README.md

# BA Agent Post-Training Experiment

This directory is a standalone experiment workspace copied out of the main training path.

Training objective:

- Keep `data_insight` isolated from long-chain planning and report writing.
- Reuse one shared base model with role-specific LoRA adapters.
- Optimize evidence-grounded analysis and conclusion reliability before attempting long-report end-to-end RL.

Recommended order:

1. Train `data_insight` with SFT.
2. Train `writer` with SFT.
3. Run `writer` DPO and reward modeling on BA preference data.
4. Optionally run PPO with the reward adapter on prompt-only tasks.

Supported SFT schema:

```json
{"system":"optional role prompt","prompt":"question or analysis context","response":"target answer"}
```

Supported automatically:

- `instruction` + `input` + `output`
- `question` + `answer`
- `messages` / `conversations` where the final assistant turn is the target

Supported DPO / reward schema:

```json
{"system":"optional role prompt","prompt":"same prompt shown to both candidates","chosen":"preferred answer","rejected":"dispreferred answer"}
```

Supported automatically:

- `question` + `response_chosen` + `response_rejected`
- `Anthropic/hh-rlhf` style `chosen` / `rejected`
- `PKU-Alignment/PKU-SafeRLHF-*` style pairwise columns

Supported PPO prompt schema:

```json
{"system":"optional role prompt","prompt":"generation prompt only"}
```

Suggested role split:

- `data_insight`: facts, supported insights, evidence refs, uncertainty only
- `writer`: briefs, chatbot answers, and section drafts that consume structured evidence

Files in this experiment:

- `utils.py`: local copy of shared training helpers and arguments
- `data_adapter.py`: local schema normalizer for SFT / DPO / reward / PPO
- `sft.py`, `dpo.py`, `reward_model.py`, `ppo_multi_adapter.py`: experiment training entrypoints
- `merge_adapter.py`, `ma_ppo_config.py`, `ma_ppo_trainer.py`: copied dependencies needed by this experiment
- `data/*.sample.jsonl`: schema examples and smoke-test inputs
- `scripts/run_ba_role_*.sh`: standalone run scripts

Quick start:

```bash
bash ./experiments/ba_agent_posttrain/scripts/run_ba_role_sft.sh \
  ROLE_NAME=data_insight \
  SFT_DATASET_NAME=./experiments/ba_agent_posttrain/data/data_insight_sft.sample.jsonl

bash ./experiments/ba_agent_posttrain/scripts/run_ba_role_sft.sh \
  ROLE_NAME=writer \
  SFT_DATASET_NAME=./experiments/ba_agent_posttrain/data/writer_sft.sample.jsonl

bash ./experiments/ba_agent_posttrain/scripts/run_ba_role_dpo.sh \
  ROLE_NAME=writer \
  PREFERENCE_DATASET_NAME=./experiments/ba_agent_posttrain/data/writer_preference.sample.jsonl
```

For real runs, replace the sample files with your own:

- `data_insight_sft.jsonl`
- `writer_sft.jsonl`
- `writer_preference.jsonl`
- `writer_prompts.jsonl`