code/Abforge_Training/readme.md

Abforge_Training

Local training workspace for ABForge-style reinforcement learning on scientific ablation design.

Scope

This repository contains:

• verl_proj/: a customized verl training tree
• reward_part/: local LLM-as-judge services for Task 1 and Task 2 rewards
• data/: local raw RL data
• ARR_March___Ablation_Study_Post_Training__New_-14.pdf: reference paper

The current setup focuses on Qwen3-based GRPO training for:

• Task 1: ablation objective identification
• Task 2: ablation experiment synthesis

Added Local Components

Task 2

• Data preprocess: verl_proj/examples/data_preprocess/abforge_task2_rl.py
• Judge service: reward_part/task2_call_api.py
• Reward API: reward_part/task2_rw.py
• Training script: verl_proj/examples/grpo_trainer/run_abforge_qwen3_8b_hf.sh

Task 1

• Data preprocess: verl_proj/examples/data_preprocess/abforge_task1_rl.py
• Judge service: reward_part/task1_call_api.py
• Reward API: reward_part/task1_rw.py
• Training script: verl_proj/examples/grpo_trainer/run_abforge_qwen3_8b_task1_hf.sh

Notes

• Reward is computed only on the structured output block:
  • Task 1: <Result>...</Result>
  • Task 2: <Proposed_Plan>...</Proposed_Plan>
• Qwen3 training paths should disable enable_thinking in chat templating.
• The current training scripts use hf rollout first for compatibility bring-up. vLLM can be added later after environment validation.