# Session Analysis: rollout-2026-01-29T20-44-10-019c0b7f-ca24-7592-8a3a-0a90a513648c.jsonl

**Date**: 2026-01-29 | **Duration**: 432s | **Turns**: 9 | **Model**: gpt-5.2-codex
**Type**: bug-fix | **Outcome**: resolved | **Specificity**: 4/5 | **Struggle**: 4/10

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## Journey

The developer was working on a cold-ranker ML project, specifically in the training pipeline (`training.py`, `dataset.py`, `config.py`). They had a concern about test set reproducibility: whether the `test_loader` would generate identical positive and negative examples across epochs and across different runs, assuming data preparation was the same.

1. **Turn 1 (inquiry)**: The developer asked whether `test_loader` would produce the same positive/negative examples across epochs and runs. This was a conceptual question about determinism in their data loading pipeline.

2. **Turn 2 (code investigation)**: The agent read `training.py`, `dataset.py`, `sampler.py`, and `config.py` to understand the full data flow. It identified three sources of non-determinism: unsorted shard paths from `os.listdir()`, multi-worker `IterableDataset` yielding in timing-dependent order, and per-worker sampler copies all seeded at 42.

3. **Turn 3 (implementation)**: The developer asked the agent to make all required changes to ensure the same test set across runs. The agent modified three files: sorted shard paths in `dataset.py`, forced `NUM_WORKER_TEST_DATALOADER = 0` in `config.py`, and added test-specific parameters in `training.py`.

4. **Turn 4 (follow-up question)**: The developer asked about multi-worker implications (typed as "multi order rest"), wanting to understand how switching to multiple workers would change the data. The agent clarified that batch order and negatives could differ across runs with multi-worker loading.

5. **Turn 5 (deeper understanding)**: The developer asked how single worker ensures negatives are always the same for a given positive. The agent explained the position-based determinism: the sampling sequence is deterministic because the RNG advances in a fixed order when iteration order is stable.

## Prompting Assessment (4/5)

**What worked:**
> "will my test_loader generate same positive and negative examples across epochs and across different runs given data preperation is same?"

This is a well-scoped question that gives the agent clear context: the concern is about reproducibility, and the assumption (same data prep) is stated explicitly. The developer also had the relevant file selected in their IDE, providing additional context.

**What was vague or could improve:**
> "if i use multi order rest ? how my data will be differnt?"

This message has a typo ("multi order rest" likely meaning "multi-worker test") and lacks specificity about what aspect of the data they care about (order? content? negatives?). The agent had to ask for clarification. More precise phrasing like "If I switch NUM_WORKER_TEST_DATALOADER back to a value greater than 0, which aspects of the test data would become non-deterministic?" would have avoided the clarification round-trip.

## Outcome: resolved

**Evidence:**
> "single worker + fixed shard order + no shuffling + same batch size → identical negatives per positive position, hence identical per positive row in practice."

The agent successfully identified the root causes of non-determinism, implemented fixes across three files, and provided a clear explanation of why the solution works. The developer's follow-up questions were about understanding the implications rather than reporting remaining issues.

## Struggle Points

The main friction came from sandbox restrictions and a typo-induced clarification cycle.

> "Exit code: 101\nWall time: 0 seconds\nOutput:\n\nthread 'main' (18626) panicked at linux-sandbox/src/linux_run_main.rs:30:9:\nerror running landlock: Sandbox(LandlockRestrict)"

The first shell command failed due to sandboxing, requiring the agent to re-issue with escalated permissions. This consumed an extra turn but was handled smoothly by the agent.

Additionally, the typo "multi order rest" forced the agent to hedge its response:

> "Do you mean **multi-worker test loading** (i.e., `num_workers > 0`), or something else by \"multi order rest\"?"

## Aha Insight

The developer gained a nuanced understanding of how data loader determinism works at the mechanics level -- that negatives are not tied to a positive by ID, but by position in the iteration sequence.

**Evidence:**
> "It's not \"negatives are tied to a positive by ID,\" it's \"negatives are tied to the **position** of that positive in the iteration.\" If you change batch size, shard splits, or the row order, you can get **different negatives for the same positive ID** even with single worker."

This is a subtle but critical insight for anyone building reproducible ML training pipelines. The developer's question "how does single worker insure that the negatives are always same for a psoitive?" shows they were building mental models, and the agent's answer corrected a likely misconception.

## Coach Tip

When asking follow-up questions about alternative configurations, state the exact parameter change you are considering rather than using shorthand that might be misread. For example, instead of "if i use multi order rest?", say "If I change NUM_WORKER_TEST_DATALOADER from 0 to 4, what changes?" This eliminates a clarification round-trip and gets you a precise answer faster. The session was otherwise well-structured -- starting with a question, requesting implementation, then probing edge cases to build understanding.
