| ````markdown | |
| --- | |
| language: | |
| - en | |
| tags: | |
| - GraphRAG | |
| - retrieval-augmented-generation | |
| - reinforcement-learning | |
| - multi-hop-reasoning | |
| license: mit | |
| datasets: | |
| - 2Wiki | |
| - MuSiQue | |
| - HotpotQA | |
| base_model: Qwen2.5-7B & Qwen2.5-7B-Instruct | |
| --- | |
| # GraphRAG-R1 | |
| **This is the official repository for the paper “[GraphRAG-R1: Graph Retrieval-Augmented Generation with Process-Constrained Reinforcement Learning](https://arxiv.org/abs/2507.23581)” (Accepted to WWW '26).** | |
| | **Item** | **Details** | | |
| |:---|:---| | |
| | **📄 Paper** | [GraphRAG-R1: Graph Retrieval-Augmented Generation with Process-Constrained Reinforcement Learning](https://arxiv.org/abs/2507.23581) | | |
| | **🏆 Conference** | **The ACM Web Conference 2026 (WWW '26)** <br> *April 13–17, 2026, Dubai, United Arab Emirates* | | |
| | **💻 Full Code** | **GitHub:** `https://github.com/ycygit/GraphRAG-R1` | | |
| | **🤗 HF Models** | This repository provides the LoRA adapters. See [Model Weights & Usage](#model-weights--usage). | | |
| | **📜 License** | MIT | | |
| --- | |
| ## 🔍 Quick Links | |
| - [Abstract](#abstract) | |
| - [Model Weights & Usage](#model-weights--usage) | |
| - [Citation](#citation) | |
| - [Contact](#contact) | |
| --- | |
| ## 📝 Abstract | |
| GraphRAG-R1 is a Graph Retrieval-Augmented Generation framework enhanced with **Process-Constrained Reinforcement Learning**. It is designed to significantly improve the reasoning capabilities of large language models (LLMs) on complex, multi-hop question answering tasks by integrating structured knowledge graph retrieval with constrained reinforcement learning over the reasoning process. | |
| **Official BibTeX Citation:** | |
| ```bibtex | |
| @inproceedings{yu2025graphrag, | |
| title={GraphRAG-R1: Graph Retrieval-Augmented Generation with Process-Constrained Reinforcement Learning}, | |
| author={Yu, Chuanyue and Zhao, Kuo and Li, Yuhan and Chang, Heng and Feng, Mingjian and Jiang, Xiangzhe and Sun, Yufei and Li, Jia and Zhang, Yuzhi and Li, Jianxin and others}, | |
| booktitle = {Proceedings of the ACM Web Conference 2026 (WWW '26)}, | |
| year={2026} | |
| } | |
| ```` | |
| --- | |
| ## 🤖 Model Weights & Usage | |
| This repository provides **LoRA adapters** for the GraphRAG-R1 framework. To use them, you must load them onto the corresponding base model. | |
| **Available Adapters:** | |
| 1. **For `Qwen2.5-7B`**: Adapter for the base model. | |
| 2. **For `Qwen2.5-7B-Instruct`**: Adapter for the instruction-tuned variant. | |
| --- | |
| ## 🙏 Contact | |
| For questions regarding the model or paper, please open an issue in the future GitHub repository or contact the authors. | |
| --- |