0, '/task')\nimport json\nfrom pathlib import Path\nimport numpy as np\nimport torch\nimport pandas as pd\nimport train_current_model as t\nimport evaluate_public as e\n\ntorch.set_num_threads(4)\ntorch.set_num_interop_threads(1)\nroot = Path('/app/solution')\npayload = torch.load(root/'pool4_ckpt/epoch-04.pt', map_location='cpu', weights_only=False)\ncfg = t.Config(**payload['config'])\ndata, eligible = t.load_or_prepare(Path('/task'), 'validation', cfg, root/'validation_cache.npz')\nmodel = t.base.SequentialTwoTower(data.item_features, t.model_config(cfg))\nmodel.load_state_dict(payload['model_state'])\nmodel.eval()\nwith torch.inference_mode():\n queries = t.inference_queries(model, data, cfg, torch.device('cpu'))\n items = torch.cat([model.encode_items(torch.arange(s, min(s+2048, len(data.item_ids)))) for s in range(0,len(data.item_ids),2048)])\n prior = torch.from_numpy(data.item_features[:,1].copy())\n targets = set(eligible.uids.astype(int))\n truth = e.validation_truth(e.CandidateSet(e.DATA/'validation_eligible_candidates.npz'), targets)\n result = {}\n for alpha in [0.0, 0.25, 0.5, 1.0, 2.0]:\n rows = []\n ranked = {}\n for start in range(0, len(eligible.uids),64):\n end = min(start+64, len(eligible.uids))\n scores = queries[start:end] @ items.T + alpha*prior[None,:]\n mask = np.unpackbits(eligible.packed[start:end],axis=1,count=len(data.item_ids),bitorder='little').astype(bool)\n scores.masked_fill_(~torch.from_numpy(mask), -torch.inf)\n selected = scores.topk(100, dim=1).indices.numpy()\n for r,cols in enumerate(selected):\n uid = int(eligible.uids[start+r])\n ids = data.item_ids[cols].astype(int).tolist()\n ranked[uid] = ids\n rows.extend((uid,item,rank) for rank,item in enumerate(ids,1))\n path = root / f'pool4_prior_{alpha:g}_validation.parquet'\n t.write_ranking(pd.DataFrame(rows,columns=['uid','item_id','rank']), path)\n score = e.score(ranked,truth)\n result[str(alpha)] = score\n print(alpha,score,flush=True)\n (root/'prior_scores.json').write_text(json.dumps(result,indent=2))\nPY\nOPENBLAS_NUM_THREADS=1 OMP_NUM_THREADS=4 PYTHONDONTWRITEBYTECODE=1 /opt/task-venv/bin/python /app/solution/calibrate_prior.py > /app/solution/prior.log 2> /app/solution/prior_progress.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat > /app/solution/calibrate_prior.py <<'PY'\n\"\"\"Public-only calibration of an item-prior coordinate in the two-tower score.\"\"\"\nimport sys\nsys.path.insert(0, '/task')\nimport json\nfrom pathlib import Path\nimport numpy as np\nimport torch\nimport pandas as pd\nimport train_current_model as t\nimport evaluate_public as e\n\ntorch.set_num_threads(4)\ntorch.set_num_interop_threads(1)\nroot = Path('/app/solution')\npayload = torch.load(root/'pool4_ckpt/epoch-04.pt', map_location='cpu', weights_only=False)\ncfg = t.Config(**payload['config'])\ndata, eligible = t.load_or_prepare(Path('/task'), 'validation', cfg, root/'validation_cache.npz')\nmodel = t.base.SequentialTwoTower(data.item_features, t.model_config(cfg))\nmodel.load_state_dict(payload['model_state'])\nmodel.eval()\nwith torch.inference_mode():\n queries = t.inference_queries(model, data, cfg, torch.device('cpu'))\n items = torch.cat([model.encode_items(torch.arange(s, min(s+2048, len(data.item_ids)))) for s in range(0,len(data.item_ids),2048)])\n prior = torch.from_numpy(data.item_features[:,1].copy())\n targets = set(eligible.uids.astype(int))\n truth = e.validation_truth(e.CandidateSet(e.DATA/'validation_eligible_candidates.npz'), targets)\n result = {}\n for alpha in [0.0, 0.25, 0.5, 1.0, 2.0]:\n rows = []\n ranked = {}\n for start in range(0, len(eligible.uids),64):\n end = min(start+64, len(eligible.uids))\n scores = queries[start:end] @ items.T + alpha*prior[None,:]\n mask = np.unpackbits(eligible.packed[start:end],axis=1,count=len(data.item_ids),bitorder='little').astype(bool)\n scores.masked_fill_(~torch.from_numpy(mask), -torch.inf)\n selected = scores.topk(100, dim=1).indices.numpy()\n for r,cols in enumerate(selected):\n uid = int(eligible.uids[start+r])\n ids = data.item_ids[cols].astype(int).tolist()\n ranked[uid] = ids\n rows.extend((uid,item,rank) for rank,item in enumerate(ids,1))\n path = root / f'pool4_prior_{alpha:g}_validation.parquet'\n t.write_ranking(pd.DataFrame(rows,columns=['uid','item_id','rank']), path)\n score = e.score(ranked,truth)\n result[str(alpha)] = score\n print(alpha,score,flush=True)\n (root/'prior_scores.json').write_text(json.dumps(result,indent=2))\nPY\nOPENBLAS_NUM_THREADS=1 OMP_NUM_THREADS=4 PYTHONDONTWRITEBYTECODE=1 /opt/task-venv/bin/python /app/solution/calibrate_prior.py > /app/solution/prior.log 2> /app/solution/prior_progress.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 89, "nanos": 219568004 }, "formatted_output": "" } } ## Step 106 · agent · 2026-09-23T06:07:15.608Z ### Thinking unavailable — no text recorded ### sleep · 2026-09-23T06:07:15.780Z { "duration_ms": 10000 } ### Tool output · 2026-09-23T06:07:25.787Z Wall time: 10.0036 seconds Sleep completed. ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "session_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "root_turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "response_id": "resp_021598e9bafc9da7016ab36c816e8487d2aae9ad1697cddf6e", "usage": { "input_tokens": 106423, "cached_input_tokens": 104832, "cache_write_input_tokens": 0, "output_tokens": 379, "reasoning_output_tokens": 358, "total_tokens": 106802 }, "turn_token_usage": { "input_tokens": 8252056, "cached_input_tokens": 8098944, "cache_write_input_tokens": 0, "output_tokens": 45172, "reasoning_output_tokens": 27082, "total_tokens": 8297228 }, "thread_token_usage": { "input_tokens": 8252056, "cached_input_tokens": 8098944, "cache_write_input_tokens": 0, "output_tokens": 45172, "reasoning_output_tokens": 27082, "total_tokens": 8297228 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "Extension", "kind": "clock.sleep", "id": "call_4vpyG0BjZh1crPD9SUXO3rv1", "durationMs": 10000 } } ### exec · 2026-09-23T06:07:29.141Z { "code": "text(await tools.exec_command({cmd:\"cat /app/solution/prior.log && tail -3 /app/solution/prior_progress.log\",max_output_tokens:1000}));\ntext(await tools.clock__curr_time({}));\n" } ### Tool output · 2026-09-23T06:07:29.269Z Script completed Wall time 0.1 seconds Output: {"chunk_id":"d823a7","wall_time_seconds":0.00000324,"exit_code":0,"original_token_count":88,"output":"0.0 {'quality_ndcg@10': 0.019783, 'quality_users': 4263}\n0.25 {'quality_ndcg@10': 0.019483, 'quality_users': 4263}\n0.5 {'quality_ndcg@10': 0.019655, 'quality_users': 4263}\n1.0 {'quality_ndcg@10': 0.0189, 'quality_users': 4263}\n2.0 {'quality_ndcg@10': 0.016206, 'quality_users': 4263}\nloaded full-sequence cache: tokens=20,056,329 targets=20,047,194\n"} {"current_time":"2026-09-23 06:07:29 UTC"} ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-c724bcde-bc1d-4fa2-afe6-244eabae94d3", "process_id": "54839", "command": [ "/bin/sh", "-lc", "cat /app/solution/prior.log && tail -3 /app/solution/prior_progress.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat /app/solution/prior.log && tail -3 /app/solution/prior_progress.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "0.0 {'quality_ndcg@10': 0.019783, 'quality_users': 4263}\n0.25 {'quality_ndcg@10': 0.019483, 'quality_users': 4263}\n0.5 {'quality_ndcg@10': 0.019655, 'quality_users': 4263}\n1.0 {'quality_ndcg@10': 0.0189, 'quality_users': 4263}\n2.0 {'quality_ndcg@10': 0.016206, 'quality_users': 4263}\nloaded full-sequence cache: tokens=20,056,329 targets=20,047,194\n", "stderr": "", "aggregated_output": "0.0 {'quality_ndcg@10': 0.019783, 'quality_users': 4263}\n0.25 {'quality_ndcg@10': 0.019483, 'quality_users': 4263}\n0.5 {'quality_ndcg@10': 0.019655, 'quality_users': 4263}\n1.0 {'quality_ndcg@10': 0.0189, 'quality_users': 4263}\n2.0 {'quality_ndcg@10': 0.016206, 'quality_users': 4263}\nloaded full-sequence cache: tokens=20,056,329 targets=20,047,194\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 3240 }, "formatted_output": "0.0 {'quality_ndcg@10': 0.019783, 'quality_users': 4263}\n0.25 {'quality_ndcg@10': 0.019483, 'quality_users': 4263}\n0.5 {'quality_ndcg@10': 0.019655, 'quality_users': 4263}\n1.0 {'quality_ndcg@10': 0.0189, 'quality_users': 4263}\n2.0 {'quality_ndcg@10': 0.016206, 'quality_users': 4263}\nloaded full-sequence cache: tokens=20,056,329 targets=20,047,194\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "session_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "root_turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "response_id": "resp_021598e9bafc9da7016ab36c9df74c87d28a1357b8939fddae", "usage": { "input_tokens": 106825, "cached_input_tokens": 106240, "cache_write_input_tokens": 0, "output_tokens": 55, "reasoning_output_tokens": 0, "total_tokens": 106880 }, "turn_token_usage": { "input_tokens": 8358881, "cached_input_tokens": 8205184, "cache_write_input_tokens": 0, "output_tokens": 45227, "reasoning_output_tokens": 27082, "total_tokens": 8404108 }, "thread_token_usage": { "input_tokens": 8358881, "cached_input_tokens": 8205184, "cache_write_input_tokens": 0, "output_tokens": 45227, "reasoning_output_tokens": 27082, "total_tokens": 8404108 } } ## Step 107 · agent · 2026-09-23T06:07:50.099Z ### Thinking unavailable — no text recorded ## Step 108 · agent · 2026-09-23T06:08:09.164Z ### Thinking unavailable — no text recorded ## Step 109 · agent · 2026-09-23T06:08:13.225Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T06:08:16.767Z { "code": "text(await tools.exec_command({cmd:\"OPENBLAS_NUM_THREADS=1 OMP_NUM_THREADS=4 PYTHONDONTWRITEBYTECODE=1 /opt/task-venv/bin/python /app/solution/train_current_model.py --stage validation --public-dir /task --output /app/solution/discovery_validation.parquet --prepared-cache /app/solution/validation_cache.npz --checkpoint-dir /app/solution/discovery_ckpt --resume-checkpoint /app/solution/baseline_ckpt/best.pt --epochs 2 --threads 4 --loss-microbatch-size 4096 --hard-negatives 0 --negative-pool-size 4096 --ranking-loss pool-softmax --target-weighting discovery-graded --ordinary-target-weight 0.1 --organic-like-target-weight 7 > /app/solution/discovery.json 2> /app/solution/discovery.log\",yield_time_ms:1000,max_output_tokens:1000}));\n" } ### Tool output · 2026-09-23T06:08:17.932Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"f93f1e","wall_time_seconds":1.000804481,"session_id":99850,"original_token_count":0,"output":""} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "session_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "root_turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "response_id": "resp_021598e9bafc9da7016ab36ca1c27487d29c7f373d61fc9635", "usage": { "input_tokens": 107100, "cached_input_tokens": 106624, "cache_write_input_tokens": 0, "output_tokens": 1347, "reasoning_output_tokens": 1140, "total_tokens": 108447 }, "turn_token_usage": { "input_tokens": 8465981, "cached_input_tokens": 8311808, "cache_write_input_tokens": 0, "output_tokens": 46574, "reasoning_output_tokens": 28222, "total_tokens": 8512555 }, "thread_token_usage": { "input_tokens": 8465981, "cached_input_tokens": 8311808, "cache_write_input_tokens": 0, "output_tokens": 46574, "reasoning_output_tokens": 28222, "total_tokens": 8512555 } } ## Step 110 · agent · 2026-09-23T06:08:24.225Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T06:08:25.241Z { "code": "text(await tools.clock__curr_time({}));\ntext(await tools.exec_command({cmd:\"tail -5 /app/solution/discovery.log\",max_output_tokens:1000}));\n" } ### Tool output · 2026-09-23T06:08:25.347Z Script completed Wall time 0.0 seconds Output: {"current_time":"2026-09-23 06:08:25 UTC"} {"chunk_id":"f01137","wall_time_seconds":0.0000047,"exit_code":0,"original_token_count":79,"output":"loaded full-sequence cache: tokens=20,056,329 targets=20,047,194\nchronological target 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\nloaded organic-like outcomes: rows=301,527 users=7,526\n"} ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-0715fa35-4695-49b0-a430-eeb33057d65c", "process_id": "25004", "command": [ "/bin/sh", "-lc", "tail -5 /app/solution/discovery.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -5 /app/solution/discovery.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "loaded full-sequence cache: tokens=20,056,329 targets=20,047,194\nchronological target 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\nloaded organic-like outcomes: rows=301,527 users=7,526\n", "stderr": "", "aggregated_output": "loaded full-sequence cache: tokens=20,056,329 targets=20,047,194\nchronological target 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\nloaded organic-like outcomes: rows=301,527 users=7,526\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4700 }, "formatted_output": "loaded full-sequence cache: tokens=20,056,329 targets=20,047,194\nchronological target 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\nloaded organic-like outcomes: rows=301,527 users=7,526\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "session_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "root_turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "response_id": "resp_021598e9bafc9da7016ab36cd228b887d2a73b9c62f09e8b7f", "usage": { "input_tokens": 108506, "cached_input_tokens": 106880, "cache_write_input_tokens": 0, "output_tokens": 63, "reasoning_output_tokens": 16, "total_tokens": 108569 }, "turn_token_usage": { "input_tokens": 8574487, "cached_input_tokens": 8418688, "cache_write_input_tokens": 0, "output_tokens": 46637, "reasoning_output_tokens": 28238, "total_tokens": 8621124 }, "thread_token_usage": { "input_tokens": 8574487, "cached_input_tokens": 8418688, "cache_write_input_tokens": 0, "output_tokens": 46637, "reasoning_output_tokens": 28238, "total_tokens": 8621124 } } ## Step 111 · agent · 2026-09-23T06:08:44.024Z ### Thinking unavailable — no text recorded The third checkpoint and popularity calibration did not beat the two-epoch control. I’m making one final bounded check: shared-pool softmax with the trainer’s existing discovery-graded weighting, which reduces repeat-listen weight and emphasizes novel tracks with organic likes. That targets a mismatch with serving eligibility, where previously encountered tracks are excluded. ## Step 112 · agent · 2026-09-23T06:09:04.459Z ### Thinking unavailable — no text recorded ## Step 113 · agent · 2026-09-23T06:09:20.084Z ### Thinking unavailable — no text recorded ### sleep · 2026-09-23T06:09:20.138Z { "duration_ms": 30000 } ### Tool output · 2026-09-23T06:09:50.146Z Wall time: 30.0044 seconds Sleep completed. ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "session_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "root_turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "response_id": "resp_021598e9bafc9da7016ab36cd99b1087d2b616fff6a403dba4", "usage": { "input_tokens": 108742, "cached_input_tokens": 108288, "cache_write_input_tokens": 0, "output_tokens": 1469, "reasoning_output_tokens": 1374, "total_tokens": 110211 }, "turn_token_usage": { "input_tokens": 8683229, "cached_input_tokens": 8526976, "cache_write_input_tokens": 0, "output_tokens": 48106, "reasoning_output_tokens": 29612, "total_tokens": 8731335 }, "thread_token_usage": { "input_tokens": 8683229, "cached_input_tokens": 8526976, "cache_write_input_tokens": 0, "output_tokens": 48106, "reasoning_output_tokens": 29612, "total_tokens": 8731335 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "Extension", "kind": "clock.sleep", "id": "call_RWkx9g7IVFK1f0mGd4TfzF7k", "durationMs": 30000 } } ### exec · 2026-09-23T06:09:54.889Z { "code": "text(await tools.exec_command({cmd:\"tail -7 /app/solution/discovery.log\",max_output_tokens:1400}));\ntext(await tools.clock__curr_time({}));\n" } ### Tool output · 2026-09-23T06:09:55.026Z Script completed Wall time 0.0 seconds Output: {"chunk_id":"dbff75","wall_time_seconds":0.000004391,"exit_code":0,"original_token_count":186,"output":"using a fresh AdamW optimizer after loading /app/solution/baseline_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 objective\nresumed validation-objective training: epoch=1 loss=9.224084\nepoch=2/2 batches=25 targets=104,067 batch_bce=9.223170 running_bce=9.109657 elapsed=15.8s\nepoch=2/2 batches=50 targets=206,457 batch_bce=8.945363 running_bce=9.079450 elapsed=31.2s\nepoch=2/2 batches=75 targets=305,335 batch_bce=9.211901 running_bce=9.092460 elapsed=46.4s\nepoch=2/2 batches=100 targets=402,892 batch_bce=8.837867 running_bce=9.071627 elapsed=61.5s\n"} {"current_time":"2026-09-23 06:09:54 UTC"} ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-3aee1ecb-70ec-4c56-bbab-faba85faa138", "process_id": "17856", "command": [ "/bin/sh", "-lc", "tail -7 /app/solution/discovery.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -7 /app/solution/discovery.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "using a fresh AdamW optimizer after loading /app/solution/baseline_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 objective\nresumed validation-objective training: epoch=1 loss=9.224084\nepoch=2/2 batches=25 targets=104,067 batch_bce=9.223170 running_bce=9.109657 elapsed=15.8s\nepoch=2/2 batches=50 targets=206,457 batch_bce=8.945363 running_bce=9.079450 elapsed=31.2s\nepoch=2/2 batches=75 targets=305,335 batch_bce=9.211901 running_bce=9.092460 elapsed=46.4s\nepoch=2/2 batches=100 targets=402,892 batch_bce=8.837867 running_bce=9.071627 elapsed=61.5s\n", "stderr": "", "aggregated_output": "using a fresh AdamW optimizer after loading /app/solution/baseline_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 objective\nresumed validation-objective training: epoch=1 loss=9.224084\nepoch=2/2 batches=25 targets=104,067 batch_bce=9.223170 running_bce=9.109657 elapsed=15.8s\nepoch=2/2 batches=50 targets=206,457 batch_bce=8.945363 running_bce=9.079450 elapsed=31.2s\nepoch=2/2 batches=75 targets=305,335 batch_bce=9.211901 running_bce=9.092460 elapsed=46.4s\nepoch=2/2 batches=100 targets=402,892 batch_bce=8.837867 running_bce=9.071627 elapsed=61.5s\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4391 }, "formatted_output": "using a fresh AdamW optimizer after loading /app/solution/baseline_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 objective\nresumed validation-objective training: epoch=1 loss=9.224084\nepoch=2/2 batches=25 targets=104,067 batch_bce=9.223170 running_bce=9.109657 elapsed=15.8s\nepoch=2/2 batches=50 targets=206,457 batch_bce=8.945363 running_bce=9.079450 elapsed=31.2s\nepoch=2/2 batches=75 targets=305,335 batch_bce=9.211901 running_bce=9.092460 elapsed=46.4s\nepoch=2/2 batches=100 targets=402,892 batch_bce=8.837867 running_bce=9.071627 elapsed=61.5s\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "session_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "root_turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "response_id": "resp_021598e9bafc9da7016ab36d2e58a087d2ab9f6b1f65274f45", "usage": { "input_tokens": 110234, "cached_input_tokens": 108544, "cache_write_input_tokens": 0, "output_tokens": 45, "reasoning_output_tokens": 0, "total_tokens": 110279 }, "turn_token_usage": { "input_tokens": 8793463, "cached_input_tokens": 8635520, "cache_write_input_tokens": 0, "output_tokens": 48151, "reasoning_output_tokens": 29612, "total_tokens": 8841614 }, "thread_token_usage": { "input_tokens": 8793463, "cached_input_tokens": 8635520, "cache_write_input_tokens": 0, "output_tokens": 48151, "reasoning_output_tokens": 29612, "total_tokens": 8841614 } } ## Step 114 · agent · 2026-09-23T06:10:04.216Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T06:10:04.218Z { "code": "text(await tools.exec_command({cmd:\"head -9 /app/solution/discovery.log && tail -2 /app/solution/discovery.log\",max_output_tokens:1000}));\n" } ### Tool output · 2026-09-23T06:10:04.327Z Script completed Wall time 0.1 seconds Output: {"chunk_id":"c60158","wall_time_seconds":0.000011031,"exit_code":0,"original_token_count":256,"output":"loaded full-sequence cache: tokens=20,056,329 targets=20,047,194\nchronological target 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\nloaded 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\nusing a fresh AdamW optimizer after loading /app/solution/baseline_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 objective\nepoch=2/2 batches=75 targets=305,335 batch_bce=9.211901 running_bce=9.092460 elapsed=46.4s\nepoch=2/2 batches=100 targets=402,892 batch_bce=8.837867 running_bce=9.071627 elapsed=61.5s\n"} ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-a1cf6bdf-5659-4d91-83d2-3856c93bb2a0", "process_id": "76348", "command": [ "/bin/sh", "-lc", "head -9 /app/solution/discovery.log && tail -2 /app/solution/discovery.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "head -9 /app/solution/discovery.log && tail -2 /app/solution/discovery.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "loaded full-sequence cache: tokens=20,056,329 targets=20,047,194\nchronological target 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\nloaded 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\nusing a fresh AdamW optimizer after loading /app/solution/baseline_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 objective\nepoch=2/2 batches=75 targets=305,335 batch_bce=9.211901 running_bce=9.092460 elapsed=46.4s\nepoch=2/2 batches=100 targets=402,892 batch_bce=8.837867 running_bce=9.071627 elapsed=61.5s\n", "stderr": "", "aggregated_output": "loaded full-sequence cache: tokens=20,056,329 targets=20,047,194\nchronological target 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\nloaded 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\nusing a fresh AdamW optimizer after loading /app/solution/baseline_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 objective\nepoch=2/2 batches=75 targets=305,335 batch_bce=9.211901 running_bce=9.092460 elapsed=46.4s\nepoch=2/2 batches=100 targets=402,892 batch_bce=8.837867 running_bce=9.071627 elapsed=61.5s\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 11031 }, "formatted_output": "loaded full-sequence cache: tokens=20,056,329 targets=20,047,194\nchronological target 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\nloaded 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\nusing a fresh AdamW optimizer after loading /app/solution/baseline_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 objective\nepoch=2/2 batches=75 targets=305,335 batch_bce=9.211901 running_bce=9.092460 elapsed=46.4s\nepoch=2/2 batches=100 targets=402,892 batch_bce=8.837867 running_bce=9.071627 elapsed=61.5s\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "session_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "root_turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "response_id": "resp_021598e9bafc9da7016ab36d33463487d29281d068302e1f23", "usage": { "input_tokens": 110596, "cached_input_tokens": 110080, "cache_write_input_tokens": 0, "output_tokens": 104, "reasoning_output_tokens": 55, "total_tokens": 110700 }, "turn_token_usage": { "input_tokens": 8904059, "cached_input_tokens": 8745600, "cache_write_input_tokens": 0, "output_tokens": 48255, "reasoning_output_tokens": 29667, "total_tokens": 8952314 }, "thread_token_usage": { "input_tokens": 8904059, "cached_input_tokens": 8745600, "cache_write_input_tokens": 0, "output_tokens": 48255, "reasoning_output_tokens": 29667, "total_tokens": 8952314 } } ## Step 115 · agent · 2026-09-23T06:10:24.310Z ### Thinking unavailable — no text recorded The final check is training normally. Its weights come only from observed fitting data: organic-like labels are bounded by the internal chronological split, and every adjacent target still has nonzero weight. I’ll select from the measured variants after this backtest and run the final refit. ### exec · 2026-09-23T06:10:38.605Z { "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()\\na=s.index('\\\"\\\"\\\"'); b=s.index('\\\"\\\"\\\"',a+3)+3\\ns=s[:a]+'''\\\"\\\"\\\"Train and rank the chronological music-discovery two-tower.\\n\\nThe selected negative objective uses a shared uniform catalog pool, masks known\\npositive items, and learns relative ranks with sampled softmax. Sampling and\\nloss changes preserve the production sequence encoder, independent item tower,\\nchronological partitions, cutoff histories, and platform candidate eligibility.\\nUse refit.sh to reproduce the validated production-warm-up/fine-tuning recipe.\\nOther objective modes remain available to reproduce the negative-mining ablation.\\n\\\"\\\"\\\"'''+s[b:]\\ns=s.replace(' loss_microbatch_size: int = 65_536',' loss_microbatch_size: int = 4_096')\\ns=s.replace(' hard_negatives: int = 8',' hard_negatives: int = 0')\\ns=s.replace(' hard_pool_size: int = 1024',' hard_pool_size: int = 4096')\\ns=s.replace(' ranking_loss: str = \\\"bce\\\"',' ranking_loss: str = \\\"pool-softmax\\\"')\\ns=s.replace('parser.add_argument(\\\"--threads\\\", type=int, default=8)', 'parser.add_argument(\\\"--threads\\\", type=int, default=4)')\\np.write_text(s)\\np=Path('/app/solution/test_hard_negatives.py')\\ns=p.read_text().replace('uniform_negatives=3, inbatch_negatives=2, hard_negatives=4,', 'uniform_negatives=3, inbatch_negatives=2, hard_negatives=4, ranking_loss=\\\"bce\\\",')\\np.write_text(s)\\np=Path('/app/solution/refit.sh'); p.write_text(p.read_text().replace('--epochs 4 --early-stopping-patience 2', '--epochs 2 --early-stopping-patience 2'))\\nPY\\nPYTHONDONTWRITEBYTECODE=1 /opt/task-venv/bin/python /app/solution/test_pool_objective.py && PYTHONDONTWRITEBYTECODE=1 /opt/task-venv/bin/python /app/solution/test_hard_negatives.py\",yield_time_ms:1000,max_output_tokens:1000}));\n" } ### Tool output · 2026-09-23T06:10:39.777Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"a601b9","wall_time_seconds":1.001159918,"session_id":96174,"original_token_count":0,"output":""} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "session_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "root_turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "response_id": "resp_021598e9bafc9da7016ab36d3c851087d28172b6a59c9f5300", "usage": { "input_tokens": 111078, "cached_input_tokens": 110464, "cache_write_input_tokens": 0, "output_tokens": 1066, "reasoning_output_tokens": 459, "total_tokens": 112144 }, "turn_token_usage": { "input_tokens": 9015137, "cached_input_tokens": 8856064, "cache_write_input_tokens": 0, "output_tokens": 49321, "reasoning_output_tokens": 30126, "total_tokens": 9064458 }, "thread_token_usage": { "input_tokens": 9015137, "cached_input_tokens": 8856064, "cache_write_input_tokens": 0, "output_tokens": 49321, "reasoning_output_tokens": 30126, "total_tokens": 9064458 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-42a17141-5631-4ead-a3db-e6a6d06437c8", "process_id": "96174", "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()\na=s.index('\"\"\"'); b=s.index('\"\"\"',a+3)+3\ns=s[:a]+'''\"\"\"Train and rank the chronological music-discovery two-tower.\n\nThe selected negative objective uses a shared uniform catalog pool, masks known\npositive items, and learns relative ranks with sampled softmax. Sampling and\nloss changes preserve the production sequence encoder, independent item tower,\nchronological partitions, cutoff histories, and platform candidate eligibility.\nUse refit.sh to reproduce the validated production-warm-up/fine-tuning recipe.\nOther objective modes remain available to reproduce the negative-mining ablation.\n\"\"\"'''+s[b:]\ns=s.replace(' loss_microbatch_size: int = 65_536',' loss_microbatch_size: int = 4_096')\ns=s.replace(' hard_negatives: int = 8',' hard_negatives: int = 0')\ns=s.replace(' hard_pool_size: int = 1024',' hard_pool_size: int = 4096')\ns=s.replace(' ranking_loss: str = \"bce\"',' ranking_loss: str = \"pool-softmax\"')\ns=s.replace('parser.add_argument(\"--threads\", type=int, default=8)', 'parser.add_argument(\"--threads\", type=int, default=4)')\np.write_text(s)\np=Path('/app/solution/test_hard_negatives.py')\ns=p.read_text().replace('uniform_negatives=3, inbatch_negatives=2, hard_negatives=4,', 'uniform_negatives=3, inbatch_negatives=2, hard_negatives=4, ranking_loss=\"bce\",')\np.write_text(s)\np=Path('/app/solution/refit.sh'); p.write_text(p.read_text().replace('--epochs 4 --early-stopping-patience 2', '--epochs 2 --early-stopping-patience 2'))\nPY\nPYTHONDONTWRITEBYTECODE=1 /opt/task-venv/bin/python /app/solution/test_pool_objective.py && PYTHONDONTWRITEBYTECODE=1 /opt/task-venv/bin/python /app/solution/test_hard_negatives.py" ], "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()\na=s.index('\"\"\"'); b=s.index('\"\"\"',a+3)+3\ns=s[:a]+'''\"\"\"Train and rank the chronological music-discovery two-tower.\n\nThe selected negative objective uses a shared uniform catalog pool, masks known\npositive items, and learns relative ranks with sampled softmax. Sampling and\nloss changes preserve the production sequence encoder, independent item tower,\nchronological partitions, cutoff histories, and platform candidate eligibility.\nUse refit.sh to reproduce the validated production-warm-up/fine-tuning recipe.\nOther objective modes remain available to reproduce the negative-mining ablation.\n\"\"\"'''+s[b:]\ns=s.replace(' loss_microbatch_size: int = 65_536',' loss_microbatch_size: int = 4_096')\ns=s.replace(' hard_negatives: int = 8',' hard_negatives: int = 0')\ns=s.replace(' hard_pool_size: int = 1024',' hard_pool_size: int = 4096')\ns=s.replace(' ranking_loss: str = \"bce\"',' ranking_loss: str = \"pool-softmax\"')\ns=s.replace('parser.add_argument(\"--threads\", type=int, default=8)', 'parser.add_argument(\"--threads\", type=int, default=4)')\np.write_text(s)\np=Path('/app/solution/test_hard_negatives.py')\ns=p.read_text().replace('uniform_negatives=3, inbatch_negatives=2, hard_negatives=4,', 'uniform_negatives=3, inbatch_negatives=2, hard_negatives=4, ranking_loss=\"bce\",')\np.write_text(s)\np=Path('/app/solution/refit.sh'); p.write_text(p.read_text().replace('--epochs 4 --early-stopping-patience 2', '--epochs 2 --early-stopping-patience 2'))\nPY\nPYTHONDONTWRITEBYTECODE=1 /opt/task-venv/bin/python /app/solution/test_pool_objective.py && PYTHONDONTWRITEBYTECODE=1 /opt/task-venv/bin/python /app/solution/test_hard_negatives.py" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "PASS: shared-pool loss equals the exact filtered full-catalog softmax\nPASS: pooled ranking objective produces finite, nonzero sequence gradients\nPASS: prefix positives, likes, target, duplicates excluded; finite nonzero gradients\n", "stderr": "", "aggregated_output": "PASS: shared-pool loss equals the exact filtered full-catalog softmax\nPASS: pooled ranking objective produces finite, nonzero sequence gradients\nPASS: prefix positives, likes, target, duplicates excluded; finite nonzero gradients\n", "exit_code": 0, "duration": { "secs": 9, "nanos": 962189010 }, "formatted_output": "PASS: shared-pool loss equals the exact filtered full-catalog softmax\nPASS: pooled ranking objective produces finite, nonzero sequence gradients\nPASS: prefix positives, likes, target, duplicates excluded; finite nonzero gradients\n" } } ## Step 116 · agent · 2026-09-23T06:10:50.813Z ### Thinking unavailable — no text recorded ### sleep · 2026-09-23T06:10:50.971Z { "duration_ms": 30000 } ### Tool output · 2026-09-23T06:11:20.979Z Wall time: 30.0038 seconds Sleep completed. ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "session_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "root_turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "response_id": "resp_021598e9bafc9da7016ab36d5ff85087d2b37c75ab9bde8bf3", "usage": { "input_tokens": 112202, "cached_input_tokens": 110848, "cache_write_input_tokens": 0, "output_tokens": 244, "reasoning_output_tokens": 223, "total_tokens": 112446 }, "turn_token_usage": { "input_tokens": 9127339, "cached_input_tokens": 8966912, "cache_write_input_tokens": 0, "output_tokens": 49565, "reasoning_output_tokens": 30349, "total_tokens": 9176904 }, "thread_token_usage": { "input_tokens": 9127339, "cached_input_tokens": 8966912, "cache_write_input_tokens": 0, "output_tokens": 49565, "reasoning_output_tokens": 30349, "total_tokens": 9176904 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "Extension", "kind": "clock.sleep", "id": "call_QGJphVda3zXwO74jbHuO0GS1", "durationMs": 30000 } } ### exec · 2026-09-23T06:11:25.361Z { "code": "text(await tools.exec_command({cmd:\"tail -4 /app/solution/discovery.log\",max_output_tokens:1000}));\ntext(await tools.write_stdin({session_id:96174,chars:\"\",yield_time_ms:1000,max_output_tokens:1000}));\ntext(await tools.clock__curr_time({}));\n" } ### Tool output · 2026-09-23T06:11:25.496Z Script completed Wall time 0.0 seconds Output: {"chunk_id":"0e6f37","wall_time_seconds":0.00000901,"exit_code":0,"original_token_count":93,"output":"epoch=2/2 batches=175 targets=696,141 batch_bce=9.131679 running_bce=9.013139 elapsed=107.6s\nepoch=2/2 batches=200 targets=790,005 batch_bce=8.822362 running_bce=8.994687 elapsed=123.7s\nepoch=2/2 batches=225 targets=884,435 batch_bce=8.975645 running_bce=8.988106 elapsed=138.6s\nepoch=2/2 batches=250 targets=987,417 batch_bce=9.360636 running_bce=8.979643 elapsed=154.9s\n"} {"chunk_id":"cc4591","wall_time_seconds":0.00000211,"exit_code":0,"original_token_count":58,"output":"PASS: shared-pool loss equals the exact filtered full-catalog softmax\nPASS: pooled ranking objective produces finite, nonzero sequence gradients\nPASS: prefix positives, likes, target, duplicates excluded; finite nonzero gradients\n"} {"current_time":"2026-09-23 06:11:25 UTC"} ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-0c9f248e-787c-491f-b4c5-08ad72991aee", "process_id": "19467", "command": [ "/bin/sh", "-lc", "tail -4 /app/solution/discovery.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -4 /app/solution/discovery.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "epoch=2/2 batches=175 targets=696,141 batch_bce=9.131679 running_bce=9.013139 elapsed=107.6s\nepoch=2/2 batches=200 targets=790,005 batch_bce=8.822362 running_bce=8.994687 elapsed=123.7s\nepoch=2/2 batches=225 targets=884,435 batch_bce=8.975645 running_bce=8.988106 elapsed=138.6s\nepoch=2/2 batches=250 targets=987,417 batch_bce=9.360636 running_bce=8.979643 elapsed=154.9s\n", "stderr": "", "aggregated_output": "epoch=2/2 batches=175 targets=696,141 batch_bce=9.131679 running_bce=9.013139 elapsed=107.6s\nepoch=2/2 batches=200 targets=790,005 batch_bce=8.822362 running_bce=8.994687 elapsed=123.7s\nepoch=2/2 batches=225 targets=884,435 batch_bce=8.975645 running_bce=8.988106 elapsed=138.6s\nepoch=2/2 batches=250 targets=987,417 batch_bce=9.360636 running_bce=8.979643 elapsed=154.9s\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 9010 }, "formatted_output": "epoch=2/2 batches=175 targets=696,141 batch_bce=9.131679 running_bce=9.013139 elapsed=107.6s\nepoch=2/2 batches=200 targets=790,005 batch_bce=8.822362 running_bce=8.994687 elapsed=123.7s\nepoch=2/2 batches=225 targets=884,435 batch_bce=8.975645 running_bce=8.988106 elapsed=138.6s\nepoch=2/2 batches=250 targets=987,417 batch_bce=9.360636 running_bce=8.979643 elapsed=154.9s\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "session_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "root_turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "response_id": "resp_021598e9bafc9da7016ab36d8923a087d2bd5693b2adf525bd", "usage": { "input_tokens": 112469, "cached_input_tokens": 112000, "cache_write_input_tokens": 0, "output_tokens": 74, "reasoning_output_tokens": 0, "total_tokens": 112543 }, "turn_token_usage": { "input_tokens": 9239808, "cached_input_tokens": 9078912, "cache_write_input_tokens": 0, "output_tokens": 49639, "reasoning_output_tokens": 30349, "total_tokens": 9289447 }, "thread_token_usage": { "input_tokens": 9239808, "cached_input_tokens": 9078912, "cache_write_input_tokens": 0, "output_tokens": 49639, "reasoning_output_tokens": 30349, "total_tokens": 9289447 } } ## Step 117 · agent · 2026-09-23T06:11:30.855Z ### Thinking unavailable — no text recorded Both objective tests pass: negative filtering and uniqueness are intact, the shared-pool loss matches the exact filtered softmax case, and gradients are finite. The last validation run is nearly finished training; then I’ll score its ranking and start the selected refit. ## Step 118 · agent · 2026-09-23T06:11:52.285Z ### Thinking unavailable — no text recorded ## Step 119 · agent · 2026-09-23T06:12:08.323Z ### Thinking unavailable — no text recorded ### sleep · 2026-09-23T06:12:08.634Z { "duration_ms": 35000 } ### Tool output · 2026-09-23T06:12:43.642Z Wall time: 35.0037 seconds Sleep completed. ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "session_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "root_turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "response_id": "resp_021598e9bafc9da7016ab36d8dd01487d2ab08aa78fe74303e", "usage": { "input_tokens": 112843, "cached_input_tokens": 112256, "cache_write_input_tokens": 0, "output_tokens": 1214, "reasoning_output_tokens": 1134, "total_tokens": 114057 }, "turn_token_usage": { "input_tokens": 9352651, "cached_input_tokens": 9191168, "cache_write_input_tokens": 0, "output_tokens": 50853, "reasoning_output_tokens": 31483, "total_tokens": 9403504 }, "thread_token_usage": { "input_tokens": 9352651, "cached_input_tokens": 9191168, "cache_write_input_tokens": 0, "output_tokens": 50853, "reasoning_output_tokens": 31483, "total_tokens": 9403504 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "Extension", "kind": "clock.sleep", "id": "call_xaOnWGrPd6YTw8pP8j4mWT0o", "durationMs": 35000 } } ### exec · 2026-09-23T06:12:46.457Z { "code": "text(await tools.exec_command({cmd:\"tail -5 /app/solution/discovery.log\",max_output_tokens:1000}));\ntext(await tools.clock__curr_time({}));\n" } ### Tool output · 2026-09-23T06:12:46.604Z Script completed Wall time 0.0 seconds Output: {"chunk_id":"9b5017","wall_time_seconds":0.00000708,"exit_code":0,"original_token_count":122,"output":"epoch=2/2 batches=225 targets=884,435 batch_bce=8.975645 running_bce=8.988106 elapsed=138.6s\nepoch=2/2 batches=250 targets=987,417 batch_bce=9.360636 running_bce=8.979643 elapsed=154.9s\nsaved epoch checkpoint: /app/solution/discovery_ckpt/epoch-02.pt\nepoch=2/2 train_loss=8.97751 validation_loss=8.94725 pair_accuracy=0.846 train_targets=1,052,953 validation_targets=130,103 train_seconds=165.1 validation_seconds=12.8\nrestored best validation-objective checkpoint: epoch=2 loss=8.947246\n"} {"current_time":"2026-09-23 06:12:46 UTC"} ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-12db831b-0958-484b-933c-b49b817c2399", "process_id": "5075", "command": [ "/bin/sh", "-lc", "tail -5 /app/solution/discovery.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -5 /app/solution/discovery.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "epoch=2/2 batches=225 targets=884,435 batch_bce=8.975645 running_bce=8.988106 elapsed=138.6s\nepoch=2/2 batches=250 targets=987,417 batch_bce=9.360636 running_bce=8.979643 elapsed=154.9s\nsaved epoch checkpoint: /app/solution/discovery_ckpt/epoch-02.pt\nepoch=2/2 train_loss=8.97751 validation_loss=8.94725 pair_accuracy=0.846 train_targets=1,052,953 validation_targets=130,103 train_seconds=165.1 validation_seconds=12.8\nrestored best validation-objective checkpoint: epoch=2 loss=8.947246\n", "stderr": "", "aggregated_output": "epoch=2/2 batches=225 targets=884,435 batch_bce=8.975645 running_bce=8.988106 elapsed=138.6s\nepoch=2/2 batches=250 targets=987,417 batch_bce=9.360636 running_bce=8.979643 elapsed=154.9s\nsaved epoch checkpoint: /app/solution/discovery_ckpt/epoch-02.pt\nepoch=2/2 train_loss=8.97751 validation_loss=8.94725 pair_accuracy=0.846 train_targets=1,052,953 validation_targets=130,103 train_seconds=165.1 validation_seconds=12.8\nrestored best validation-objective checkpoint: epoch=2 loss=8.947246\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 7080 }, "formatted_output": "epoch=2/2 batches=225 targets=884,435 batch_bce=8.975645 running_bce=8.988106 elapsed=138.6s\nepoch=2/2 batches=250 targets=987,417 batch_bce=9.360636 running_bce=8.979643 elapsed=154.9s\nsaved epoch checkpoint: /app/solution/discovery_ckpt/epoch-02.pt\nepoch=2/2 train_loss=8.97751 validation_loss=8.94725 pair_accuracy=0.846 train_targets=1,052,953 validation_targets=130,103 train_seconds=165.1 validation_seconds=12.8\nrestored best validation-objective checkpoint: epoch=2 loss=8.947246\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "session_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "root_turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "response_id": "resp_021598e9bafc9da7016ab36ddbd04887d2992648cbd25a2aa7", "usage": { "input_tokens": 114080, "cached_input_tokens": 112640, "cache_write_input_tokens": 0, "output_tokens": 45, "reasoning_output_tokens": 0, "total_tokens": 114125 }, "turn_token_usage": { "input_tokens": 9466731, "cached_input_tokens": 9303808, "cache_write_input_tokens": 0, "output_tokens": 50898, "reasoning_output_tokens": 31483, "total_tokens": 9517629 }, "thread_token_usage": { "input_tokens": 9466731, "cached_input_tokens": 9303808, "cache_write_input_tokens": 0, "output_tokens": 50898, "reasoning_output_tokens": 31483, "total_tokens": 9517629 } } ## Step 120 · agent · 2026-09-23T06:12:54.547Z ### Thinking unavailable — no text recorded The final discovery-weighted checkpoint has completed training and internal validation. Its public ranking is being generated now. I’m leaving the remaining time for the chosen model’s refit and submission checks. ### sleep · 2026-09-23T06:12:56.419Z { "duration_ms": 15000 } ### Tool output · 2026-09-23T06:13:11.427Z Wall time: 15.0032 seconds Sleep completed. ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "session_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "root_turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "response_id": "resp_021598e9bafc9da7016ab36dded2d887d2b46006ebc81a9de2", "usage": { "input_tokens": 114361, "cached_input_tokens": 113920, "cache_write_input_tokens": 0, "output_tokens": 145, "reasoning_output_tokens": 79, "total_tokens": 114506 }, "turn_token_usage": { "input_tokens": 9581092, "cached_input_tokens": 9417728, "cache_write_input_tokens": 0, "output_tokens": 51043, "reasoning_output_tokens": 31562, "total_tokens": 9632135 }, "thread_token_usage": { "input_tokens": 9581092, "cached_input_tokens": 9417728, "cache_write_input_tokens": 0, "output_tokens": 51043, "reasoning_output_tokens": 31562, "total_tokens": 9632135 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-3680d7c8-4c93-47b4-95e0-7bd5b364abcd", "process_id": "99850", "command": [ "/bin/sh", "-lc", "OPENBLAS_NUM_THREADS=1 OMP_NUM_THREADS=4 PYTHONDONTWRITEBYTECODE=1 /opt/task-venv/bin/python /app/solution/train_current_model.py --stage validation --public-dir /task --output /app/solution/discovery_validation.parquet --prepared-cache /app/solution/validation_cache.npz --checkpoint-dir /app/solution/discovery_ckpt --resume-checkpoint /app/solution/baseline_ckpt/best.pt --epochs 2 --threads 4 --loss-microbatch-size 4096 --hard-negatives 0 --negative-pool-size 4096 --ranking-loss pool-softmax --target-weighting discovery-graded --ordinary-target-weight 0.1 --organic-like-target-weight 7 > /app/solution/discovery.json 2> /app/solution/discovery.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "OPENBLAS_NUM_THREADS=1 OMP_NUM_THREADS=4 PYTHONDONTWRITEBYTECODE=1 /opt/task-venv/bin/python /app/solution/train_current_model.py --stage validation --public-dir /task --output /app/solution/discovery_validation.parquet --prepared-cache /app/solution/validation_cache.npz --checkpoint-dir /app/solution/discovery_ckpt --resume-checkpoint /app/solution/baseline_ckpt/best.pt --epochs 2 --threads 4 --loss-microbatch-size 4096 --hard-negatives 0 --negative-pool-size 4096 --ranking-loss pool-softmax --target-weighting discovery-graded --ordinary-target-weight 0.1 --organic-like-target-weight 7 > /app/solution/discovery.json 2> /app/solution/discovery.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 287, "nanos": 921032444 }, "formatted_output": "" } } ### Harness context { "type": "codex_item_completed", "item": { "type": "Extension", "kind": "clock.sleep", "id": "call_7byE3idJrN1KVq5hgIpWEqB0", "durationMs": 15000 } } ### exec · 2026-09-23T06:13:15.590Z { "code": "text(await tools.exec_command({cmd:\"tail -3 /app/solution/discovery.log && ls -l /app/solution/discovery_validation.parquet\",max_output_tokens:1000}));\ntext(await tools.clock__curr_time({}));\n" } ### Tool output · 2026-09-23T06:13:15.731Z Script completed Wall time 0.0 seconds Output: {"chunk_id":"e3cd65","wall_time_seconds":0.00000537,"exit_code":0,"original_token_count":41,"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 1262594 Sep 23 06:13 /app/solution/discovery_validation.parquet\n"} {"current_time":"2026-09-23 06:13:15 UTC"} ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-fca18945-bf93-49dd-8365-5b634985e99b", "process_id": "38860", "command": [ "/bin/sh", "-lc", "tail -3 /app/solution/discovery.log && ls -l /app/solution/discovery_validation.parquet" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -3 /app/solution/discovery.log && ls -l /app/solution/discovery_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 1262594 Sep 23 06:13 /app/solution/discovery_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 1262594 Sep 23 06:13 /app/solution/discovery_validation.parquet\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 5370 }, "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 1262594 Sep 23 06:13 /app/solution/discovery_validation.parquet\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "session_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "root_turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "response_id": "resp_021598e9bafc9da7016ab36df79b3087d2bcb9e519d2bdd633", "usage": { "input_tokens": 114529, "cached_input_tokens": 114176, "cache_write_input_tokens": 0, "output_tokens": 58, "reasoning_output_tokens": 0, "total_tokens": 114587 }, "turn_token_usage": { "input_tokens": 9695621, "cached_input_tokens": 9531904, "cache_write_input_tokens": 0, "output_tokens": 51101, "reasoning_output_tokens": 31562, "total_tokens": 9746722 }, "thread_token_usage": { "input_tokens": 9695621, "cached_input_tokens": 9531904, "cache_write_input_tokens": 0, "output_tokens": 51101, "reasoning_output_tokens": 31562, "total_tokens": 9746722 } } ### exec · 2026-09-23T06:13:19.953Z { "code": "text(await tools.exec_command({cmd:\"PYTHONDONTWRITEBYTECODE=1 /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/discovery_validation.parquet > /app/solution/discovery_score.json && cat /app/solution/discovery_score.json\",yield_time_ms:1000,max_output_tokens:1000}));\n" } ### Tool output · 2026-09-23T06:13:21.120Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"c8e1f9","wall_time_seconds":1.001725672,"session_id":28359,"original_token_count":0,"output":""} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "session_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "root_turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "response_id": "resp_021598e9bafc9da7016ab36dfbfc7487d2bd6a7cf0b1eee84e", "usage": { "input_tokens": 114728, "cached_input_tokens": 114304, "cache_write_input_tokens": 0, "output_tokens": 83, "reasoning_output_tokens": 0, "total_tokens": 114811 }, "turn_token_usage": { "input_tokens": 9810349, "cached_input_tokens": 9646208, "cache_write_input_tokens": 0, "output_tokens": 51184, "reasoning_output_tokens": 31562, "total_tokens": 9861533 }, "thread_token_usage": { "input_tokens": 9810349, "cached_input_tokens": 9646208, "cache_write_input_tokens": 0, "output_tokens": 51184, "reasoning_output_tokens": 31562, "total_tokens": 9861533 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-4ec80270-a019-4770-842f-06a48137a555", "process_id": "28359", "command": [ "/bin/sh", "-lc", "PYTHONDONTWRITEBYTECODE=1 /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/discovery_validation.parquet > /app/solution/discovery_score.json && cat /app/solution/discovery_score.json" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "PYTHONDONTWRITEBYTECODE=1 /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/discovery_validation.parquet > /app/solution/discovery_score.json && cat /app/solution/discovery_score.json" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{\n \"model\": \"/app/solution/discovery_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.020892,\n \"quality_users\": 4263\n }\n}\n", "stderr": "", "aggregated_output": "{\n \"model\": \"/app/solution/discovery_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.020892,\n \"quality_users\": 4263\n }\n}\n", "exit_code": 0, "duration": { "secs": 3, "nanos": 196574011 }, "formatted_output": "{\n \"model\": \"/app/solution/discovery_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.020892,\n \"quality_users\": 4263\n }\n}\n" } } ### exec · 2026-09-23T06:13:24.336Z { "code": "text(await tools.write_stdin({session_id:28359,chars:\"\",yield_time_ms:1000,max_output_tokens:1000}));\n" } ### Tool output · 2026-09-23T06:13:24.443Z Script completed Wall time 0.0 seconds Output: {"chunk_id":"d5f586","wall_time_seconds":0.00000195,"exit_code":0,"original_token_count":35,"output":"{\n \"model\": \"/app/solution/discovery_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.020892,\n \"quality_users\": 4263\n }\n}\n"} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "session_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "root_turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "response_id": "resp_021598e9bafc9da7016ab36e0147b887d2a998b475c2344168", "usage": { "input_tokens": 114871, "cached_input_tokens": 114560, "cache_write_input_tokens": 0, "output_tokens": 39, "reasoning_output_tokens": 0, "total_tokens": 114910 }, "turn_token_usage": { "input_tokens": 9925220, "cached_input_tokens": 9760768, "cache_write_input_tokens": 0, "output_tokens": 51223, "reasoning_output_tokens": 31562, "total_tokens": 9976443 }, "thread_token_usage": { "input_tokens": 9925220, "cached_input_tokens": 9760768, "cache_write_input_tokens": 0, "output_tokens": 51223, "reasoning_output_tokens": 31562, "total_tokens": 9976443 } } ## Step 121 · agent · 2026-09-23T06:13:31.711Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T06:13:37.132Z { "code": "text(await tools.exec_command({cmd:\"OPENBLAS_NUM_THREADS=1 OMP_NUM_THREADS=4 PYTHONDONTWRITEBYTECODE=1 /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_submission_ckpt/best.pt --epochs 2 --early-stopping-patience 2 --threads 4 --loss-microbatch-size 4096 --hard-negatives 0 --negative-pool-size 4096 --ranking-loss pool-softmax > /app/solution/final.json 2> /app/solution/final.log\",yield_time_ms:1000,max_output_tokens:1000}));\n" } ### Tool output · 2026-09-23T06:13:38.297Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"d2967c","wall_time_seconds":1.001085751,"session_id":41794,"original_token_count":0,"output":""} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "session_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "root_turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "response_id": "resp_021598e9bafc9da7016ab36e04a79487d28cd4952dcab6d69a", "usage": { "input_tokens": 115019, "cached_input_tokens": 114688, "cache_write_input_tokens": 0, "output_tokens": 366, "reasoning_output_tokens": 173, "total_tokens": 115385 }, "turn_token_usage": { "input_tokens": 10040239, "cached_input_tokens": 9875456, "cache_write_input_tokens": 0, "output_tokens": 51589, "reasoning_output_tokens": 31735, "total_tokens": 10091828 }, "thread_token_usage": { "input_tokens": 10040239, "cached_input_tokens": 9875456, "cache_write_input_tokens": 0, "output_tokens": 51589, "reasoning_output_tokens": 31735, "total_tokens": 10091828 } } ## Step 122 · agent · 2026-09-23T06:13:58.164Z ### Thinking unavailable — no text recorded The discovery-weighted variant scored 0.020892, so I’m rejecting it. I’ve selected the two-epoch shared-pool recipe: it scored **0.022759 versus production’s 0.021280**, with a faster fine-tuning epoch than the original objective. The two-epoch control scored slightly higher at 0.022962, so this is an efficiency tradeoff, not a decisive quality win over that control. The final refit is now running. ### exec · 2026-09-23T06:14:27.225Z { "code": "text(await tools.exec_command({cmd:\"cat > /app/solution/README.md <<'MD'\\n# Music-discovery two-tower\\n\\nThe selected recipe uses one production BCE warm-up epoch followed by one\\nshared-pool sampled-softmax epoch. `submission.parquet` is the final-cutoff\\nranking, refitted on `train_* + val_*`.\\n\\n## Diagnosis and change\\n\\nThe original loss averages 24 uniform and eight frequency-corrected in-batch\\nnegatives. Most uniform draws are easy; the small in-batch component further\\ndownweights frequent competitors. Its frequency weights are only a heuristic:\\nunique-item sampling and rejection change the actual proposal distribution.\\nThe baseline puts the same track first for 2,197 of 8,456 users.\\n\\nThe selected objective samples 4,096 unique catalog items uniformly per block,\\nshares their encodings, and computes a matrix of user–item scores. It masks the\\ncomplete training-prefix positive set, active likes, and current target. A\\nsampled-softmax loss emphasizes high-scoring competitors smoothly; multiplying\\nthe sampled negative partition by catalog-size/pool-size estimates its full\\ncatalog mass. Existing organic-strong target weights remain unchanged.\\n\\nExplicit top-eight hard mining with 50% negative-loss weight failed badly. More\\nsoftmax epochs, discovery-graded target weighting, and catalog-prior calibration\\nalso failed to improve the selected public score. None is used in the submission.\\n\\n## Public backtest\\n\\nAll fitting and features for these rankings use only `train_*`. Scores are the\\nprovided evaluator's graded NDCG@10 over 4,263 relevant users.\\n\\n| Recipe | Total epochs | NDCG@10 |\\n|---|---:|---:|\\n| Production | 1 | 0.021280 |\\n| Original-objective control | 2 | 0.022962 |\\n| Hard-negative BCE | 2 | 0.002557 |\\n| **Selected shared-pool softmax** | **2** | **0.022759** |\\n| Shared-pool softmax | 3 | 0.022422 |\\n| Shared-pool softmax | 4 | 0.019783 |\\n| Shared-pool softmax + discovery weighting | 2 | 0.020892 |\\n\\nSelected improvement over production: **6.95% relative**. The original two-epoch\\ncontrol has a slightly higher point estimate. We selected the shared-pool\\nobjective for comparable retrieval with a faster extra epoch (166 versus 221\\nseconds on four CPU threads). The paired bootstrap interval against production\\nincludes zero; this public improvement is modest and does not establish online\\nlift or a hidden-test result. See `comparison_initial.json` for uncertainty.\\n\\n## Reproduction and boundaries\\n\\nRun `sh /app/solution/refit.sh`. It reproduces the warm-up with\\n`train_production.py`, then calls `train_current_model.py --stage submission\\n--epochs 2 --ranking-loss pool-softmax --hard-negatives 0\\n--negative-pool-size 4096 --loss-microbatch-size 4096`, resuming the warm-up.\\nThe selected checkpoint uses the original chronological internal split, with the\\nconfigured objective evaluated using fixed sampling seeds.\\n\\nThe GRU, item tower, 192-dimensional embeddings, raw dot-product serving,\\ncutoffs, target users, and platform eligibility masks are unchanged. There is no\\nuser-ID embedding, cross-encoder, reranker, or selected popularity correction.\\nThe final item vectors remain independently precomputable. Organic outcomes are\\nan observational offline target, not propensity-corrected causal lift.\\n\\n`test_pool_objective.py` checks the exact full-catalog loss limit and finite\\nnonzero gradients. `test_hard_negatives.py` checks filtering and duplicate\\nexclusion for the rejected ablation. `validate_submission.py` verifies the final\\nfile, integer columns, exact users, 100 distinct items and ranks per user, and\\ntest-candidate eligibility. Ranking writes are atomic.\\nMD\\nPYTHONDONTWRITEBYTECODE=1 /opt/task-venv/bin/python /app/solution/compare_public.py /app/solution/control_validation.parquet /app/solution/pool_validation.parquet > /app/solution/comparison_control.json\",yield_time_ms:1000,max_output_tokens:1000}));\n" } ### Tool output · 2026-09-23T06:14:28.397Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"d97f3e","wall_time_seconds":1.001295804,"session_id":34017,"original_token_count":0,"output":""} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "session_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "root_turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "response_id": "resp_021598e9bafc9da7016ab36e129ad887d28a51381a092a6fd6", "usage": { "input_tokens": 115443, "cached_input_tokens": 114816, "cache_write_input_tokens": 0, "output_tokens": 1563, "reasoning_output_tokens": 516, "total_tokens": 117006 }, "turn_token_usage": { "input_tokens": 10155682, "cached_input_tokens": 9990272, "cache_write_input_tokens": 0, "output_tokens": 53152, "reasoning_output_tokens": 32251, "total_tokens": 10208834 }, "thread_token_usage": { "input_tokens": 10155682, "cached_input_tokens": 9990272, "cache_write_input_tokens": 0, "output_tokens": 53152, "reasoning_output_tokens": 32251, "total_tokens": 10208834 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-a4b73d38-74bc-4743-a7de-c6f3ab743478", "process_id": "34017", "command": [ "/bin/sh", "-lc", "cat > /app/solution/README.md <<'MD'\n# Music-discovery two-tower\n\nThe selected recipe uses one production BCE warm-up epoch followed by one\nshared-pool sampled-softmax epoch. `submission.parquet` is the final-cutoff\nranking, refitted on `train_* + val_*`.\n\n## Diagnosis and change\n\nThe original loss averages 24 uniform and eight frequency-corrected in-batch\nnegatives. Most uniform draws are easy; the small in-batch component further\ndownweights frequent competitors. Its frequency weights are only a heuristic:\nunique-item sampling and rejection change the actual proposal distribution.\nThe baseline puts the same track first for 2,197 of 8,456 users.\n\nThe selected objective samples 4,096 unique catalog items uniformly per block,\nshares their encodings, and computes a matrix of user–item scores. It masks the\ncomplete training-prefix positive set, active likes, and current target. A\nsampled-softmax loss emphasizes high-scoring competitors smoothly; multiplying\nthe sampled negative partition by catalog-size/pool-size estimates its full\ncatalog mass. Existing organic-strong target weights remain unchanged.\n\nExplicit top-eight hard mining with 50% negative-loss weight failed badly. More\nsoftmax epochs, discovery-graded target weighting, and catalog-prior calibration\nalso failed to improve the selected public score. None is used in the submission.\n\n## Public backtest\n\nAll fitting and features for these rankings use only `train_*`. Scores are the\nprovided evaluator's graded NDCG@10 over 4,263 relevant users.\n\n| Recipe | Total epochs | NDCG@10 |\n|---|---:|---:|\n| Production | 1 | 0.021280 |\n| Original-objective control | 2 | 0.022962 |\n| Hard-negative BCE | 2 | 0.002557 |\n| **Selected shared-pool softmax** | **2** | **0.022759** |\n| Shared-pool softmax | 3 | 0.022422 |\n| Shared-pool softmax | 4 | 0.019783 |\n| Shared-pool softmax + discovery weighting | 2 | 0.020892 |\n\nSelected improvement over production: **6.95% relative**. The original two-epoch\ncontrol has a slightly higher point estimate. We selected the shared-pool\nobjective for comparable retrieval with a faster extra epoch (166 versus 221\nseconds on four CPU threads). The paired bootstrap interval against production\nincludes zero; this public improvement is modest and does not establish online\nlift or a hidden-test result. See `comparison_initial.json` for uncertainty.\n\n## Reproduction and boundaries\n\nRun `sh /app/solution/refit.sh`. It reproduces the warm-up with\n`train_production.py`, then calls `train_current_model.py --stage submission\n--epochs 2 --ranking-loss pool-softmax --hard-negatives 0\n--negative-pool-size 4096 --loss-microbatch-size 4096`, resuming the warm-up.\nThe selected checkpoint uses the original chronological internal split, with the\nconfigured objective evaluated using fixed sampling seeds.\n\nThe GRU, item tower, 192-dimensional embeddings, raw dot-product serving,\ncutoffs, target users, and platform eligibility masks are unchanged. There is no\nuser-ID embedding, cross-encoder, reranker, or selected popularity correction.\nThe final item vectors remain independently precomputable. Organic outcomes are\nan observational offline target, not propensity-corrected causal lift.\n\n`test_pool_objective.py` checks the exact full-catalog loss limit and finite\nnonzero gradients. `test_hard_negatives.py` checks filtering and duplicate\nexclusion for the rejected ablation. `validate_submission.py` verifies the final\nfile, integer columns, exact users, 100 distinct items and ranks per user, and\ntest-candidate eligibility. Ranking writes are atomic.\nMD\nPYTHONDONTWRITEBYTECODE=1 /opt/task-venv/bin/python /app/solution/compare_public.py /app/solution/control_validation.parquet /app/solution/pool_validation.parquet > /app/solution/comparison_control.json" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat > /app/solution/README.md <<'MD'\n# Music-discovery two-tower\n\nThe selected recipe uses one production BCE warm-up epoch followed by one\nshared-pool sampled-softmax epoch. `submission.parquet` is the final-cutoff\nranking, refitted on `train_* + val_*`.\n\n## Diagnosis and change\n\nThe original loss averages 24 uniform and eight frequency-corrected in-batch\nnegatives. Most uniform draws are easy; the small in-batch component further\ndownweights frequent competitors. Its frequency weights are only a heuristic:\nunique-item sampling and rejection change the actual proposal distribution.\nThe baseline puts the same track first for 2,197 of 8,456 users.\n\nThe selected objective samples 4,096 unique catalog items uniformly per block,\nshares their encodings, and computes a matrix of user–item scores. It masks the\ncomplete training-prefix positive set, active likes, and current target. A\nsampled-softmax loss emphasizes high-scoring competitors smoothly; multiplying\nthe sampled negative partition by catalog-size/pool-size estimates its full\ncatalog mass. Existing organic-strong target weights remain unchanged.\n\nExplicit top-eight hard mining with 50% negative-loss weight failed badly. More\nsoftmax epochs, discovery-graded target weighting, and catalog-prior calibration\nalso failed to improve the selected public score. None is used in the submission.\n\n## Public backtest\n\nAll fitting and features for these rankings use only `train_*`. Scores are the\nprovided evaluator's graded NDCG@10 over 4,263 relevant users.\n\n| Recipe | Total epochs | NDCG@10 |\n|---|---:|---:|\n| Production | 1 | 0.021280 |\n| Original-objective control | 2 | 0.022962 |\n| Hard-negative BCE | 2 | 0.002557 |\n| **Selected shared-pool softmax** | **2** | **0.022759** |\n| Shared-pool softmax | 3 | 0.022422 |\n| Shared-pool softmax | 4 | 0.019783 |\n| Shared-pool softmax + discovery weighting | 2 | 0.020892 |\n\nSelected improvement over production: **6.95% relative**. The original two-epoch\ncontrol has a slightly higher point estimate. We selected the shared-pool\nobjective for comparable retrieval with a faster extra epoch (166 versus 221\nseconds on four CPU threads). The paired bootstrap interval against production\nincludes zero; this public improvement is modest and does not establish online\nlift or a hidden-test result. See `comparison_initial.json` for uncertainty.\n\n## Reproduction and boundaries\n\nRun `sh /app/solution/refit.sh`. It reproduces the warm-up with\n`train_production.py`, then calls `train_current_model.py --stage submission\n--epochs 2 --ranking-loss pool-softmax --hard-negatives 0\n--negative-pool-size 4096 --loss-microbatch-size 4096`, resuming the warm-up.\nThe selected checkpoint uses the original chronological internal split, with the\nconfigured objective evaluated using fixed sampling seeds.\n\nThe GRU, item tower, 192-dimensional embeddings, raw dot-product serving,\ncutoffs, target users, and platform eligibility masks are unchanged. There is no\nuser-ID embedding, cross-encoder, reranker, or selected popularity correction.\nThe final item vectors remain independently precomputable. Organic outcomes are\nan observational offline target, not propensity-corrected causal lift.\n\n`test_pool_objective.py` checks the exact full-catalog loss limit and finite\nnonzero gradients. `test_hard_negatives.py` checks filtering and duplicate\nexclusion for the rejected ablation. `validate_submission.py` verifies the final\nfile, integer columns, exact users, 100 distinct items and ranks per user, and\ntest-candidate eligibility. Ranking writes are atomic.\nMD\nPYTHONDONTWRITEBYTECODE=1 /opt/task-venv/bin/python /app/solution/compare_public.py /app/solution/control_validation.parquet /app/solution/pool_validation.parquet > /app/solution/comparison_control.json" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 5, "nanos": 361625611 }, "formatted_output": "" } } ## Step 123 · agent · 2026-09-23T06:14:49.810Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T06:14:50.468Z { "code": "text(await tools.exec_command({cmd:\"tail -5 /app/solution/final.log && cat /app/solution/comparison_control.json\",max_output_tokens:1400}));\ntext(await tools.clock__curr_time({}));\n" } ### Tool output · 2026-09-23T06:14:50.576Z Script completed Wall time 0.0 seconds Output: {"chunk_id":"b626f9","wall_time_seconds":0.00000501,"exit_code":0,"original_token_count":256,"output":"Training 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 objective\nresumed validation-objective training: epoch=1 loss=9.104300\nepoch=2/2 batches=25 targets=97,617 batch_bce=8.609646 running_bce=9.041091 elapsed=16.0s\nepoch=2/2 batches=50 targets=196,561 batch_bce=8.980230 running_bce=8.993424 elapsed=32.5s\nepoch=2/2 batches=75 targets=301,265 batch_bce=8.932291 running_bce=9.005951 elapsed=49.1s\n{\n \"/app/solution/control_validation.parquet\": {\n \"ndcg_at_10\": 0.02296198006527616,\n \"users\": 4263,\n \"delta_from_first\": 0.0,\n \"paired_bootstrap_95pct\": [\n 0.0,\n 0.0\n ]\n },\n \"/app/solution/pool_validation.parquet\": {\n \"ndcg_at_10\": 0.022759429484195553,\n \"users\": 4263,\n \"delta_from_first\": -0.00020255058108060672,\n \"paired_bootstrap_95pct\": [\n -0.0027347735865456886,\n 0.0022741141505421337\n ]\n }\n}\n"} {"current_time":"2026-09-23 06:14:50 UTC"} ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-4a576daf-81d8-4524-ae04-9bc06508de78", "process_id": "1288", "command": [ "/bin/sh", "-lc", "tail -5 /app/solution/final.log && cat /app/solution/comparison_control.json" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -5 /app/solution/final.log && cat /app/solution/comparison_control.json" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "Training 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 objective\nresumed validation-objective training: epoch=1 loss=9.104300\nepoch=2/2 batches=25 targets=97,617 batch_bce=8.609646 running_bce=9.041091 elapsed=16.0s\nepoch=2/2 batches=50 targets=196,561 batch_bce=8.980230 running_bce=8.993424 elapsed=32.5s\nepoch=2/2 batches=75 targets=301,265 batch_bce=8.932291 running_bce=9.005951 elapsed=49.1s\n{\n \"/app/solution/control_validation.parquet\": {\n \"ndcg_at_10\": 0.02296198006527616,\n \"users\": 4263,\n \"delta_from_first\": 0.0,\n \"paired_bootstrap_95pct\": [\n 0.0,\n 0.0\n ]\n },\n \"/app/solution/pool_validation.parquet\": {\n \"ndcg_at_10\": 0.022759429484195553,\n \"users\": 4263,\n \"delta_from_first\": -0.00020255058108060672,\n \"paired_bootstrap_95pct\": [\n -0.0027347735865456886,\n 0.0022741141505421337\n ]\n }\n}\n", "stderr": "", "aggregated_output": "Training 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 objective\nresumed validation-objective training: epoch=1 loss=9.104300\nepoch=2/2 batches=25 targets=97,617 batch_bce=8.609646 running_bce=9.041091 elapsed=16.0s\nepoch=2/2 batches=50 targets=196,561 batch_bce=8.980230 running_bce=8.993424 elapsed=32.5s\nepoch=2/2 batches=75 targets=301,265 batch_bce=8.932291 running_bce=9.005951 elapsed=49.1s\n{\n \"/app/solution/control_validation.parquet\": {\n \"ndcg_at_10\": 0.02296198006527616,\n \"users\": 4263,\n \"delta_from_first\": 0.0,\n \"paired_bootstrap_95pct\": [\n 0.0,\n 0.0\n ]\n },\n \"/app/solution/pool_validation.parquet\": {\n \"ndcg_at_10\": 0.022759429484195553,\n \"users\": 4263,\n \"delta_from_first\": -0.00020255058108060672,\n \"paired_bootstrap_95pct\": [\n -0.0027347735865456886,\n 0.0022741141505421337\n ]\n }\n}\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 5010 }, "formatted_output": "Training 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 objective\nresumed validation-objective training: epoch=1 loss=9.104300\nepoch=2/2 batches=25 targets=97,617 batch_bce=8.609646 running_bce=9.041091 elapsed=16.0s\nepoch=2/2 batches=50 targets=196,561 batch_bce=8.980230 running_bce=8.993424 elapsed=32.5s\nepoch=2/2 batches=75 targets=301,265 batch_bce=8.932291 running_bce=9.005951 elapsed=49.1s\n{\n \"/app/solution/control_validation.parquet\": {\n \"ndcg_at_10\": 0.02296198006527616,\n \"users\": 4263,\n \"delta_from_first\": 0.0,\n \"paired_bootstrap_95pct\": [\n 0.0,\n 0.0\n ]\n },\n \"/app/solution/pool_validation.parquet\": {\n \"ndcg_at_10\": 0.022759429484195553,\n \"users\": 4263,\n \"delta_from_first\": -0.00020255058108060672,\n \"paired_bootstrap_95pct\": [\n -0.0027347735865456886,\n 0.0022741141505421337\n ]\n }\n}\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "session_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "root_turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "response_id": "resp_021598e9bafc9da7016ab36e4491cc87d2a8529c77567dcabd", "usage": { "input_tokens": 117065, "cached_input_tokens": 115328, "cache_write_input_tokens": 0, "output_tokens": 573, "reasoning_output_tokens": 516, "total_tokens": 117638 }, "turn_token_usage": { "input_tokens": 10272747, "cached_input_tokens": 10105600, "cache_write_input_tokens": 0, "output_tokens": 53725, "reasoning_output_tokens": 32767, "total_tokens": 10326472 }, "thread_token_usage": { "input_tokens": 10272747, "cached_input_tokens": 10105600, "cache_write_input_tokens": 0, "output_tokens": 53725, "reasoning_output_tokens": 32767, "total_tokens": 10326472 } } ## Step 124 · agent · 2026-09-23T06:15:12.528Z ### Thinking unavailable — no text recorded The selected refit is progressing on `train_* + val_*`. The implementation, reproduction script, and README are in place. The README records both the selected model’s 6.95% point-estimate gain over production and the uncertainty shown by the controls. ## Step 125 · agent · 2026-09-23T06:15:31.304Z ### Thinking unavailable — no text recorded ### sleep · 2026-09-23T06:15:31.480Z { "duration_ms": 35000 } ### Tool output · 2026-09-23T06:16:06.488Z Wall time: 35.0035 seconds Sleep completed. ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "session_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "root_turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "response_id": "resp_021598e9bafc9da7016ab36e5ad0a487d2b8abda920aac0a3b", "usage": { "input_tokens": 118091, "cached_input_tokens": 116864, "cache_write_input_tokens": 0, "output_tokens": 1115, "reasoning_output_tokens": 1034, "total_tokens": 119206 }, "turn_token_usage": { "input_tokens": 10390838, "cached_input_tokens": 10222464, "cache_write_input_tokens": 0, "output_tokens": 54840, "reasoning_output_tokens": 33801, "total_tokens": 10445678 }, "thread_token_usage": { "input_tokens": 10390838, "cached_input_tokens": 10222464, "cache_write_input_tokens": 0, "output_tokens": 54840, "reasoning_output_tokens": 33801, "total_tokens": 10445678 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "Extension", "kind": "clock.sleep", "id": "call_AZ1zRGsKvtYVYgXKrNV5Fa7X", "durationMs": 35000 } } ### exec · 2026-09-23T06:16:08.996Z { "code": "text(await tools.exec_command({cmd:\"tail -4 /app/solution/final.log\",max_output_tokens:1000}));\ntext(await tools.clock__curr_time({}));\n" } ### Tool output · 2026-09-23T06:16:09.125Z Script completed Wall time 0.0 seconds Output: {"chunk_id":"ef5840","wall_time_seconds":0.000005351,"exit_code":0,"original_token_count":93,"output":"epoch=2/2 batches=125 targets=498,782 batch_bce=9.177387 running_bce=8.977183 elapsed=80.2s\nepoch=2/2 batches=150 targets=597,467 batch_bce=8.851833 running_bce=8.964730 elapsed=95.5s\nepoch=2/2 batches=175 targets=689,009 batch_bce=8.577190 running_bce=8.954397 elapsed=110.1s\nepoch=2/2 batches=200 targets=790,657 batch_bce=8.790282 running_bce=8.936151 elapsed=126.3s\n"} {"current_time":"2026-09-23 06:16:09 UTC"} ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-2f3d04a1-b582-44a5-928a-47ffa9fa4755", "process_id": "24724", "command": [ "/bin/sh", "-lc", "tail -4 /app/solution/final.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -4 /app/solution/final.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "epoch=2/2 batches=125 targets=498,782 batch_bce=9.177387 running_bce=8.977183 elapsed=80.2s\nepoch=2/2 batches=150 targets=597,467 batch_bce=8.851833 running_bce=8.964730 elapsed=95.5s\nepoch=2/2 batches=175 targets=689,009 batch_bce=8.577190 running_bce=8.954397 elapsed=110.1s\nepoch=2/2 batches=200 targets=790,657 batch_bce=8.790282 running_bce=8.936151 elapsed=126.3s\n", "stderr": "", "aggregated_output": "epoch=2/2 batches=125 targets=498,782 batch_bce=9.177387 running_bce=8.977183 elapsed=80.2s\nepoch=2/2 batches=150 targets=597,467 batch_bce=8.851833 running_bce=8.964730 elapsed=95.5s\nepoch=2/2 batches=175 targets=689,009 batch_bce=8.577190 running_bce=8.954397 elapsed=110.1s\nepoch=2/2 batches=200 targets=790,657 batch_bce=8.790282 running_bce=8.936151 elapsed=126.3s\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 5351 }, "formatted_output": "epoch=2/2 batches=125 targets=498,782 batch_bce=9.177387 running_bce=8.977183 elapsed=80.2s\nepoch=2/2 batches=150 targets=597,467 batch_bce=8.851833 running_bce=8.964730 elapsed=95.5s\nepoch=2/2 batches=175 targets=689,009 batch_bce=8.577190 running_bce=8.954397 elapsed=110.1s\nepoch=2/2 batches=200 targets=790,657 batch_bce=8.790282 running_bce=8.936151 elapsed=126.3s\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "session_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "root_turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "response_id": "resp_021598e9bafc9da7016ab36ea6aad087d2bab6bb17702d18e3", "usage": { "input_tokens": 119229, "cached_input_tokens": 117888, "cache_write_input_tokens": 0, "output_tokens": 45, "reasoning_output_tokens": 0, "total_tokens": 119274 }, "turn_token_usage": { "input_tokens": 10510067, "cached_input_tokens": 10340352, "cache_write_input_tokens": 0, "output_tokens": 54885, "reasoning_output_tokens": 33801, "total_tokens": 10564952 }, "thread_token_usage": { "input_tokens": 10510067, "cached_input_tokens": 10340352, "cache_write_input_tokens": 0, "output_tokens": 54885, "reasoning_output_tokens": 33801, "total_tokens": 10564952 } } ## Step 126 · agent · 2026-09-23T06:16:13.317Z ### Thinking unavailable — no text recorded The refit has processed about three-quarters of its targets without numerical issues. Once ranking finishes, I’ll verify exact user coverage, 100 distinct items and ranks per user, integer columns, and test-candidate eligibility. ## Step 127 · agent · 2026-09-23T06:16:33.019Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T06:16:34.350Z { "code": "text(await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\nfrom pathlib import Path\\nfor name in ['edit_objective.py','add_pool_objective.py','check_resources.py']:\\n (Path('/app/solution')/name).unlink(missing_ok=True)\\nPY\\ntail -3 /app/solution/final.log\",max_output_tokens:1000}));\n" } ### Tool output · 2026-09-23T06:16:34.521Z Script completed Wall time 0.2 seconds Output: {"chunk_id":"419524","wall_time_seconds":0.000003671,"exit_code":0,"original_token_count":70,"output":"epoch=2/2 batches=175 targets=689,009 batch_bce=8.577190 running_bce=8.954397 elapsed=110.1s\nepoch=2/2 batches=200 targets=790,657 batch_bce=8.790282 running_bce=8.936151 elapsed=126.3s\nepoch=2/2 batches=225 targets=893,699 batch_bce=8.881661 running_bce=8.909205 elapsed=142.4s\n"} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "session_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "root_turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "response_id": "resp_021598e9bafc9da7016ab36ea9648887d28c264b2f52cb6f76", "usage": { "input_tokens": 119494, "cached_input_tokens": 119040, "cache_write_input_tokens": 0, "output_tokens": 760, "reasoning_output_tokens": 609, "total_tokens": 120254 }, "turn_token_usage": { "input_tokens": 10629561, "cached_input_tokens": 10459392, "cache_write_input_tokens": 0, "output_tokens": 55645, "reasoning_output_tokens": 34410, "total_tokens": 10685206 }, "thread_token_usage": { "input_tokens": 10629561, "cached_input_tokens": 10459392, "cache_write_input_tokens": 0, "output_tokens": 55645, "reasoning_output_tokens": 34410, "total_tokens": 10685206 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-df0c333b-e995-4ff7-ae83-c08c8ace55ae", "process_id": "67956", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nfor name in ['edit_objective.py','add_pool_objective.py','check_resources.py']:\n (Path('/app/solution')/name).unlink(missing_ok=True)\nPY\ntail -3 /app/solution/final.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nfor name in ['edit_objective.py','add_pool_objective.py','check_resources.py']:\n (Path('/app/solution')/name).unlink(missing_ok=True)\nPY\ntail -3 /app/solution/final.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "epoch=2/2 batches=175 targets=689,009 batch_bce=8.577190 running_bce=8.954397 elapsed=110.1s\nepoch=2/2 batches=200 targets=790,657 batch_bce=8.790282 running_bce=8.936151 elapsed=126.3s\nepoch=2/2 batches=225 targets=893,699 batch_bce=8.881661 running_bce=8.909205 elapsed=142.4s\n", "stderr": "", "aggregated_output": "epoch=2/2 batches=175 targets=689,009 batch_bce=8.577190 running_bce=8.954397 elapsed=110.1s\nepoch=2/2 batches=200 targets=790,657 batch_bce=8.790282 running_bce=8.936151 elapsed=126.3s\nepoch=2/2 batches=225 targets=893,699 batch_bce=8.881661 running_bce=8.909205 elapsed=142.4s\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 3671 }, "formatted_output": "epoch=2/2 batches=175 targets=689,009 batch_bce=8.577190 running_bce=8.954397 elapsed=110.1s\nepoch=2/2 batches=200 targets=790,657 batch_bce=8.790282 running_bce=8.936151 elapsed=126.3s\nepoch=2/2 batches=225 targets=893,699 batch_bce=8.881661 running_bce=8.909205 elapsed=142.4s\n" } } ## Step 128 · agent · 2026-09-23T06:16:50.614Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T06:16:56.760Z { "code": "text(await tools.exec_command({cmd:\"while [ ! -s /app/solution/final.json ]; do sleep 1; done\\nPYTHONDONTWRITEBYTECODE=1 /opt/task-venv/bin/python /app/solution/validate_submission.py > /app/solution/submission_check.txt\\ncat /app/solution/submission_check.txt\\n/opt/task-venv/bin/python - <<'PY'\\nfrom pathlib import Path\\nimport hashlib, json\\nroot=Path('/app/solution')\\nmeta=json.loads((root/'final.json').read_text())\\nassert meta['stage']=='submission'\\nassert meta['config']['ranking_loss']=='pool-softmax'\\nassert meta['config']['epochs']==2\\nassert meta['config']['target_weighting']=='organic-strong'\\nassert meta['config']['retrieval_prior_weight']==0.0\\nassert meta['training_history'][-1]['epoch']==2\\nh=hashlib.sha256((root/'submission.parquet').read_bytes()).hexdigest()\\n(root/'submission.sha256').write_text(h+' submission.parquet\\\\n')\\nprint('PASS: final refit metadata and SHA-256 recorded')\\nPY\",yield_time_ms:1000,max_output_tokens:1000}));\n" } ### Tool output · 2026-09-23T06:16:57.931Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"5378c4","wall_time_seconds":1.001145772,"session_id":71837,"original_token_count":0,"output":""} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "session_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "root_turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "response_id": "resp_021598e9bafc9da7016ab36ec2b44087d2953bd9373df83742", "usage": { "input_tokens": 120418, "cached_input_tokens": 119296, "cache_write_input_tokens": 0, "output_tokens": 694, "reasoning_output_tokens": 420, "total_tokens": 121112 }, "turn_token_usage": { "input_tokens": 10749979, "cached_input_tokens": 10578688, "cache_write_input_tokens": 0, "output_tokens": 56339, "reasoning_output_tokens": 34830, "total_tokens": 10806318 }, "thread_token_usage": { "input_tokens": 10749979, "cached_input_tokens": 10578688, "cache_write_input_tokens": 0, "output_tokens": 56339, "reasoning_output_tokens": 34830, "total_tokens": 10806318 } } ## Step 129 · agent · 2026-09-23T06:17:16.349Z ### Thinking unavailable — no text recorded ### sleep · 2026-09-23T06:17:16.415Z { "duration_ms": 20000 } ### Tool output · 2026-09-23T06:17:36.423Z Wall time: 20.0036 seconds Sleep completed. ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "session_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "root_turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "response_id": "resp_021598e9bafc9da7016ab36eda19e487d29ac7afe1a39688b2", "usage": { "input_tokens": 121170, "cached_input_tokens": 120192, "cache_write_input_tokens": 0, "output_tokens": 453, "reasoning_output_tokens": 432, "total_tokens": 121623 }, "turn_token_usage": { "input_tokens": 10871149, "cached_input_tokens": 10698880, "cache_write_input_tokens": 0, "output_tokens": 56792, "reasoning_output_tokens": 35262, "total_tokens": 10927941 }, "thread_token_usage": { "input_tokens": 10871149, "cached_input_tokens": 10698880, "cache_write_input_tokens": 0, "output_tokens": 56792, "reasoning_output_tokens": 35262, "total_tokens": 10927941 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "Extension", "kind": "clock.sleep", "id": "call_Vi9PxZPCXywurHVtVQpSbTBO", "durationMs": 20000 } } ### exec · 2026-09-23T06:17:39.901Z { "code": "text(await tools.exec_command({cmd:\"tail -5 /app/solution/final.log\",max_output_tokens:1000}));\ntext(await tools.clock__curr_time({}));\n" } ### Tool output · 2026-09-23T06:17:40.018Z Script completed Wall time 0.0 seconds Output: {"chunk_id":"77a510","wall_time_seconds":0.00000461,"exit_code":0,"original_token_count":121,"output":"epoch=2/2 batches=225 targets=893,699 batch_bce=8.881661 running_bce=8.909205 elapsed=142.4s\nepoch=2/2 batches=250 targets=994,228 batch_bce=8.269615 running_bce=8.882647 elapsed=158.0s\nsaved epoch checkpoint: /app/solution/final_ckpt/epoch-02.pt\nepoch=2/2 train_loss=8.86546 validation_loss=8.68068 pair_accuracy=0.848 train_targets=1,067,606 validation_targets=131,997 train_seconds=169.7 validation_seconds=12.8\nrestored best validation-objective checkpoint: epoch=2 loss=8.680680\n"} {"current_time":"2026-09-23 06:17:39 UTC"} ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-178f4954-6088-4afb-95c5-90654408b5fa", "process_id": "23773", "command": [ "/bin/sh", "-lc", "tail -5 /app/solution/final.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -5 /app/solution/final.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "epoch=2/2 batches=225 targets=893,699 batch_bce=8.881661 running_bce=8.909205 elapsed=142.4s\nepoch=2/2 batches=250 targets=994,228 batch_bce=8.269615 running_bce=8.882647 elapsed=158.0s\nsaved epoch checkpoint: /app/solution/final_ckpt/epoch-02.pt\nepoch=2/2 train_loss=8.86546 validation_loss=8.68068 pair_accuracy=0.848 train_targets=1,067,606 validation_targets=131,997 train_seconds=169.7 validation_seconds=12.8\nrestored best validation-objective checkpoint: epoch=2 loss=8.680680\n", "stderr": "", "aggregated_output": "epoch=2/2 batches=225 targets=893,699 batch_bce=8.881661 running_bce=8.909205 elapsed=142.4s\nepoch=2/2 batches=250 targets=994,228 batch_bce=8.269615 running_bce=8.882647 elapsed=158.0s\nsaved epoch checkpoint: /app/solution/final_ckpt/epoch-02.pt\nepoch=2/2 train_loss=8.86546 validation_loss=8.68068 pair_accuracy=0.848 train_targets=1,067,606 validation_targets=131,997 train_seconds=169.7 validation_seconds=12.8\nrestored best validation-objective checkpoint: epoch=2 loss=8.680680\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4610 }, "formatted_output": "epoch=2/2 batches=225 targets=893,699 batch_bce=8.881661 running_bce=8.909205 elapsed=142.4s\nepoch=2/2 batches=250 targets=994,228 batch_bce=8.269615 running_bce=8.882647 elapsed=158.0s\nsaved epoch checkpoint: /app/solution/final_ckpt/epoch-02.pt\nepoch=2/2 train_loss=8.86546 validation_loss=8.68068 pair_accuracy=0.848 train_targets=1,067,606 validation_targets=131,997 train_seconds=169.7 validation_seconds=12.8\nrestored best validation-objective checkpoint: epoch=2 loss=8.680680\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "session_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "root_turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "response_id": "resp_021598e9bafc9da7016ab36f00af7c87d2acbfbad9866a1f4e", "usage": { "input_tokens": 121646, "cached_input_tokens": 120960, "cache_write_input_tokens": 0, "output_tokens": 45, "reasoning_output_tokens": 0, "total_tokens": 121691 }, "turn_token_usage": { "input_tokens": 10992795, "cached_input_tokens": 10819840, "cache_write_input_tokens": 0, "output_tokens": 56837, "reasoning_output_tokens": 35262, "total_tokens": 11049632 }, "thread_token_usage": { "input_tokens": 10992795, "cached_input_tokens": 10819840, "cache_write_input_tokens": 0, "output_tokens": 56837, "reasoning_output_tokens": 35262, "total_tokens": 11049632 } } ## Step 130 · agent · 2026-09-23T06:17:44.572Z ### Thinking unavailable — no text recorded The refit finished training and selected its second-epoch checkpoint. It is now ranking the test candidates. Final validation is queued to run as soon as the new file is written. ## Step 131 · agent · 2026-09-23T06:18:03.138Z ### Thinking unavailable — no text recorded ### sleep · 2026-09-23T06:18:03.192Z { "duration_ms": 20000 } ### Tool output · 2026-09-23T06:18:23.201Z Wall time: 20.0039 seconds Sleep completed. ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "session_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "root_turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "response_id": "resp_021598e9bafc9da7016ab36f0454f487d29170488080f956a0", "usage": { "input_tokens": 121926, "cached_input_tokens": 121472, "cache_write_input_tokens": 0, "output_tokens": 663, "reasoning_output_tokens": 599, "total_tokens": 122589 }, "turn_token_usage": { "input_tokens": 11114721, "cached_input_tokens": 10941312, "cache_write_input_tokens": 0, "output_tokens": 57500, "reasoning_output_tokens": 35861, "total_tokens": 11172221 }, "thread_token_usage": { "input_tokens": 11114721, "cached_input_tokens": 10941312, "cache_write_input_tokens": 0, "output_tokens": 57500, "reasoning_output_tokens": 35861, "total_tokens": 11172221 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "Extension", "kind": "clock.sleep", "id": "call_JGPwXyJhcSTdvLxHCB0Q8Ynz", "durationMs": 20000 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-6b08d564-cc66-485c-a4b9-d92210bd47a6", "process_id": "41794", "command": [ "/bin/sh", "-lc", "OPENBLAS_NUM_THREADS=1 OMP_NUM_THREADS=4 PYTHONDONTWRITEBYTECODE=1 /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_submission_ckpt/best.pt --epochs 2 --early-stopping-patience 2 --threads 4 --loss-microbatch-size 4096 --hard-negatives 0 --negative-pool-size 4096 --ranking-loss pool-softmax > /app/solution/final.json 2> /app/solution/final.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "OPENBLAS_NUM_THREADS=1 OMP_NUM_THREADS=4 PYTHONDONTWRITEBYTECODE=1 /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_submission_ckpt/best.pt --epochs 2 --early-stopping-patience 2 --threads 4 --loss-microbatch-size 4096 --hard-negatives 0 --negative-pool-size 4096 --ranking-loss pool-softmax > /app/solution/final.json 2> /app/solution/final.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 287, "nanos": 164874863 }, "formatted_output": "" } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-43305259-51ac-46ea-b92e-f8c50dfc96ac", "process_id": "71837", "command": [ "/bin/sh", "-lc", "while [ ! -s /app/solution/final.json ]; do sleep 1; done\nPYTHONDONTWRITEBYTECODE=1 /opt/task-venv/bin/python /app/solution/validate_submission.py > /app/solution/submission_check.txt\ncat /app/solution/submission_check.txt\n/opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nimport hashlib, json\nroot=Path('/app/solution')\nmeta=json.loads((root/'final.json').read_text())\nassert meta['stage']=='submission'\nassert meta['config']['ranking_loss']=='pool-softmax'\nassert meta['config']['epochs']==2\nassert meta['config']['target_weighting']=='organic-strong'\nassert meta['config']['retrieval_prior_weight']==0.0\nassert meta['training_history'][-1]['epoch']==2\nh=hashlib.sha256((root/'submission.parquet').read_bytes()).hexdigest()\n(root/'submission.sha256').write_text(h+' submission.parquet\\n')\nprint('PASS: final refit metadata and SHA-256 recorded')\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "while [ ! -s /app/solution/final.json ]; do sleep 1; done\nPYTHONDONTWRITEBYTECODE=1 /opt/task-venv/bin/python /app/solution/validate_submission.py > /app/solution/submission_check.txt\ncat /app/solution/submission_check.txt\n/opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nimport hashlib, json\nroot=Path('/app/solution')\nmeta=json.loads((root/'final.json').read_text())\nassert meta['stage']=='submission'\nassert meta['config']['ranking_loss']=='pool-softmax'\nassert meta['config']['epochs']==2\nassert meta['config']['target_weighting']=='organic-strong'\nassert meta['config']['retrieval_prior_weight']==0.0\nassert meta['training_history'][-1]['epoch']==2\nh=hashlib.sha256((root/'submission.parquet').read_bytes()).hexdigest()\n(root/'submission.sha256').write_text(h+' submission.parquet\\n')\nprint('PASS: final refit metadata and SHA-256 recorded')\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "PASS: regular file; 8,456 users; 845,600 rows; integer columns; 100 distinct eligible items and ranks 1..100 per user\nPASS: final refit metadata and SHA-256 recorded\n", "stderr": "", "aggregated_output": "PASS: regular file; 8,456 users; 845,600 rows; integer columns; 100 distinct eligible items and ranks 1..100 per user\nPASS: final refit metadata and SHA-256 recorded\n", "exit_code": 0, "duration": { "secs": 88, "nanos": 992216189 }, "formatted_output": "PASS: regular file; 8,456 users; 845,600 rows; integer columns; 100 distinct eligible items and ranks 1..100 per user\nPASS: final refit metadata and SHA-256 recorded\n" } } ### exec · 2026-09-23T06:18:27.632Z { "code": "text(await tools.exec_command({cmd:\"tail -4 /app/solution/final.log && cat /app/solution/submission_check.txt\",max_output_tokens:1000}));\ntext(await tools.clock__curr_time({}));\n" } ### Tool output · 2026-09-23T06:18:27.749Z Script completed Wall time 0.0 seconds Output: {"chunk_id":"f2b673","wall_time_seconds":0.00000352,"exit_code":0,"original_token_count":55,"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\nPASS: regular file; 8,456 users; 845,600 rows; integer columns; 100 distinct eligible items and ranks 1..100 per user\n"} {"current_time":"2026-09-23 06:18:27 UTC"} ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-8de3b829-6798-4e8d-94e7-39d82b42515b", "process_id": "80300", "command": [ "/bin/sh", "-lc", "tail -4 /app/solution/final.log && cat /app/solution/submission_check.txt" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -4 /app/solution/final.log && cat /app/solution/submission_check.txt" } ], "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\nPASS: regular file; 8,456 users; 845,600 rows; integer columns; 100 distinct eligible items and ranks 1..100 per user\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\nPASS: regular file; 8,456 users; 845,600 rows; integer columns; 100 distinct eligible items and ranks 1..100 per user\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 3520 }, "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\nPASS: regular file; 8,456 users; 845,600 rows; integer columns; 100 distinct eligible items and ranks 1..100 per user\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "session_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "root_turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "response_id": "resp_021598e9bafc9da7016ab36f2f609487d2b7cba9130dc97c5f", "usage": { "input_tokens": 122612, "cached_input_tokens": 121728, "cache_write_input_tokens": 0, "output_tokens": 55, "reasoning_output_tokens": 0, "total_tokens": 122667 }, "turn_token_usage": { "input_tokens": 11237333, "cached_input_tokens": 11063040, "cache_write_input_tokens": 0, "output_tokens": 57555, "reasoning_output_tokens": 35861, "total_tokens": 11294888 }, "thread_token_usage": { "input_tokens": 11237333, "cached_input_tokens": 11063040, "cache_write_input_tokens": 0, "output_tokens": 57555, "reasoning_output_tokens": 35861, "total_tokens": 11294888 } } ## Step 132 · agent · 2026-09-23T06:18:30.109Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T06:18:34.046Z { "code": "text(await tools.write_stdin({session_id:71837,chars:\"\",yield_time_ms:1000,max_output_tokens:1200}));\ntext(await tools.write_stdin({session_id:41794,chars:\"\",yield_time_ms:1000,max_output_tokens:1000}));\ntext(await tools.exec_command({cmd:\"ls -l /app/solution/submission.parquet /app/solution/submission.sha256 /app/solution/train_current_model.py /app/solution/two_tower_model.py /app/solution/README.md && cat /app/solution/submission.sha256\",max_output_tokens:1500}));\n" } ### Tool output · 2026-09-23T06:18:34.195Z Script completed Wall time 0.0 seconds Output: {"chunk_id":"282f52","wall_time_seconds":0.00000235,"exit_code":0,"original_token_count":42,"output":"PASS: regular file; 8,456 users; 845,600 rows; integer columns; 100 distinct eligible items and ranks 1..100 per user\nPASS: final refit metadata and SHA-256 recorded\n"} {"chunk_id":"f8136d","wall_time_seconds":4.5e-7,"exit_code":0,"original_token_count":0,"output":""} {"chunk_id":"60e2de","wall_time_seconds":0.00000278,"exit_code":0,"original_token_count":119,"output":"-rw-r--r-- 1 agent agent 3525 Sep 23 06:14 /app/solution/README.md\n-rw-r--r-- 1 agent agent 1390845 Sep 23 06:18 /app/solution/submission.parquet\n-rw-r--r-- 1 agent agent 85 Sep 23 06:18 /app/solution/submission.sha256\n-rw-r--r-- 1 agent agent 89838 Sep 23 06:10 /app/solution/train_current_model.py\n-rw-r--r-- 1 agent agent 15985 Sep 23 05:19 /app/solution/two_tower_model.py\n8ca1e0673452e6feec395e32192bf9f257abd247e310ea55511975034eae06c4 submission.parquet\n"} ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-d6025d20-b5aa-47fe-8c29-d53e2aaef71c", "process_id": "88432", "command": [ "/bin/sh", "-lc", "ls -l /app/solution/submission.parquet /app/solution/submission.sha256 /app/solution/train_current_model.py /app/solution/two_tower_model.py /app/solution/README.md && cat /app/solution/submission.sha256" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "list_files", "cmd": "ls -l /app/solution/submission.parquet /app/solution/submission.sha256 /app/solution/train_current_model.py /app/solution/two_tower_model.py /app/solution/README.md", "path": "submission.parquet" }, { "type": "read", "cmd": "cat /app/solution/submission.sha256", "name": "submission.sha256", "path": "/app/solution/submission.sha256" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "-rw-r--r-- 1 agent agent 3525 Sep 23 06:14 /app/solution/README.md\n-rw-r--r-- 1 agent agent 1390845 Sep 23 06:18 /app/solution/submission.parquet\n-rw-r--r-- 1 agent agent 85 Sep 23 06:18 /app/solution/submission.sha256\n-rw-r--r-- 1 agent agent 89838 Sep 23 06:10 /app/solution/train_current_model.py\n-rw-r--r-- 1 agent agent 15985 Sep 23 05:19 /app/solution/two_tower_model.py\n8ca1e0673452e6feec395e32192bf9f257abd247e310ea55511975034eae06c4 submission.parquet\n", "stderr": "", "aggregated_output": "-rw-r--r-- 1 agent agent 3525 Sep 23 06:14 /app/solution/README.md\n-rw-r--r-- 1 agent agent 1390845 Sep 23 06:18 /app/solution/submission.parquet\n-rw-r--r-- 1 agent agent 85 Sep 23 06:18 /app/solution/submission.sha256\n-rw-r--r-- 1 agent agent 89838 Sep 23 06:10 /app/solution/train_current_model.py\n-rw-r--r-- 1 agent agent 15985 Sep 23 05:19 /app/solution/two_tower_model.py\n8ca1e0673452e6feec395e32192bf9f257abd247e310ea55511975034eae06c4 submission.parquet\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 2780 }, "formatted_output": "-rw-r--r-- 1 agent agent 3525 Sep 23 06:14 /app/solution/README.md\n-rw-r--r-- 1 agent agent 1390845 Sep 23 06:18 /app/solution/submission.parquet\n-rw-r--r-- 1 agent agent 85 Sep 23 06:18 /app/solution/submission.sha256\n-rw-r--r-- 1 agent agent 89838 Sep 23 06:10 /app/solution/train_current_model.py\n-rw-r--r-- 1 agent agent 15985 Sep 23 05:19 /app/solution/two_tower_model.py\n8ca1e0673452e6feec395e32192bf9f257abd247e310ea55511975034eae06c4 submission.parquet\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "session_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "root_turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "response_id": "resp_021598e9bafc9da7016ab36f3407c087d282cd5bc615a2fcda", "usage": { "input_tokens": 122823, "cached_input_tokens": 122496, "cache_write_input_tokens": 0, "output_tokens": 160, "reasoning_output_tokens": 20, "total_tokens": 122983 }, "turn_token_usage": { "input_tokens": 11360156, "cached_input_tokens": 11185536, "cache_write_input_tokens": 0, "output_tokens": 57715, "reasoning_output_tokens": 35881, "total_tokens": 11417871 }, "thread_token_usage": { "input_tokens": 11360156, "cached_input_tokens": 11185536, "cache_write_input_tokens": 0, "output_tokens": 57715, "reasoning_output_tokens": 35881, "total_tokens": 11417871 } } ## Step 133 · agent · 2026-09-23T06:18:44.434Z ### Thinking unavailable — no text recorded Saved [submission.parquet](/app/solution/submission.parquet). Public NDCG@10: **0.021280 → 0.022759** (+6.95%). Refitted on `train_* + val_*`; all format and eligibility checks passed for 8,456 users. [Implementation](/app/solution/train_current_model.py) and [experiment record](/app/solution/README.md) include the changes, controls, uncertainty, and reproduction command. ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "session_id": "01a0ccb4-b23f-76c2-905d-9e8b0e97f817", "root_turn_id": "01a0ccb4-b24d-7903-b491-cb991cb6dff5", "response_id": "resp_021598e9bafc9da7016ab36f3a764c87d28398de1eaca9beb2", "usage": { "input_tokens": 123340, "cached_input_tokens": 122624, "cache_write_input_tokens": 0, "output_tokens": 339, "reasoning_output_tokens": 234, "total_tokens": 123679 }, "turn_token_usage": { "input_tokens": 11483496, "cached_input_tokens": 11308160, "cache_write_input_tokens": 0, "output_tokens": 58054, "reasoning_output_tokens": 36115, "total_tokens": 11541550 }, "thread_token_usage": { "input_tokens": 11483496, "cached_input_tokens": 11308160, "cache_write_input_tokens": 0, "output_tokens": 58054, "reasoning_output_tokens": 36115, "total_tokens": 11541550 } }