{
  "filename": "rollout-2026-01-29T10-18-14-019c0942-bb05-7bf3-a9dc-88b38b29e56c.jsonl",
  "date": "2026-01-29",
  "session_id": "019c0942-bb05-7bf3-a9dc-88b38b29e56c",
  "first_timestamp": "2026-01-29T10:18:14.150Z",
  "last_timestamp": "2026-01-29T10:38:39.887Z",
  "duration_seconds": 1225.7,
  "model": "gpt-5",
  "cli_version": "0.92.0",
  "approval_policy": "on-request",
  "effort": "high",
  "raw_event_count": 268,
  "user_messages": [
    {
      "index": 0,
      "text": "<permissions instructions>Filesystem sandboxing defines which files can be read or written. `sandbox_mode` is `workspace-write`: The sandbox permits reading files, and editing files in `cwd` and `writable_roots`. Editing files in other directories requires approval. Network access is restricted. Approvals are your mechanism to get user consent to run shell commands without the sandbox. `approval_policy` is `on-request`: Commands will be run in the sandbox by default, and you can specify in your tool call if you want to escalate a command to run without sandboxing. If the completing the task requires escalated permissions, Do not let these settings or the sandbox deter you from attempting to accomplish the user's task.\n\nHere are scenarios where you'll need to request approval:\n- You need to run a command that writes to a directory that requires it (e.g. running tests that write to /var)\n- You need to run a GUI app (e.g., open/xdg-open/osascript) to open browsers or files.\n- You are running sandboxed and need to run a command that requires network access (e.g. installing packages)\n- If you run a command that is important to solving the user's query, but it fails because of sandboxing, rerun the command with approval. ALWAYS proceed to use the `sandbox_permissions` and `justification` parameters - do not message the user before requesting approval for the command.\n- You are about to take a potentially destructive action such as an `rm` or `git reset` that the user did not explicitly ask for.\n\nWhen requesting approval to execute a command that will require escalated privileges:\n  - Provide the `sandbox_permissions` parameter with the value `\"require_escalated\"`\n  - Include a short, 1 sentence explanation for why you need escalated permissions in the justification parameter The writable roots are `/home/ubuntu/nikhil/cold-ranker`, `/tmp`.</permissions instructions>",
      "timestamp": "2026-01-29T10:18:14.151Z"
    },
    {
      "index": 1,
      "text": "# AGENTS.md instructions for /home/ubuntu/nikhil/cold-ranker\n\n<INSTRUCTIONS>\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 file path so you can open the source for full instructions when using a specific skill.\n### Available skills\n- skill-creator: Guide for creating effective skills. This skill should be used when users want to create a new skill (or update an existing skill) that extends Codex's capabilities with specialized knowledge, workflows, or tool integrations. (file: /home/ubuntu/.codex/skills/.system/skill-creator/SKILL.md)\n- skill-installer: Install Codex skills into $CODEX_HOME/skills from a curated list or a GitHub repo path. Use when a user asks to list installable skills, install a curated skill, or install a skill from another repo (including private repos). (file: /home/ubuntu/.codex/skills/.system/skill-installer/SKILL.md)\n### How to use skills\n- Discovery: The list above is the skills available in this session (name + description + file path). Skill bodies live on disk at the listed paths.\n- Trigger rules: If the user names a skill (with `$SkillName` or plain text) OR the task clearly matches a skill's description shown above, you must use that skill for that turn. Multiple mentions mean use them all. Do not carry skills across turns unless re-mentioned.\n- Missing/blocked: If a named skill isn't in the list or the path can't be read, say so briefly and continue with the best fallback.\n- How to use a skill (progressive disclosure):\n  1) After deciding to use a skill, open its `SKILL.md`. Read only enough to follow the workflow.\n  2) If `SKILL.md` points to extra folders such as `references/`, load only the specific files needed for the request; don't bulk-load everything.\n  3) If `scripts/` exist, prefer running or patching them instead of retyping large code blocks.\n  4) If `assets/` or templates exist, reuse them instead of recreating from scratch.\n- Coordination and sequencing:\n  - If multiple skills apply, choose the minimal set that covers the request and state the order you'll use them.\n  - Announce which skill(s) you're using and why (one short line). If you skip an obvious skill, say why.\n- Context hygiene:\n  - Keep context small: summarize long sections instead of pasting them; only load extra files when needed.\n  - Avoid deep reference-chasing: prefer opening only files directly linked from `SKILL.md` unless you're blocked.\n  - When variants exist (frameworks, providers, domains), pick only the relevant reference file(s) and note that choice.\n- Safety and fallback: If a skill can't be applied cleanly (missing files, unclear instructions), state the issue, pick the next-best approach, and continue.\n</INSTRUCTIONS>",
      "timestamp": "2026-01-29T10:18:14.151Z"
    },
    {
      "index": 2,
      "text": "<environment_context>\n  <cwd>/home/ubuntu/nikhil/cold-ranker</cwd>\n  <shell>bash</shell>\n</environment_context>",
      "timestamp": "2026-01-29T10:18:14.151Z"
    },
    {
      "index": 3,
      "text": "<permissions instructions>Filesystem sandboxing defines which files can be read or written. `sandbox_mode` is `workspace-write`: The sandbox permits reading files, and editing files in `cwd` and `writable_roots`. Editing files in other directories requires approval. Network access is restricted. Approvals are your mechanism to get user consent to run shell commands without the sandbox. `approval_policy` is `on-request`: Commands will be run in the sandbox by default, and you can specify in your tool call if you want to escalate a command to run without sandboxing. If the completing the task requires escalated permissions, Do not let these settings or the sandbox deter you from attempting to accomplish the user's task.\n\nHere are scenarios where you'll need to request approval:\n- You need to run a command that writes to a directory that requires it (e.g. running tests that write to /var)\n- You need to run a GUI app (e.g., open/xdg-open/osascript) to open browsers or files.\n- You are running sandboxed and need to run a command that requires network access (e.g. installing packages)\n- If you run a command that is important to solving the user's query, but it fails because of sandboxing, rerun the command with approval. ALWAYS proceed to use the `sandbox_permissions` and `justification` parameters - do not message the user before requesting approval for the command.\n- You are about to take a potentially destructive action such as an `rm` or `git reset` that the user did not explicitly ask for.\n\nWhen requesting approval to execute a command that will require escalated privileges:\n  - Provide the `sandbox_permissions` parameter with the value `\"require_escalated\"`\n  - Include a short, 1 sentence explanation for why you need escalated permissions in the justification parameter The writable roots are `/home/ubuntu/nikhil/cold-ranker`, `/home/ubuntu/nikhil/cold-ranker`, `/tmp`.</permissions instructions>",
      "timestamp": "2026-01-29T10:18:14.213Z"
    },
    {
      "index": 4,
      "text": "<permissions instructions>Filesystem sandboxing defines which files can be read or written. `sandbox_mode` is `workspace-write`: The sandbox permits reading files, and editing files in `cwd` and `writable_roots`. Editing files in other directories requires approval. Network access is restricted. Approvals are your mechanism to get user consent to run shell commands without the sandbox. `approval_policy` is `on-request`: Commands will be run in the sandbox by default, and you can specify in your tool call if you want to escalate a command to run without sandboxing. If the completing the task requires escalated permissions, Do not let these settings or the sandbox deter you from attempting to accomplish the user's task.\n\nHere are scenarios where you'll need to request approval:\n- You need to run a command that writes to a directory that requires it (e.g. running tests that write to /var)\n- You need to run a GUI app (e.g., open/xdg-open/osascript) to open browsers or files.\n- You are running sandboxed and need to run a command that requires network access (e.g. installing packages)\n- If you run a command that is important to solving the user's query, but it fails because of sandboxing, rerun the command with approval. ALWAYS proceed to use the `sandbox_permissions` and `justification` parameters - do not message the user before requesting approval for the command.\n- You are about to take a potentially destructive action such as an `rm` or `git reset` that the user did not explicitly ask for.\n\nWhen requesting approval to execute a command that will require escalated privileges:\n  - Provide the `sandbox_permissions` parameter with the value `\"require_escalated\"`\n  - Include a short, 1 sentence explanation for why you need escalated permissions in the justification parameter The writable roots are `/home/ubuntu/nikhil/cold-ranker`, `/home/ubuntu/nikhil/cold-ranker`, `/tmp`.</permissions instructions>",
      "timestamp": "2026-01-29T10:18:14.213Z"
    },
    {
      "index": 5,
      "text": "# Context from my IDE setup:\n\n## Active file: col/src/config/config.py\n\n## Active selection of the file:\nimport os\n\nLANGUAGE=\"bengali\"\nLANGUAGE_KEY=\"bn\"\n\nMODE='test'\n\ndef _env_flag(name: str, default: bool = False) -> bool:\n    value = os.environ.get(name)\n    if value is None:\n        return default\n    return value.strip().lower() in {\"1\", \"true\", \"yes\", \"y\", \"on\"}\n\ndef get_openai_client():\n    try:\n        from openai import OpenAI\n    except Exception as exc:\n        raise ImportError(\n            \"OpenAI embeddings require the `openai` package. Install it with `pip install openai`.\"\n        ) from exc\n\n    api_key = os.environ.get(\"OPENAI_API_KEY\")\n    if not api_key:\n        raise EnvironmentError(\"Set `OPENAI_API_KEY` in your environment to generate embeddings.\")\n    return OpenAI(api_key=api_key)\n\nBATCH_SIZE = 500\n\nCATEGORICAL_COLUMNS = [\"user_id\", \"gender\", \"if_user_genre_pref_embedding_present\", \"target_series_id\", \"target_author_id\"]\nCONTINUOUS_COLUMNS = [\"age_days\", \"target_rating_count\", \"target_average_rating\"]\n\nEMBEDDING_COLUMNS_TO_SIZE = {\n    \"user_genre_pref_embedding\": 512,\n    \n    \"read_series_avg_genre_emb\": 512,\n    \"target_genre_embedding\": 512,\n    \n    \"read_series_avg_prod2vec_emb\": 16,\n    \"target_prod2vec_emb\": 16\n}\n\n# Sizes for explicit cross features (element-wise products)\n# - genre_cross: user_genre_pref_embedding x target_genre_embedding (512)\n# - prod2vec_cross: read_series_avg_prod2vec_emb x target_prod2vec_emb (16)\n# CROSS_FEATURES_TO_SIZE = {\n#     \"genre_cross\": EMBEDDING_COLUMNS_TO_SIZE[\"user_genre_pref_embedding\"],\n#     \"prod2vec_cross\": EMBEDDING_COLUMNS_TO_SIZE[\"read_series_avg_prod2vec_emb\"],\n# }\n\nSERIES_COLUMNS = [\"target_series_id\", \"target_author_id\", \"target_rating_count\", \"target_average_rating\", \"target_genre_embedding\", \"target_prod2vec_emb\"]\nUSER_COLUMNS = [\"user_id\", \"gender\", \"age_days\", \"if_user_genre_pref_embedding_present\",  \"user_genre_pref_embedding\", \"read_series_avg_genre_emb\", \"read_series_avg_prod2vec_emb\"]\n\nINT_COLUMNS = [\"user_id\", \"target_series_id\", \"target_author_id\", \"gender\", \"if_user_genre_pref_embedding_present\"]\nFLOAT_COLUMNS = [\"age_days\", \"target_rating_count\", \"target_average_rating\"]\nFLOAT_ARR_COLUMNS = [\"target_prod2vec_emb\", \"read_series_avg_prod2vec_emb\", \"target_genre_embedding\", \"user_genre_pref_embedding\", \"read_series_avg_genre_emb\"]\n\n            \nCOL_TO_EMB_DIM = {\n    \"user_id\": 16,\n    \"gender\": 4,\n    \"target_series_id\": 16,\n    \"target_author_id\": 16,\n}\n\nTOTAL_INPUT_DIM = (\n    sum(COL_TO_EMB_DIM.values())\n    + sum(EMBEDDING_COLUMNS_TO_SIZE.values())\n    # + sum(CROSS_FEATURES_TO_SIZE.values())\n    + 4  # continuous features: target_average_rating, age, flag, rating_count\n)\n\n# TOTAL_INPUT_DIM_WITH_COSINE = TOTAL_INPUT_DIM + 3  - 16# three cosine features\nTOTAL_INPUT_DIM_WITH_COSINE = 251\n\nMLP_LAYERS = [512, 64, 16]\nDROPOUT_RATE = 0.1\n    \nNEG_PER_POS = 7\nNEG_PER_POS_TEST = 7\nTOTAL_EPOCHS = 20\nLEARNING_RATE = 0.001\n\nTRAIN_BATCH_SIZE = 2**10\nTEST_BATCH_SIZE = 2**10\n\nTRAIN_TEST_SPLIT_RATIO = 0.8\n\n_cpu_count = os.cpu_count() or 0\nNUM_WORKER_TRAIN_DATALOADER = max(0, _cpu_count - 2)\nNUM_WORKER_TEST_DATALOADER = max(0, _cpu_count - 2)\n\nSHARD_ROWS = 10000\n\n# Weights & Biases (wandb) - optional experiment tracking.\n# Enable by exporting `WANDB_ENABLED=1` and having `wandb` installed in your env.\nWANDB_ENABLED = _env_flag(\"WANDB_ENABLED\", default=False)\nWANDB_PROJECT = os.environ.get(\"WANDB_PROJECT\", \"cold-ranker\")\nWANDB_ENTITY = os.environ.get(\"WANDB_ENTITY\") or None\nWANDB_RUN_NAME = os.environ.get(\"WANDB_RUN_NAME\") or None\nWANDB_TAGS = [t.strip() for t in os.environ.get(\"WANDB_TAGS\", \"\").split(\",\") if t.strip()] or None\nWANDB_MODE = os.environ.get(\"WANDB_MODE\") or None  # \"online\" | \"offline\" | \"disabled\"\nWANDB_DIR = os.environ.get(\"WANDB_DIR\") or None\nWANDB_LOG_EVERY_N_STEPS = int(os.environ.get(\"WANDB_LOG_EVERY_N_STEPS\", \"100\"))\nWANDB_WATCH_MODEL = _env_flag(\"WANDB_WATCH_MODEL\", default=False)\nWANDB_WATCH_LOG = os.environ.get(\"WANDB_WATCH_LOG\", \"gradients\")  # \"gradients\" | \"parameters\" | \"all\"\nWANDB_WATCH_LOG_FREQ = int(os.environ.get(\"WANDB_WATCH_LOG_FREQ\", \"200\"))\n\n## Open tabs:\n- config.py: col/src/config/config.py\n- model.py: col/src/model/model.py\n- training.py: col/src/model/training.py\n- ctr_ranker.py: col/src/ctr_ranker.py\n- driver.py: col/src/driver.py\n\n## My request for Codex:\n(nik_gpu) ubuntu@ip-192-168-1-56:~/nikhil/cold-ranker/col/src$ python driver.py\nDevice: cuda\nData loaded from ../../data/bn/cat_columns_to_mapper.pkl\nwandb: Currently logged in as: shaw-nikhil05 to https://api.wandb.ai. Use `wandb login --relogin` to force relogin\nwandb: Tracking run with wandb version 0.23.0\nwandb: Run data is saved locally in /home/ubuntu/nikhil/cold-ranker/col/src/wandb/run-20260129_100031-37s6j63j\nwandb: Run `wandb offline` to turn off syncing.\nwandb: Syncing run debug-$(date +%F-%H%M)\nwandb: ⭐️ View project at https://wandb.ai/shaw-nikhil05/cold-ranker\nwandb: 🚀 View run at https://wandb.ai/shaw-nikhil05/cold-ranker/runs/37s6j63j\nEpoch 001 | train_loss=0.2741 | test_loss=0.2584 | elapsed=123.7s                                                           \nEpoch 002 | train_loss=0.2578 | test_loss=0.2583 | elapsed=123.0s                                                           \nEpoch 003 | train_loss=0.2548 | test_loss=0.2542 | elapsed=122.5s                                                           \nEpoch 004 | train_loss=0.2529 | test_loss=0.2589 | elapsed=122.2s                                                           \nEpoch 005 | train_loss=0.2516 | test_loss=0.2556 | elapsed=121.9s                                                           \nEpoch 006 | train_loss=0.2506 | test_loss=0.2535 | elapsed=121.5s                                                           \nEpoch 007 | train_loss=0.2497 | test_loss=0.2558 | elapsed=122.4s                                                           \nEpoch 008 | train_loss=0.2489 | test_loss=0.2591 | elapsed=123.0s \n\n\ntest loss not decreasing\n",
      "timestamp": "2026-01-29T10:18:14.297Z"
    },
    {
      "index": 6,
      "text": "Warning: apply_patch was requested via shell. Use the apply_patch tool instead of exec_command.",
      "timestamp": "2026-01-29T10:20:31.259Z"
    },
    {
      "index": 7,
      "text": "Warning: apply_patch was requested via shell. Use the apply_patch tool instead of exec_command.",
      "timestamp": "2026-01-29T10:22:10.948Z"
    },
    {
      "index": 8,
      "text": "# Context from my IDE setup:\n\n## Active file: col/src/config/config.py\n\n## Open tabs:\n- config.py: col/src/config/config.py\n- model.py: col/src/model/model.py\n- training.py: col/src/model/training.py\n- ctr_ranker.py: col/src/ctr_ranker.py\n- driver.py: col/src/driver.py\n\n## My request for Codex:\n(nik_gpu) ubuntu@ip-192-168-1-56:~/nikhil/cold-ranker/col/src$ python driver.py\nDevice: cuda\nData loaded from ../../data/bn/cat_columns_to_mapper.pkl\nwandb: Currently logged in as: shaw-nikhil05 to https://api.wandb.ai. Use `wandb login --relogin` to force relogin\nwandb: Tracking run with wandb version 0.23.0\nwandb: Run data is saved locally in /home/ubuntu/nikhil/cold-ranker/col/src/wandb/run-20260129_102508-srdhrd74\nwandb: Run `wandb offline` to turn off syncing.\nwandb: Syncing run debug-$(date +%F-%H%M)\nwandb: ⭐️ View project at https://wandb.ai/shaw-nikhil05/cold-ranker\nwandb: 🚀 View run at https://wandb.ai/shaw-nikhil05/cold-ranker/runs/srdhrd74\nEpoch 001 | train_loss=0.2741 | test_loss=0.2585 | elapsed=152.7s                                       \nEpoch 002 | train_loss=0.2578 | test_loss=0.2584 | elapsed=121.6s                                       \nEpoch 003 | train_loss=0.2548 | test_loss=0.2543 | elapsed=121.9s                                       \nEpoch 004 | train_loss=0.2529 | test_loss=0.2590 | elapsed=149.4s   \n",
      "timestamp": "2026-01-29T10:34:24.498Z"
    },
    {
      "index": 9,
      "text": "Warning: apply_patch was requested via shell. Use the apply_patch tool instead of exec_command.",
      "timestamp": "2026-01-29T10:35:56.590Z"
    },
    {
      "index": 10,
      "text": "Warning: apply_patch was requested via shell. Use the apply_patch tool instead of exec_command.",
      "timestamp": "2026-01-29T10:36:13.694Z"
    },
    {
      "index": 11,
      "text": "Warning: apply_patch was requested via shell. Use the apply_patch tool instead of exec_command.",
      "timestamp": "2026-01-29T10:37:08.316Z"
    },
    {
      "index": 12,
      "text": "Warning: apply_patch was requested via shell. Use the apply_patch tool instead of exec_command.",
      "timestamp": "2026-01-29T10:37:17.624Z"
    },
    {
      "index": 13,
      "text": "Warning: apply_patch was requested via shell. Use the apply_patch tool instead of exec_command.",
      "timestamp": "2026-01-29T10:37:27.046Z"
    },
    {
      "index": 14,
      "text": "Warning: apply_patch was requested via shell. Use the apply_patch tool instead of exec_command.",
      "timestamp": "2026-01-29T10:37:36.082Z"
    }
  ],
  "assistant_messages": [
    {
      "index": 0,
      "text": "I’ll quickly scan the training/eval code and config to pinpoint why the test loss is flat.",
      "timestamp": "2026-01-29T10:18:26.767Z"
    },
    {
      "index": 1,
      "text": "Next, I’ll add a deterministic option to the negative sampler and use a fixed-seed sampler for the test loader so evaluation is stable across epochs.",
      "timestamp": "2026-01-29T10:20:30.384Z"
    },
    {
      "index": 2,
      "text": "I’m going to patch the training loop to use a deterministic negative sampler for the test loader (recreated each epoch) so evaluation is consistent.",
      "timestamp": "2026-01-29T10:22:05.735Z"
    },
    {
      "index": 3,
      "text": "I inspected the data pipeline and training loop; the test dataloader was re‑sampling negatives each epoch, so the eval distribution changed and made test loss bounce.\n\nWhat I changed\n- col/src/model/sampler.py: added an optional `seed` to `PopularitySampler` and used a per-instance RNG for deterministic sampling.\n- col/src/model/training.py: \n  - use `train_sampler` for training and create a fixed‑seed `test_sampler` (`seed=42`) for evaluation.\n  - rebuild the test dataloader each epoch with `shuffle_shards=False` and `shuffle_within_shard=False` so eval is identical across epochs.\n\nWhy this helps\n- Previously, every epoch drew new negative samples for test, so BCE loss varied even if the model improved slightly. With fixed negatives and no shuffling, the reported test loss becomes stable and trends reflect model changes, not sampling noise.\n\nHow to verify\n- Re-run: `python col/src/driver.py`.\n- In W&B, compare `test/loss_epoch` across epochs; it should now be much steadier. Also watch `test/pos_prob_mean` > `test/neg_prob_mean` widening as the model improves.\n\nQuick nits you may want fixed next\n- `driver.py`: `WANDB_RUN_NAME` is literally “debug-$(date +%F-%H%M)”. Use Python time formatting instead, and fix the env name typo:\n  - `WANDB_LOG_EVERY_N_STEPS` (plural) not `WANDB_LOG_EVERY_N_STEP`.\n  - If you want, I can patch `driver.py` to set `WANDB_RUN_NAME` via `datetime.now().strftime(...)`.\n- Consider logging AUC/PR-AUC for a threshold‑independent view of progress.\n\nSecurity note\n- `driver.py` contains plaintext API keys and DB credentials. I recommend moving to env vars and rotating these keys.\n\nWant me to apply the W&B name/typo fix and add an AUC metric to eval?",
      "timestamp": "2026-01-29T10:24:20.832Z"
    },
    {
      "index": 4,
      "text": "I’m going to add ReduceLROnPlateau + early stopping, gradient clipping, and fix the W&B env var and run name.",
      "timestamp": "2026-01-29T10:35:50.529Z"
    },
    {
      "index": 5,
      "text": "I’m going to grab line numbers for the updated code so I can point you to exact locations.",
      "timestamp": "2026-01-29T10:38:05.595Z"
    },
    {
      "index": 6,
      "text": "Here’s what I changed to stabilize and speed up convergence, plus how to verify quickly.\n\nWhat I changed\n- Deterministic test negatives\n  - `col/src/model/sampler.py:4-20`: added `seed` to `PopularitySampler` and an internal RNG for reproducible sampling.\n  - `col/src/model/training.py:337-434`: recreate the test dataloader each epoch with `seed=42`, `shuffle_shards=False`, `shuffle_within_shard=False`.\n- LR scheduling + early stop\n  - `col/src/config/config.py:83-90`: new knobs `GRAD_CLIP_NORM`, `LR_SCHED_*`, `EARLY_STOP_*` (env‑overridable).\n  - `col/src/model/training.py:288-297`: `ReduceLROnPlateau` on test loss when available, else train loss.\n  - `col/src/model/training.py:414-432`: early stopping with `EARLY_STOP_PATIENCE` and `EARLY_STOP_MIN_DELTA`.\n- Gradient clipping\n  - `col/src/model/training.py:130-131` and `146-147` around backprop: clip with `GRAD_CLIP_NORM` (default 5.0).\n- W&B cleanup\n  - `col/src/driver.py:2,14-16`: run name now `debug-YYYYMMDD-HHMM`; fixed env var to `WANDB_LOG_EVERY_N_STEPS`.\n\nHow to run\n- Just re-run: `python col/src/driver.py`.\n- Optional env tweaks:\n  - `GRAD_CLIP_NORM=1.0` to be stricter.\n  - `EARLY_STOP_PATIENCE=4` if your curve is noisy.\n  - `LR_SCHED_FACTOR=0.5 LR_SCHED_PATIENCE=2` for quicker LR drops.\n\nWhat to expect\n- Test loss should now be evaluated on an identical distribution every epoch. You should see steadier `test/loss_epoch` and, if overfitting starts, early stopping will trigger with a message like “Early stopping at epoch N (best=…)”.\n- LR will drop when the monitored metric plateaus; step-wise LR is logged in W&B at step-level as `train/lr`.\n\nQuick follow-ups I can add\n- Log ROC-AUC/PR-AUC on test for threshold‑free progress.\n- Save best checkpoint and restore on early stop.\n- Surface label ratio and pos/neg probs to W&B summary for sanity checks.\n\nNote\n- `col/src/driver.py` currently contains plaintext API keys/DB creds. Consider moving these to your shell env or a `.env` and rotate the keys.",
      "timestamp": "2026-01-29T10:38:39.886Z"
    }
  ],
  "tool_calls": [
    {
      "name": "shell",
      "input_summary": "{\"command\":[\"bash\",\"-lc\",\"ls -la\"],\"timeout_ms\":120000,\"workdir\":\"/home/ubuntu/nikhil/cold-ranker\"}",
      "output_summary": "{\"output\":\"\\nthread 'main' (4806) panicked at linux-sandbox/src/linux_run_main.rs:30:9:\\nerror running landlock: Sandbox(LandlockRestrict)\\nnote: run with `RUST_BACKTRACE=1` environment variable to di..."
    },
    {
      "name": "shell",
      "input_summary": "{\"command\":[\"bash\",\"-lc\",\"ls -la\"],\"justification\":\"Need to list repository files to inspect training/eval code to debug test loss issue.\",\"sandbox_permissions\":\"require_escalated\",\"timeout_ms\":120000...",
      "output_summary": "{\"output\":\"total 16\\ndrwxrwxr-x 4 ubuntu ubuntu 4096 Jan 28 15:44 .\\ndrwxrwxr-x 8 ubuntu ubuntu 4096 Jan 28 15:40 ..\\ndrwxrwxr-x 4 ubuntu ubuntu 4096 Jan 28 15:44 col\\ndrwxrwxr-x 4 ubuntu ubuntu 4096 ..."
    },
    {
      "name": "shell",
      "input_summary": "{\"command\":[\"bash\",\"-lc\",\"ls -la col/src && echo '---' && rg --files col/src | sed -n '1,200p'\"],\"justification\":\"Need to view project source files to trace how test loss is computed and where MODE is...",
      "output_summary": "{\"output\":\"total 1508\\ndrwxrwxr-x  8 ubuntu ubuntu    4096 Jan 28 16:08 .\\ndrwxrwxr-x  4 ubuntu ubuntu    4096 Jan 28 15:44 ..\\n-rw-rw-r--  1 ubuntu ubuntu 1279926 Jan 28 15:41 BENGALI_prod2vec_emb.pa..."
    },
    {
      "name": "shell",
      "input_summary": "{\"command\":[\"bash\",\"-lc\",\"sed -n '1,200p' col/src/driver.py\"],\"timeout_ms\":120000,\"workdir\":\"/home/ubuntu/nikhil/cold-ranker\"}",
      "output_summary": "{\"output\":\"\\nthread 'main' (4846) panicked at linux-sandbox/src/linux_run_main.rs:30:9:\\nerror running landlock: Sandbox(LandlockRestrict)\\nnote: run with `RUST_BACKTRACE=1` environment variable to di..."
    },
    {
      "name": "shell",
      "input_summary": "{\"command\":[\"bash\",\"-lc\",\"sed -n '1,200p' col/src/driver.py\"],\"justification\":\"Need to examine training driver to see evaluation and test split behavior.\",\"sandbox_permissions\":\"require_escalated\",\"ti...",
      "output_summary": "{\"output\":\"import os\\nos.environ[\\\"AWS_DEFAULT_REGION\\\"] = \\\"ap-south-1\\\"\\nos.environ[\\\"OPENAI_API_KEY\\\"] = \\\"sk-proj-qRoswT4wSHS3R4i7ZcIbP3FW5XT0xI_WFPd_ZkNr0OXrihudRu2ZzXthe8u_9BvNahk8fsEPhOT3BlbkFJ..."
    },
    {
      "name": "shell",
      "input_summary": "{\"command\":[\"bash\",\"-lc\",\"sed -n '1,240p' col/src/model/training.py\"]}",
      "output_summary": "{\"output\":\"\\nthread 'main' (4860) panicked at linux-sandbox/src/linux_run_main.rs:30:9:\\nerror running landlock: Sandbox(LandlockRestrict)\\nnote: run with `RUST_BACKTRACE=1` environment variable to di..."
    },
    {
      "name": "shell",
      "input_summary": "{\"command\":[\"bash\",\"-lc\",\"sed -n '1,240p' col/src/model/training.py\"],\"justification\":\"Inspect training loop, eval, data splits to diagnose non-decreasing test loss.\",\"sandbox_permissions\":\"require_es...",
      "output_summary": "{\"output\":\"import os\\nimport math\\nfrom model.sampler import PopularitySampler\\nimport time\\nfrom typing import Optional\\nfrom typing import Dict, Tuple\\nimport numpy as np\\nfrom torch import nn\\nimpo..."
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    {
      "name": "shell",
      "input_summary": "{\"command\":[\"bash\",\"-lc\",\"sed -n '240,560p' col/src/model/training.py\"],\"justification\":\"Continue reading the training driver for test loader and evaluation.\",\"sandbox_permissions\":\"require_escalated\"...",
      "output_summary": "{\"output\":\"        neg_per_pos = NEG_PER_POS,\\n        batch_size = TRAIN_BATCH_SIZE,\\n        num_worker_dataloader = NUM_WORKER_TRAIN_DATALOADER,\\n        pin_memory = True,\\n        shuffle_shards ..."
    },
    {
      "name": "shell",
      "input_summary": "{\"command\":[\"bash\",\"-lc\",\"sed -n '1,260p' col/src/model/dataset.py\"],\"justification\":\"Need to inspect how negatives are sampled in get_sharded_iterable_dataloader and whether test loader resamples acr...",
      "output_summary": "{\"output\":\"Total output lines: 210\\n\\nimport os\\nimport json\\nimport numpy as np\\nimport torch\\nfrom torch.utils.data import DataLoader, IterableDataset\\n\\nfrom config.config import EMBEDDING_COLUMNS_..."
    },
    {
      "name": "shell",
      "input_summary": "{\"command\":[\"bash\",\"-lc\",\"nl -ba col/src/model/dataset.py | sed -n '1,260p'\"],\"justification\":\"View dataset logic completely without truncation.\",\"sandbox_permissions\":\"require_escalated\",\"timeout_ms\"...",
      "output_summary": "{\"output\":\"Total output lines: 210\\n\\n     1\\timport os\\n     2\\timport json\\n     3\\timport numpy as np\\n     4\\timport torch\\n     5\\tfrom torch.utils.data import DataLoader, IterableDataset\\n     6..."
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    {
      "name": "shell",
      "input_summary": "{\"command\":[\"bash\",\"-lc\",\"sed -n '1,220p' col/src/model/sampler.py\"],\"justification\":\"Understand how PopularitySampler samples negatives; check if test negatives are dynamic and if there’s leakage.\",\"...",
      "output_summary": "{\"output\":\"import numpy as np\\n\\n\\nclass PopularitySampler:\\n    def __init__(self, vocab_arr, positive_series_arr):\\n        values, counts = np.unique(positive_series_arr, return_counts=True)\\n     ..."
    },
    {
      "name": "shell",
      "input_summary": "{\"command\":[\"bash\",\"-lc\",\"sed -n '1,260p' col/src/model/model.py\"],\"justification\":\"Inspect model architecture and how input dimension is used; check for mismatches that could affect training/test res...",
      "output_summary": "{\"output\":\"import torch\\nfrom torch import nn\\nfrom torch.nn import functional as F\\n\\nclass CTRModelWithCosineSignals(nn.Module):\\n    def __init__(\\n        self,\\n        input_dim,\\n        mlp_la..."
    },
    {
      "name": "shell",
      "input_summary": "{\"command\":[\"bash\",\"-lc\",\"applypatch << 'PATCH'\\n*** Begin Patch\\n*** Update File: col/src/model/sampler.py\\n@@\\n-class PopularitySampler:\\n-    def __init__(self, vocab_arr, positive_series_arr):\\n- ...",
      "output_summary": "{\"output\":\"Success. Updated the following files:\\nM col/src/model/sampler.py\\n\",\"metadata\":{\"exit_code\":0,\"duration_seconds\":0.0}}"
    },
    {
      "name": "shell",
      "input_summary": "{\"command\":[\"bash\",\"-lc\",\"applypatch << 'PATCH'\\n*** Begin Patch\\n*** Update File: col/src/model/training.py\\n@@\\n-    sampler = PopularitySampler(\\n-        vocab_arr = np.arange(target_series_id_voc...",
      "output_summary": "{\"output\":\"Success. Updated the following files:\\nM col/src/model/training.py\\n\",\"metadata\":{\"exit_code\":0,\"duration_seconds\":0.0}}"
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    {
      "name": "shell",
      "input_summary": "{\"command\":[\"bash\",\"-lc\",\"sed -n '1,260p' col/src/utils/wandb_utils.py\"],\"timeout_ms\":120000,\"workdir\":\"/home/ubuntu/nikhil/cold-ranker\"}",
      "output_summary": "{\"output\":\"\\nthread 'main' (5049) panicked at linux-sandbox/src/linux_run_main.rs:30:9:\\nerror running landlock: Sandbox(LandlockRestrict)\\nnote: run with `RUST_BACKTRACE=1` environment variable to di..."
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    {
      "name": "shell",
      "input_summary": "{\"command\":[\"bash\",\"-lc\",\"sed -n '1,260p' col/src/utils/wandb_utils.py\"],\"justification\":\"Check wandb utils for step logging semantics.\",\"sandbox_permissions\":\"require_escalated\",\"timeout_ms\":120000,\"...",
      "output_summary": "{\"output\":\"from typing import Any, Dict, Optional, Tuple\\n\\n\\ndef maybe_init_wandb(\\n    enabled: bool,\\n    project: str,\\n    config: Dict[str, Any],\\n    entity: Optional[str] = None,\\n    name: Op..."
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    {
      "name": "update_plan",
      "input_summary": "{\"plan\":[{\"status\":\"in_progress\",\"step\":\"Add LR scheduler + early stop\"},{\"status\":\"pending\",\"step\":\"Add gradient clipping\"},{\"status\":\"pending\",\"step\":\"Fix W&B run name/env var\"},{\"status\":\"pending\",...",
      "output_summary": "Plan updated"
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    {
      "name": "shell",
      "input_summary": "{\"command\":[\"bash\",\"-lc\",\"applypatch << 'PATCH'\\n*** Begin Patch\\n*** Update File: col/src/config/config.py\\n@@\\n MODE='test'\\n@@\\n LEARNING_RATE = 0.001\\n+\\n+# Training stability helpers\\n+GRAD_CLIP_...",
      "output_summary": "{\"output\":\"Success. Updated the following files:\\nM col/src/config/config.py\\n\",\"metadata\":{\"exit_code\":0,\"duration_seconds\":0.0}}"
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    {
      "name": "shell",
      "input_summary": "{\"command\":[\"bash\",\"-lc\",\"applypatch << 'PATCH'\\n*** Begin Patch\\n*** Update File: col/src/model/training.py\\n@@\\n from config.config import (\\n@@\\n     TRAIN_BATCH_SIZE,\\n+    GRAD_CLIP_NORM,\\n+    L...",
      "output_summary": "{\"output\":\"Success. Updated the following files:\\nM col/src/model/training.py\\n\",\"metadata\":{\"exit_code\":0,\"duration_seconds\":0.0}}"
    },
    {
      "name": "shell",
      "input_summary": "{\"command\":[\"bash\",\"-lc\",\"applypatch << 'PATCH'\\n*** Begin Patch\\n*** Update File: col/src/model/training.py\\n@@\\n-    optimizer = torch.optim.AdamW(model.parameters(), lr=LEARNING_RATE, weight_decay=...",
      "output_summary": "{\"output\":\"Success. Updated the following files:\\nM col/src/model/training.py\\n\",\"metadata\":{\"exit_code\":0,\"duration_seconds\":0.0}}"
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    {
      "name": "shell",
      "input_summary": "{\"command\":[\"bash\",\"-lc\",\"applypatch << 'PATCH'\\n*** Begin Patch\\n*** Update File: col/src/model/training.py\\n@@\\n def train_one_epoch(\\n@@\\n ) -> Tuple[float, Dict[str, float], int]:\\n@@\\n-          ...",
      "output_summary": "{\"output\":\"Success. Updated the following files:\\nM col/src/model/training.py\\n\",\"metadata\":{\"exit_code\":0,\"duration_seconds\":0.0}}"
    },
    {
      "name": "shell",
      "input_summary": "{\"command\":[\"bash\",\"-lc\",\"applypatch << 'PATCH'\\n*** Begin Patch\\n*** Update File: col/src/model/training.py\\n@@\\n-    for epoch in range(1, TOTAL_EPOCHS + 1):\\n+    best_metric = float(\\\"inf\\\")\\n+   ...",
      "output_summary": "{\"output\":\"Success. Updated the following files:\\nM col/src/model/training.py\\n\",\"metadata\":{\"exit_code\":0,\"duration_seconds\":0.0}}"
    },
    {
      "name": "shell",
      "input_summary": "{\"command\":[\"bash\",\"-lc\",\"applypatch << 'PATCH'\\n*** Begin Patch\\n*** Update File: col/src/driver.py\\n@@\\n-import os\\n+import os\\n+from datetime import datetime\\n@@\\n-os.environ['WANDB_RUN_NAME']=f\\\"d...",
      "output_summary": "{\"output\":\"Success. Updated the following files:\\nM col/src/driver.py\\n\",\"metadata\":{\"exit_code\":0,\"duration_seconds\":0.0}}"
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      "name": "shell",
      "input_summary": "{\"command\":[\"bash\",\"-lc\",\"nl -ba col/src/model/training.py | sed -n '1,200p'\"],\"timeout_ms\":120000,\"workdir\":\"/home/ubuntu/nikhil/cold-ranker\"}",
      "output_summary": "{\"output\":\"\\nthread 'main' (7858) panicked at linux-sandbox/src/linux_run_main.rs:30:9:\\nerror running landlock: Sandbox(LandlockRestrict)\\nnote: run with `RUST_BACKTRACE=1` environment variable to di..."
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      "name": "shell",
      "input_summary": "{\"command\":[\"bash\",\"-lc\",\"nl -ba col/src/model/training.py | sed -n '1,320p'\"],\"justification\":\"List training.py with line numbers for precise references.\",\"sandbox_permissions\":\"require_escalated\",\"t...",
      "output_summary": "{\"output\":\"Total output lines: 325\\n\\n     1\\timport os\\n     2\\timport math\\n     3\\tfrom model.sampler import PopularitySampler\\n     4\\timport time\\n     5\\tfrom typing import Optional\\n     6\\tfro..."
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      "name": "shell",
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      "output_summary": "{\"output\":\"\\nthread 'main' (7880) panicked at linux-sandbox/src/linux_run_main.rs:30:9:\\nerror running landlock: Sandbox(LandlockRestrict)\\nnote: run with `RUST_BACKTRACE=1` environment variable to di..."
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      "name": "shell",
      "input_summary": "{\"command\":[\"bash\",\"-lc\",\"nl -ba col/src/model/training.py | sed -n '320,999p'\"],\"justification\":\"Continue listing to capture end of file with early stopping block.\",\"sandbox_permissions\":\"require_esc...",
      "output_summary": "{\"output\":\"   320\\t            \\\"dropout_rate\\\": float(DROPOUT_RATE),\\n   321\\t            \\\"col_to_emb_dim\\\": COL_TO_EMB_DIM,\\n   322\\t            \\\"total_input_dim\\\": int(TOTAL_INPUT_DIM),\\n   323\\t..."
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    {
      "name": "shell",
      "input_summary": "{\"command\":[\"bash\",\"-lc\",\"nl -ba col/src/model/sampler.py | sed -n '1,140p'\"],\"timeout_ms\":120000,\"workdir\":\"/home/ubuntu/nikhil/cold-ranker\"}",
      "output_summary": "{\"output\":\"\\nthread 'main' (7902) panicked at linux-sandbox/src/linux_run_main.rs:30:9:\\nerror running landlock: Sandbox(LandlockRestrict)\\nnote: run with `RUST_BACKTRACE=1` environment variable to di..."
    },
    {
      "name": "shell",
      "input_summary": "{\"command\":[\"bash\",\"-lc\",\"nl -ba col/src/model/sampler.py | sed -n '1,200p'\"],\"justification\":\"List sampler with line numbers.\",\"sandbox_permissions\":\"require_escalated\",\"timeout_ms\":120000,\"workdir\":...",
      "output_summary": "{\"output\":\"     1\\timport numpy as np\\n     2\\t\\n     3\\t\\n     4\\tclass PopularitySampler:\\n     5\\t    def __init__(self, vocab_arr, positive_series_arr, seed=None):\\n     6\\t        values, counts ..."
    },
    {
      "name": "shell",
      "input_summary": "{\"command\":[\"bash\",\"-lc\",\"nl -ba col/src/driver.py | sed -n '1,120p'\"],\"timeout_ms\":120000,\"workdir\":\"/home/ubuntu/nikhil/cold-ranker\"}",
      "output_summary": "{\"output\":\"\\nthread 'main' (7924) panicked at linux-sandbox/src/linux_run_main.rs:30:9:\\nerror running landlock: Sandbox(LandlockRestrict)\\nnote: run with `RUST_BACKTRACE=1` environment variable to di..."
    },
    {
      "name": "shell",
      "input_summary": "{\"command\":[\"bash\",\"-lc\",\"nl -ba col/src/driver.py | sed -n '1,160p'\"],\"justification\":\"List driver for env var fixes lines.\",\"sandbox_permissions\":\"require_escalated\",\"timeout_ms\":120000,\"workdir\":\"/...",
      "output_summary": "{\"output\":\"     1\\timport os\\n     2\\tfrom datetime import datetime\\n     3\\tos.environ[\\\"AWS_DEFAULT_REGION\\\"] = \\\"ap-south-1\\\"\\n     4\\tos.environ[\\\"OPENAI_API_KEY\\\"] = \\\"sk-proj-qRoswT4wSHS3R4i7ZcI..."
    },
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      "name": "shell",
      "input_summary": "{\"command\":[\"bash\",\"-lc\",\"nl -ba col/src/config/config.py | sed -n '1,160p'\"],\"timeout_ms\":120000,\"workdir\":\"/home/ubuntu/nikhil/cold-ranker\"}",
      "output_summary": "{\"output\":\"\\nthread 'main' (7946) panicked at linux-sandbox/src/linux_run_main.rs:30:9:\\nerror running landlock: Sandbox(LandlockRestrict)\\nnote: run with `RUST_BACKTRACE=1` environment variable to di..."
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    {
      "name": "shell",
      "input_summary": "{\"command\":[\"bash\",\"-lc\",\"nl -ba col/src/config/config.py | sed -n '1,220p'\"],\"justification\":\"Show config with new constants lines.\",\"sandbox_permissions\":\"require_escalated\",\"timeout_ms\":120000,\"wor...",
      "output_summary": "{\"output\":\"     1\\timport os\\n     2\\t\\n     3\\tLANGUAGE=\\\"bengali\\\"\\n     4\\tLANGUAGE_KEY=\\\"bn\\\"\\n     5\\t\\n     6\\tMODE='test'\\n     7\\t\\n     8\\tdef _env_flag(name: str, default: bool = False) -> b..."
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    {
      "name": "update_plan",
      "input_summary": "{\"plan\":[{\"status\":\"completed\",\"step\":\"Add LR scheduler + early stop\"},{\"status\":\"completed\",\"step\":\"Add gradient clipping\"},{\"status\":\"completed\",\"step\":\"Fix W&B run name/env var\"},{\"status\":\"in_prog...",
      "output_summary": "Plan updated"
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  ],
  "turn_count": 15,
  "files_touched": [],
  "summary_from_context": "auto",
  "metrics": {
    "outcome": "resolved",
    "session_type": "bug-fix",
    "iteration_count": 15,
    "struggle_score": 6,
    "prompt_specificity": 4,
    "time_of_day": "morning"
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}