| # Abforge_Training |
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| Local training workspace for ABForge-style reinforcement learning on scientific ablation design. |
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| ## Scope |
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| This repository contains: |
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| - `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 |
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| The current setup focuses on Qwen3-based GRPO training for: |
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| - Task 1: ablation objective identification |
| - Task 2: ablation experiment synthesis |
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| ## Added Local Components |
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| ### Task 2 |
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| - 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` |
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| ### Task 1 |
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| - 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` |
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| ## Notes |
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| - 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. |
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