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:

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

Supported SFT schema:

{"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:

{"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:

{"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 ./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