---
pretty_name: DR-AntiForget RQ3 Writing Refresh
tags:
- continual-learning
- lora
- evaluation
---

# RQ3 Writing Refresh: Reproducibility Artifacts

This directory contains the compact reproducibility package for the RQ3.1 experiment that replaced only the `lora-writing` adapter with an OpenScholar-refreshed adapter (`writing++`) while keeping the Qwen3-32B base, Plan adapter, and Search adapter frozen.

## Headline result

The refresh improved teacher-forced performance on the new OpenScholar domain but did not yield a reliable general downstream improvement:

| Evaluation set | Source PPL | Refreshed PPL | Relative change |
|---|---:|---:|---:|
| OpenScholar holdout | 3.5567 | 2.7634 | **-22.3%** |
| Historical Writing holdout | 1.5866 | 1.6561 | **+4.4%** |

The downstream table should be interpreted as a preliminary negative/mixed result. Every paired 95% bootstrap confidence interval includes zero. See [`analysis/DECLINE_ANALYSIS.md`](analysis/DECLINE_ANALYSIS.md) for the failure and routing audit.

## Contents

- `model/lora-writing/`: refreshed adapter config and weights. The weight SHA-256 is `4e31f83c31f4784647147eea5458f5e96cd7712f5a88b21c6fd1ae2b9919e6c5`.
- `downstream/downstream_examples.jsonl`: 1,800 final answers with questions, scores, failure flags, and source-file provenance; 2 adapter conditions × 3 routing modes × 3 benchmarks × 100 tasks.
- `downstream/aggregate_results.json`, `table.md`, and `table.tex`: exact aggregate reproduced from the 1,800 exported rows.
- `analysis/analysis_summary.json`: paired bootstrap intervals, win/tie/loss counts, answer lengths, failure transitions, dataset-shift statistics, and stage-reach summaries.
- `analysis/paired_deltas.csv`: all 21 routing/benchmark/metric paired comparisons.
- `analysis/trajectory_diagnostics.jsonl`: compact per-task routing diagnostics without raw chain-of-thought or judge reasoning.
- `analysis/dataset_shift.json`: descriptive statistics for the new-domain train split and historical Writing holdout.
- `splits/openscholar_split_manifest.json`: exact trajectory-grouped split (seed 42), including 795 train rows and 205 holdout rows with zero group overlap.
- `metadata/`: training state, paired NLL/PPL evaluation, experiment metadata, model/data revisions, and adapter hashes.
- `scripts/build_export.py`: deterministic export and validation script. It selects the latest scored record per cell/task, verifies all 18 cells against the published aggregate, and builds the compact files above.

## Existing source data

The raw source datasets already live in [`WeAct/DR-AntiForget`](https://huggingface.co/datasets/WeAct/DR-AntiForget) and are not duplicated here:

- `lora_writing_openscholar/lora-writing-openscholar.jsonl` (1,000 rows)
- `tinker_holdout_test_v2/lora-writing_holdout_test.jsonl` (200 historical holdout rows)

Dataset revision used by training: `3dde83eb8ee2518c3eea4ef5317a40189215e475`.

## JSONL schema

Each row in `downstream/downstream_examples.jsonl` has:

- `condition`: `original_writing` or `writing_pp`
- `routing`: `hard`, `soft`, or `soft_oracle`
- `benchmark`: `researchqa`, `healthbench`, or `deepresearchbench`
- `task_id`, `task_question`, `answer`, `answer_source`, `answer_length`
- `framework_failure`: answer begins with `Task incomplete` or `Task interrupted`
- `metrics`: raw 0–1 judge metrics (`coverage`, `score`, or the four DRB dimensions plus their mean)
- `source_files`: paths relative to the source evaluation run

## Caveats

HealthBench and DeepResearchBench scores use preliminary local Qwen3-32B judges. The run has one training seed and independently sampled agent/search trajectories. Use the retained answers for official-judge rescoring, and do not treat the current downstream deltas as statistically significant or as a clean causal estimate of the Writing adapter effect.