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DeepRefine: Agent-Compiled Knowledge Refinement via Reinforcement Learning

ARXIV PYTHON PYTHON LICENSE

🤔 Your LLM-Wiki Need to be Refined

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DeepRefine is a general LLM-based reasoning model for agent-compiled knowledge refinement that improves the quality of any pre-constructed knowledge bases with user queries to make it more suitable for the downstream tasks.

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📊 Training Data Preprocessing

We collect the raw training data of HotpotQA from https://hotpotqa.github.io/ and then construct the data samples for RL training through the following script:

bash scripts/autograph-r1/data_prepare/hotpotqa_cons.sh

Or you can also access the training data under the folder data/.

🔥 GRPO Training

⚠️ Configuration Reminder: Please ensure to replace all path configurations in the following scripts with your own paths.

Update your config in verl/third_party/autograph_r1/config.ini.

Train $\texttt{DeepRefine-4B}$ based on $\texttt{Qwen3-4B-Instruct-2507en}$ with GRPO and GBD reward:

bash scripts/train/run_qwen3-4b_graph_refiner.sh

Train $\texttt{DeepRefine-8B}$ based on $\texttt{Qwen3-8B}$ with GRPO and GBD reward:

bash scripts/train/run_qwen3-8b_graph_refiner.sh

We have also provided our model in HuggingFace.

🔍 Evaluation

⚠️ Configuration Reminder: Please ensure to replace all path configurations in the following scripts with your own paths.

There are six evaluation mode:

  • Graph Retriever, no refinement.
bash scripts/eval/gr_refine_bench_no_refine.sh
  • Graph Retriever, naive refinement (without training).
bash scripts/eval/gr_refine_bench_wo_rl.sh
  • Graph Retriever, deeprefine.
bash scripts/eval/gr_refine_bench_rl.sh
  • Text Retriever, no refinement.
bash scripts/eval/tr_refine_bench_no_refine.sh
  • Text Retriever, naive refinement (without training).
bash scripts/eval/tr_refine_bench_wo_rl.sh
  • Text Retriever, deeprefine.
bash scripts/eval/tr_refine_bench_rl.sh

📖 Citation

@article{huang2026deeprefine,
  title={DeepRefine: Agent-Compiled Knowledge Refinement via Reinforcement Learning},
  author={Huang, Haoyu and Bai, Jiaxin and Liu, Shujie and Wei, Yang and Tsang, Hong Ting and Gao, Yisen and Xie, Zhongwei and Li, Yufei and Song, Yangqiu},
  journal={arXiv preprint arXiv:2605.10488},
  year={2026}
}

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DeepRefine is a general LLM-based reasoning model for agent-compiled knowledge refinement that improves the quality of any pre-constructed knowledge bases with user queries to make it more suitable for the downstream tasks.

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