# Session Analysis: rollout-2026-01-29T10-18-05-019c0942-9af8-7ae3-ae19-b76cbfc7e92d.jsonl

**Date**: 2026-01-29 | **Duration**: 0.2s | **Turns**: 6 | **Model**: gpt-5
**Type**: bug-fix | **Outcome**: abandoned | **Specificity**: 4/5 | **Struggle**: 6/10

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## Journey

The developer started a new day (Jan 29) and ran a new training run. This time they had reverted `NEG_PER_POS` back to 7, `NEG_PER_POS_TEST` also to 7 (was previously 50), and included the full config in the prompt. The training output shows a different pattern from the Jan 28 sessions — much less severe overfitting, with train_loss and test_loss staying relatively close (train: 0.2489, test: 0.2591 at epoch 8). However, the developer noted "test loss not decreasing" and abandoned the session in 0.2 seconds — no assistant response was generated.

## Prompting Assessment (4/5)

**What worked:**
The developer provided extensive context — the full `config.py` file contents, training output, and a specific observation about the problem.

> "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 observation "test loss not decreasing" is actually more specific than the previous "how to improve my model?" — the developer is naming a concrete problem. However, they abandoned before getting any response. Also, the test loss IS decreasing at epoch 3 (0.2542) and epoch 6 (0.2535) — it's just not monotonically decreasing. A more precise prompt would have been: "test loss is plateauing around 0.25 while train loss keeps dropping."

> "test loss not decreasing"

## Outcome: abandoned

**Evidence:**
The session has zero assistant messages and zero tool calls. Duration was 0.2 seconds. The developer opened the session, provided the prompt, and immediately closed it — possibly realizing they wanted to phrase the question differently, or accidentally triggering a session.

## Struggle Points

The recurring pattern of instant session abandonment continues. This is the fourth session (out of 8 total) that was abandoned in under 1 second.

> "test loss not decreasing"

The developer's frustration with the model's plateau is evident, but the impatience with the AI tool compounds the problem.

## Aha Insight

Comparing this training run to the Jan 28 runs reveals something important: after reverting the cross-feature changes and adjusting `NEG_PER_POS`, the model actually behaves much better. Train and test loss stay within ~0.01 of each other through 8 epochs (vs. the 0.14 gap in Jan 28). The developer perceived this as a problem ("test loss not decreasing") but the real story is that the overfitting is largely gone — the model is simply approaching its capacity limit at this architecture/data scale. The next step isn't more epochs; it's a fundamentally different approach (more data, different architecture, or better features).

**Evidence:**
> "Epoch 006 | train_loss=0.2506 | test_loss=0.2535" — train/test gap is only 0.003, showing minimal overfitting
> "Epoch 008 | train_loss=0.2489 | test_loss=0.2591" — slight divergence but nothing like the 0.14 gap from Jan 28

## Coach Tip

A model that doesn't overfit but plateaus early is a fundamentally different problem than a model that overfits badly. The first means you need more capacity or better features; the second means you need regularization. Learn to distinguish these two failure modes — they require opposite interventions. Throwing more regularization at a capacity-limited model will make things worse.
