DeepRefine: Agent-Compiled Knowledge Refinement via Reinforcement Learning

🤔 Your LLM-Wiki Need to be Refined

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.

News

• [2026/5/10] Static quants of DeepRefine-v1-8B, 🤗 mradermacher/DeepRefine-v1-8B-GGUF has been released. Thanks to the community!

📊 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