# Session Analysis: rollout-2026-01-29T23-07-18-019c0c02-d7b7-7c30-acc5-4f66b8839635.jsonl

**Date**: 2026-01-29 | **Duration**: 127s | **Turns**: 4 | **Model**: gpt-5.2-codex
**Type**: bug-fix | **Outcome**: resolved | **Specificity**: 5/5 | **Struggle**: 2/10

---

## Journey

The developer was training a cold-start recommendation model ("cold-ranker") on a GPU instance. After running `python driver.py`, the data pipeline completed successfully (sharding series meta, user meta, interactions, and derived columns) and training began under W&B tracking. However, the DataLoader crashed immediately on the first batch with an `IndexError` in the custom `IterableDataset.__iter__` method. The developer pasted the full terminal output -- including the successful data prep, W&B initialization, and the complete traceback -- into Codex CLI via VS Code, effectively handing off the debugging task.

Codex navigated sandbox permission issues (the first `ls` failed due to Landlock sandboxing), then systematically read through `dataset.py`, `sampler.py`, `training.py`, and `config.py` to understand the data flow and tensor shapes. It identified that `age_days` and `total_interactions` were being expanded from 2D to 3D via `[:, None]`, which caused `np.tile(..., (dup, 1))` to duplicate along the wrong axis. This left axis-0 at the original batch size (256) while the shuffle index was sized for the duplicated label count (840), producing the out-of-bounds error. Codex applied a patch removing the spurious `[:, None]` from both columns.

## Prompting Assessment (5/5)

**What worked:**
The developer provided an exemplary bug report -- the entire terminal output including the full traceback with exact line numbers and the runtime context. No guesswork was needed.

> "IndexError: index 839 is out of bounds for axis 0 with size 256"

The error message, the file paths, the open IDE tabs, and the active config file were all included, giving the model a complete picture.

**What was vague or could improve:**
Nothing was vague. This is a model prompt for a bug-fix session. The only potential improvement would be stating what was changed recently (e.g., "I added negative sampling duplication") to help prioritize hypotheses faster, but the model found the root cause quickly regardless.

## Outcome: resolved

**Evidence:**
> "The crash came from `age_days` (and similarly `total_interactions`) being expanded to 3D by `[:, None]`, so `np.tile(..., (dup, 1))` duplicated along the wrong axis. That left axis-0 at the original batch size (256), but the shuffle index was sized for the duplicated label count (e.g., 840), causing the out-of-bounds. I removed the extra axis so these arrays stay 2D and tile correctly."

The fix was applied via `apply_patch` to `col/src/model/dataset.py`, changing:
```
age_days = batch_floats_data[:, self.shard_float_slices["age_days"]].astype(np.float32, copy=False)[:, None]
```
to:
```
age_days = batch_floats_data[:, self.shard_float_slices["age_days"]].astype(np.float32, copy=False)
```
(and the equivalent change for `total_interactions`).

## Struggle Points

The only friction was the sandbox environment. The first shell command failed due to Landlock restrictions before Codex escalated permissions.

> "error running landlock: Sandbox(LandlockRestrict)"

This was resolved in one retry by requesting escalated permissions, costing only a few seconds. Beyond that, the session was smooth -- the model read four source files in sequence and pinpointed the root cause without any false starts or wasted tool calls.

## Aha Insight

The core insight was understanding how NumPy broadcasting interacts with `np.tile` when an extra axis is present. The `[:, None]` expansion turned a `(batch, 1)` slice into `(batch, 1, 1)`, causing the `(dup, 1)` tile pattern to duplicate along axis-1 instead of axis-0, leaving axis-0 unchanged at 256 while labels grew to 840 after duplication.

**Evidence:**
> "I'm removing the redundant dimension expansion for age_days and total_interactions to prevent indexing errors when using 1D indices on 3D arrays, ensuring consistent shapes and avoiding potential bugs during training."

## Coach Tip

When building custom `IterableDataset` classes with negative sampling duplication, add a shape assertion right after tiling and before shuffling -- something like `assert all(arr.shape[0] == labels.shape[0] for arr in [age_days, total_interactions, ...])`. This turns a cryptic `IndexError` deep inside a DataLoader worker into an immediate, descriptive failure at the point of divergence, making these shape mismatches trivially debuggable without needing an AI agent.
