Update README.md

README.md

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----
-license: mit
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+````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.
+
+---