language:
- en
tags:
- GraphRAG
- retrieval-augmented-generation
- reinforcement-learning
- multi-hop-reasoning
license: apache-2.0
datasets:
- 2Wiki
- MuSiQue
- HotpotQA
GraphRAG-R1
This is the official repository for the paper “GraphRAG-R1: Graph Retrieval-Augmented Generation with Process-Constrained Reinforcement Learning” (Accepted to WWW '26).
| Item | Details |
|---|---|
| 📄 Paper | GraphRAG-R1: Graph Retrieval-Augmented Generation with Process-Constrained Reinforcement Learning |
| 🏆 Conference | The ACM Web Conference 2026 (WWW '26) 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. |
🔍 Quick Links
📝 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:
@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:
- For
Qwen2.5-7B: Adapter for the base model. - 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.