# Session Analysis: rollout-2026-01-29T10-18-14-019c0942-bb05-7bf3-a9dc-88b38b29e56c.jsonl

**Date**: 2026-01-29 | **Duration**: 1226s | **Turns**: 15 | **Model**: gpt-5
**Type**: bug-fix | **Outcome**: resolved | **Specificity**: 4/5 | **Struggle**: 6/10

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## Journey

This was the most substantive engineering session in the entire dataset (~20 minutes, 268 events, 15 turns). The developer reported the same "test loss not decreasing" issue from the instantly-abandoned session 8, but this time stayed long enough for a full investigation and multi-file patch. The session had two major phases:

**Phase 1: Root cause diagnosis and deterministic test negatives** — The assistant investigated the training loop, sampler, dataset, and model, and identified that test negatives were being re-sampled each epoch, making test loss noisy. It patched `sampler.py` to accept a `seed` parameter and modified `training.py` to use a fixed-seed sampler for test evaluation with shuffling disabled.

**Phase 2: Training stability improvements** — After a second training run (4 epochs, similar plateau), the assistant added: ReduceLROnPlateau scheduler, early stopping with configurable patience, gradient clipping, env-overridable config knobs in `config.py`, and fixed the `WANDB_RUN_NAME` datetime formatting in `driver.py`.

Six successful `applypatch` operations modified `sampler.py`, `training.py` (multiple patches), `config.py`, and `driver.py`.

## Prompting Assessment (4/5)

**What worked:**
The developer provided the full config file contents plus training output, giving the assistant complete context. The second prompt also included a fresh training run showing the same pattern, confirming the issue wasn't a fluke.

> "Epoch 001 | train_loss=0.2741 | test_loss=0.2584 | elapsed=123.7s\n...\nEpoch 008 | train_loss=0.2489 | test_loss=0.2591 | elapsed=123.0s\n\n\ntest loss not decreasing"

**What was vague or could improve:**
The developer didn't specify which training changes from the previous day's sessions they had kept vs. reverted, making it harder for the assistant to know the current state of the code without re-reading everything.

> "test loss not decreasing"

## Outcome: resolved

**Evidence:**
> "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."

> "What I changed - Deterministic test negatives... LR scheduling + early stop... Gradient clipping... W&B cleanup"

The assistant delivered a comprehensive multi-file patch addressing multiple root causes.

## Struggle Points

The `apply_patch` routing issue was severe in this session — 7 out of 15 user messages were "Warning: apply_patch was requested via shell. Use the apply_patch tool instead of exec_command." Despite this, the assistant managed to successfully apply all 6 patches. This Codex CLI tooling friction added significant time to the session.

> "Warning: apply_patch was requested via shell. Use the apply_patch tool instead of exec_command."

This appeared 7 times across the session, showing the tooling was fighting the assistant at every step.

## Aha Insight

The root cause of the "test loss not decreasing" was not a model problem at all — it was a **measurement problem**. The test dataloader was using a non-deterministic negative sampler, so each epoch's test set was effectively a different dataset. The apparent plateau was actually noise from re-sampling. Once the assistant fixed evaluation to use deterministic negatives (seed=42, no shuffling), the test loss became a reliable signal. This distinction between "the model isn't learning" and "the metric is noisy" is fundamental and easily missed.

**Evidence:**
> "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."

## Coach Tip

Before concluding "my model isn't learning," first verify that your evaluation pipeline is deterministic. Stochastic test sets produce noisy metrics that can mask genuine improvement. Add a `seed` to your test sampler, disable shuffling, and compare test loss epoch-to-epoch. If the noise disappears, you know the model was learning — you just couldn't see it.
