{
  "filename": "rollout-2026-01-29T11-47-50-019c0994-c5b5-7340-a58c-ce993a3e9f9d.jsonl",
  "date": "2026-01-29",
  "session_id": "019c0994-c5b5-7340-a58c-ce993a3e9f9d",
  "first_timestamp": "2026-01-29T11:47:50.839Z",
  "last_timestamp": "2026-01-29T12:05:58.903Z",
  "duration_seconds": 1088.1,
  "model": "gpt-5.2-codex",
  "cli_version": "0.92.0",
  "approval_policy": "on-request",
  "effort": "xhigh",
  "raw_event_count": 124,
  "user_messages": [
    {
      "index": 0,
      "text": "<permissions instructions>Filesystem sandboxing defines which files can be read or written. `sandbox_mode` is `read-only`: The sandbox only permits reading files. 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</permissions instructions>",
      "timestamp": "2026-01-29T11:47:50.839Z"
    },
    {
      "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": "2026-01-29T11:47:50.839Z"
    },
    {
      "index": 2,
      "text": "<environment_context>\n  <cwd>/home/ubuntu/nikhil/cold-ranker</cwd>\n  <shell>bash</shell>\n</environment_context>",
      "timestamp": "2026-01-29T11:47:50.839Z"
    },
    {
      "index": 3,
      "text": "# Context from my IDE setup:\n\n## Active file: col/src/model/sampler.py\n\n## Open tabs:\n- sampler.py: col/src/model/sampler.py\n- training.py: col/src/model/training.py\n- config.py: col/src/config/config.py\n- model.py: col/src/model/model.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_112744-qsjspn6g\nwandb: Run `wandb offline` to turn off syncing.\nwandb: Syncing run debug-20260129-1127\nwandb: ⭐️ View project at https://wandb.ai/shaw-nikhil05/cold-ranker\nwandb: 🚀 View run at https://wandb.ai/shaw-nikhil05/cold-ranker/runs/qsjspn6g\nwandb: Detected [openai] in use.\nwandb: Use W&B Weave for improved LLM call tracing. Install Weave with `pip install weave` then add `import weave` to the top of your script.\nwandb: For more information, check out the docs at: https://weave-docs.wandb.ai/\nEpoch 001 | train_loss=0.2531 | test_loss=0.2499 | elapsed=165.2s                      \nEpoch 002 | train_loss=0.2458 | test_loss=0.2480 | elapsed=163.0s                      \nEpoch 003 | train_loss=0.2437 | test_loss=0.2494 | elapsed=162.8s                      \nEpoch 004 | train_loss=0.2424 | test_loss=0.2482 | elapsed=164.8s                      \nEpoch 005 | train_loss=0.2415 | test_loss=0.2480 | elapsed=162.8s                      \nEpoch 006 | train_loss=0.2405 | test_loss=0.2531 | elapsed=164.4s                      \nEpoch 007 | train_loss=0.2398 | test_loss=0.2503 | elapsed=188.8s\n\n\nwhy is my test loss not decreasing?\n",
      "timestamp": "2026-01-29T11:47:50.906Z"
    },
    {
      "index": 4,
      "text": "# Context from my IDE setup:\n\n## Active file: col/src/model/model.py\n\n## Open tabs:\n- model.py: col/src/model/model.py\n- sampler.py: col/src/model/sampler.py\n- training.py: col/src/model/training.py\n- config.py: col/src/config/config.py\n- driver.py: col/src/driver.py\n\n## My request for Codex:\n.\n",
      "timestamp": "2026-01-29T12:05:53.043Z"
    }
  ],
  "assistant_messages": [
    {
      "index": 0,
      "text": "I’ll scan the training loop, evaluation code, and sampler to spot common causes like not setting `eval()` or misconfigured sampling.",
      "timestamp": "2026-01-29T11:48:06.530Z"
    },
    {
      "index": 1,
      "text": "I’ve reviewed the training loop, evaluation path, dataloaders, and the sampler to pinpoint likely causes. Updating the plan to reflect findings and next actions.",
      "timestamp": "2026-01-29T11:52:08.086Z"
    },
    {
      "index": 2,
      "text": "Short answer: your eval is correct, but your data pipeline injects label noise and your model regularization/optimization choices likely cap generalization, so test loss hovers around a flat baseline despite small train-loss gains.\n\nWhat I verified\n- Eval mode is set properly and grads are off during test.\n  - `model.train(is_train)` flips to eval when `optimizer is None` (col/src/model/training.py:61–63), and the loop runs under `torch.set_grad_enabled(is_train)` (col/src/model/training.py:74), so dropout is disabled and BN uses running stats during test.\n- Test loader is deterministic across epochs (by design).\n  - You rebuild a test `PopularitySampler(seed=42)` every epoch (col/src/model/training.py:330–341) and disable shard/within-shard shuffling (col/src/model/training.py:344–351). That means you’re evaluating on essentially the same negatives every epoch. Flat test loss is therefore a sensitive signal of “no generalization improvement”.\n- MLP uses BatchNorm + Dropout between linears (col/src/model/model.py:36–53). This combo can create a noticeable train/eval gap if stats are noisy or batches aren’t representative.\n\nPrimary issue found: false negatives from the sampler\n- In the dataset, negatives are drawn globally and then concatenated with positives, without excluding the current positive series ID:\n  - Negatives: `neg_series_ids = self.sampler.sample(self.neg_per_pos * len(pos_series_ids))` (col/src/model/dataset.py:120–121).\n  - Labels: first N are 1 (positives), the rest are 0 (negatives) (col/src/model/dataset.py:126–129).\n- Because user features are tiled (col/src/model/dataset.py:112–119) and negatives are assigned in a flat sequence, it is possible for a negative assigned to user i to equal that user’s true positive series. That’s label noise (“false negatives”), which suppresses both train and test improvements and can pin test loss near a plateau.\n\nOther contributors\n- Distribution mismatch in negatives: the test negatives are drawn from a popularity distribution estimated on training positives (col/src/model/training.py:407, 331–337 and col/src/model/sampler.py:4–22). This can bias evaluation and limit the headroom for the test loss to improve.\n- BN sensitivity: BatchNorm running stats can differ from the eval distribution (especially with many concatenated features), keeping test loss flat despite slight train loss drops (col/src/model/model.py:41–44).\n- Aggressive regularization/optimizer: AdamW with `lr=1e-3` and `weight_decay=1e-3` (col/src/model/training.py:257) plus dropout 0.2 (col/src/config/config.py:55) may hold back generalization early.\n\nConcrete fixes (in order of impact)\n- Eliminate false negatives in sampling.\n  - Option A (simple): draw negatives per example and reject the positive ID until you get K distinct negatives for that example.\n    - Pseudocode inside col/src/model/dataset.py around 112–129:\n      - For each `pos_id` in the batch, sample `k=NEG_PER_POS` from the sampler; if any equals `pos_id`, resample those positions; then append to per-example list. Concatenate per-example negatives to align with the tiled user features.\n  - Option B (vectorized in the sampler): add `PopularitySampler.sample_excluding(exclude_ids, k)` that zeroes `p[exclude_id]`, renormalizes, and samples `k` per row; fill any invalid draws by resampling only conflicting positions.\n- Make test negatives reflect test distribution.\n  - Build `PopularitySampler` for test from test positives (use the test shards to compute `values, counts`), and keep `seed=42` for determinism. This avoids evaluating with a train-driven negative prior.\n- Soften BN/regularization to reduce train–test gap.\n  - Remove BatchNorm (replace with LayerNorm or none) in CTR MLPs, or at least test an ablation:\n    - In col/src/model/model.py, drop the `nn.BatchNorm1d(...)` layers and keep `Linear → ReLU → Dropout`.\n  - Lower regularization and/or LR to help generalization:\n    - Try `weight_decay=1e-4` and `lr=5e-4` in col/src/model/training.py:257.\n    - Add `ReduceLROnPlateau` on `test_loss` with patience 2–3 to escape plateaus.\n- Stabilize evaluation further\n  - Increase `NEG_PER_POS_TEST` (e.g., 15–50) to reduce variance in a pointwise BCE setup (col/src/config/config.py:59–67).\n  - Ensure `NUM_WORKER_TEST_DATALOADER` is modest (e.g., 0–2) so worker forking doesn’t complicate deterministic behavior (col/src/config/config.py:79–83).\n\nQuick sanity checks you can run now\n- Log the metrics you already compute to confirm behavior:\n  - `test/pos_prob_mean` and `test/neg_prob_mean` from train_one_epoch’s return (col/src/model/training.py:167–175). You’ll likely see them barely diverging, consistent with flat loss.\n- One-epoch A/B:\n  - A: current setup.\n  - B: BN removed and `NEG_PER_POS_TEST=32`.\n  - Compare test loss after epoch 1; B should drop more.\n- Overfit a tiny training shard:\n  - Train on a single shard for 1–2 epochs; test on that same shard with deterministic negatives. If test loss doesn’t drop sharply, label noise is the culprit.\n\nIf you want, I can draft the precise code changes to:\n- Add `sample_excluding` to the sampler and update the dataset to use it.\n- Swap BN out of the MLP.\n- Add an LR scheduler and the config toggles for the above.",
      "timestamp": "2026-01-29T11:52:36.682Z"
    },
    {
      "index": 3,
      "text": "Got it — how would you like to proceed? I can implement the sampler fix (avoid false negatives), remove BatchNorm, or add a scheduler.",
      "timestamp": "2026-01-29T12:05:58.853Z"
    }
  ],
  "tool_calls": [
    {
      "name": "update_plan",
      "input_summary": "{\"plan\":[{\"status\":\"in_progress\",\"step\":\"Inspect training/eval loop\"},{\"status\":\"pending\",\"step\":\"Check dataloaders and sampler\"},{\"status\":\"pending\",\"step\":\"Review model modules (dropout/BN)\"},{\"stat...",
      "output_summary": "Plan updated"
    },
    {
      "name": "shell",
      "input_summary": "{\"command\":[\"bash\",\"-lc\",\"rg -n \\\"def train|def evaluate|model\\\\.train\\\\(|model\\\\.eval\\\\(|no_grad|DataLoader|Sampler|Weighted|RandomSampler|BatchNorm|Dropout|seed|manual_seed|cudnn|optimizer|scheduler...",
      "output_summary": "{\"output\":\"\\nthread 'main' (5439) 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\",\"rg -n \\\"def train|def evaluate|model\\\\.train\\\\(|model\\\\.eval\\\\(|no_grad|DataLoader|Sampler|Weighted|RandomSampler|BatchNorm|Dropout|seed|manual_seed|cudnn|optimizer|scheduler...",
      "output_summary": "{\"output\":\"col/src/model/dataset.py:5:from torch.utils.data import DataLoader, IterableDataset\\ncol/src/model/dataset.py:71:            rng = np.random.default_rng(worker_info.seed)\\ncol/src/model/dat..."
    },
    {
      "name": "shell",
      "input_summary": "{\"command\":[\"bash\",\"-lc\",\"sed -n '1,240p' col/src/model/training.py\"],\"timeout_ms\":120000,\"workdir\":\"/home/ubuntu/nikhil/cold-ranker\"}",
      "output_summary": "{\"output\":\"\\nthread 'main' (5665) 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\":\"Read training loop implementation to verify eval mode and dataloaders.\",\"sandbox_permissions\":\"require_escalated\"...",
      "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..."
    },
    {
      "name": "shell",
      "input_summary": "{\"command\":[\"bash\",\"-lc\",\"sed -n '240,520p' col/src/model/training.py\"],\"justification\":\"Continue reading training driver and evaluation parts.\",\"sandbox_permissions\":\"require_escalated\",\"timeout_ms\":...",
      "output_summary": "{\"output\":\"            total_test_batches = 0\\n            for test_shard_path in test_shard_paths:\\n                ints_data = np.load(f\\\"{test_shard_path}/ints.npy\\\")\\n                total_test_ba..."
    },
    {
      "name": "shell",
      "input_summary": "{\"command\":[\"bash\",\"-lc\",\"sed -n '1,220p' col/src/model/sampler.py\"],\"justification\":\"Review negative sampler behavior and randomness seeding.\",\"sandbox_permissions\":\"require_escalated\",\"timeout_ms\":1...",
      "output_summary": "{\"output\":\"import numpy as np\\n\\n\\nclass PopularitySampler:\\n    def __init__(self, vocab_arr, positive_series_arr, seed=None):\\n        values, counts = np.unique(positive_series_arr, return_counts=T..."
    },
    {
      "name": "shell",
      "input_summary": "{\"command\":[\"bash\",\"-lc\",\"sed -n '1,260p' col/src/model/dataset.py\"],\"justification\":\"Understand how dataset constructs batches and applies sampler for negatives.\",\"sandbox_permissions\":\"require_escal...",
      "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 '1,240p'\"],\"justification\":\"View full dataset file with line numbers for precise reference.\",\"sandbox_permissions\":\"require_escalated...",
      "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..."
    },
    {
      "name": "shell",
      "input_summary": "{\"command\":[\"bash\",\"-lc\",\"sed -n '103,129p' col/src/model/dataset.py\"],\"justification\":\"View the middle section where inputs are tiled and negatives sampled.\",\"sandbox_permissions\":\"require_escalated\"...",
      "output_summary": "{\"output\":\"                user_genre_pref_embedding = batch_floats_data[:, self.shard_float_slices[\\\"user_genre_pref_embedding\\\"]]\\n                read_series_avg_genre_emb = batch_floats_data[:, se..."
    },
    {
      "name": "shell",
      "input_summary": "{\"command\":[\"bash\",\"-lc\",\"sed -n '1,260p' col/src/model/model.py\"],\"justification\":\"Review model architecture, dropout/batchnorm usage, and forward signature.\",\"sandbox_permissions\":\"require_escalated...",
      "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..."
    },
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      "name": "shell",
      "input_summary": "{\"command\":[\"bash\",\"-lc\",\"sed -n '1,260p' col/src/config/config.py\"],\"justification\":\"Inspect hyperparameters like LR, dropout, NEG_PER_POS, batch sizes, mode.\",\"sandbox_permissions\":\"require_escalate...",
      "output_summary": "{\"output\":\"import os\\nfrom openai import OpenAI\\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..."
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      "name": "shell",
      "input_summary": "{\"command\":[\"bash\",\"-lc\",\"sed -n '1,260p' col/src/driver.py\"],\"justification\":\"Open the driver file the user ran to confirm it calls training.driver().\",\"sandbox_permissions\":\"require_escalated\",\"time...",
      "output_summary": "{\"output\":\"import os\\nfrom datetime import datetime\\nos.environ[\\\"AWS_DEFAULT_REGION\\\"] = \\\"ap-south-1\\\"\\nos.environ[\\\"OPENAI_API_KEY\\\"] = \\\"sk-proj-qRoswT4wSHS3R4i7ZcIbP3FW5XT0xI_WFPd_ZkNr0OXrihudRu2..."
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      "name": "update_plan",
      "input_summary": "{\"plan\":[{\"status\":\"completed\",\"step\":\"Inspect training/eval loop\"},{\"status\":\"completed\",\"step\":\"Check dataloaders and sampler\"},{\"status\":\"completed\",\"step\":\"Review model modules (dropout/BN)\"},{\"st...",
      "output_summary": "Plan updated"
    }
  ],
  "turn_count": 5,
  "files_touched": [],
  "summary_from_context": "auto",
  "metrics": {
    "outcome": "resolved",
    "session_type": "bug-fix",
    "iteration_count": 5,
    "struggle_score": 0,
    "prompt_specificity": 4,
    "time_of_day": "morning"
  }
}