# Session Analysis: rollout-2026-01-28T21-21-52-019c067b-f20e-70a1-9ee2-84f4163b55b7.jsonl

**Date**: 2026-01-28 | **Duration**: 107s | **Turns**: 6 | **Model**: gpt-5
**Type**: bug-fix | **Outcome**: resolved | **Specificity**: 4/5 | **Struggle**: 4/10

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## Journey

The developer reported a concrete result: changing `NEG_PER_POS` from 7 to 20 reduced test_loss from 0.12 to 0.08. The assistant investigated how `NEG_PER_POS` flows through the codebase (config.py -> training.py -> dataset.py) and provided a thorough analysis of why the loss dropped, potential trade-offs, and recommendations for next steps including `pos_weight` balancing, AUC/PR-AUC logging, and a negatives sweep.

## Prompting Assessment (4/5)

**What worked:**
The developer provided a clear before/after result with the exact parameter change, giving the assistant a specific data point to analyze.

> "i changed NEG_PER_POS from 7 to 20, the test_loss changed from 0.12 to 0.08"

**What was vague or could improve:**
The developer shared the result but didn't ask a specific question — it was implicitly "is this good?" or "what should I do next?" A more targeted prompt would be: "I changed NEG_PER_POS from 7 to 20 and test_loss dropped from 0.12 to 0.08. Is this a real improvement or just an artifact of the changed class distribution?"

> "i changed NEG_PER_POS from 7 to 20, the test_loss changed from 0.12 to 0.08"

## Outcome: resolved

**Evidence:**
> "Nice — that drop is consistent with how your pipeline uses NEG_PER_POS."

> "Class imbalance: raising 7->20 shifts positive rate from 12.5% to ~4.8%, so unweighted BCE will naturally reward predicting lower probabilities on more negatives."

The assistant correctly identified that the loss drop may be partially an artifact of changed class distribution rather than a genuine improvement, and recommended monitoring `pos_prob_mean`/`neg_prob_mean` and adding ranking-centric metrics like AUC.

## Struggle Points

This session was efficient with no significant struggle points. The sandbox escalation issues continued (every command needed two attempts) but the session completed quickly.

## Aha Insight

The assistant raised a subtle but critical point that the developer likely didn't consider: lowering BCE loss by adding more negatives doesn't necessarily mean the model improved. With 20 negatives per positive, the positive rate drops to ~4.8%, and unweighted BCE will naturally be lower simply because the model can achieve low loss by predicting near-zero for everything. The developer thought they found an improvement, but the metric itself became misleading when the class distribution changed.

**Evidence:**
> "Class imbalance: raising 7->20 shifts positive rate from 12.5% to ~4.8%, so unweighted BCE will naturally reward predicting lower probabilities on more negatives."

> "Calibration/thresholds: unweighted BCE with heavy imbalance can push overall probabilities down; recall may drop even if loss improves."

## Coach Tip

When you change a hyperparameter that affects class distribution (like negatives-per-positive), you can't directly compare BCE loss values between runs. Instead, use a distribution-invariant metric like AUC or PR-AUC. Ask the AI to help you add these metrics first, then run your experiments — otherwise you're comparing apples to oranges.
