File size: 2,419 Bytes
27fbbec c03a3dd 27fbbec c03a3dd 27fbbec c03a3dd 27fbbec e697b0e 27fbbec |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 |
---
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](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). |
---
## 🔍 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.
--- |