Graph-R1: Towards Agentic GraphRAG Framework via End-to-end Reinforcement Learning
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arXiv
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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866912508940386304 |
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| author | Luo, Haoran E, Haihong Chen, Guanting Lin, Qika Guo, Yikai Xu, Fangzhi Kuang, Zemin Song, Meina Wu, Xiaobao Zhu, Yifan Tuan, Luu Anh |
| author_facet | Luo, Haoran E, Haihong Chen, Guanting Lin, Qika Guo, Yikai Xu, Fangzhi Kuang, Zemin Song, Meina Wu, Xiaobao Zhu, Yifan Tuan, Luu Anh |
| contents | Retrieval-Augmented Generation (RAG) mitigates hallucination in LLMs by incorporating external knowledge, but relies on chunk-based retrieval that lacks structural semantics. GraphRAG methods improve RAG by modeling knowledge as entity-relation graphs, but still face challenges in high construction cost, fixed one-time retrieval, and reliance on long-context reasoning and prompt design. To address these challenges, we propose Graph-R1, an agentic GraphRAG framework via end-to-end reinforcement learning (RL). It introduces lightweight knowledge hypergraph construction, models retrieval as a multi-turn agent-environment interaction, and optimizes the agent process via an end-to-end reward mechanism. Experiments on standard RAG datasets show that Graph-R1 outperforms traditional GraphRAG and RL-enhanced RAG methods in reasoning accuracy, retrieval efficiency, and generation quality. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_21892 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Graph-R1: Towards Agentic GraphRAG Framework via End-to-end Reinforcement Learning Luo, Haoran E, Haihong Chen, Guanting Lin, Qika Guo, Yikai Xu, Fangzhi Kuang, Zemin Song, Meina Wu, Xiaobao Zhu, Yifan Tuan, Luu Anh Computation and Language Retrieval-Augmented Generation (RAG) mitigates hallucination in LLMs by incorporating external knowledge, but relies on chunk-based retrieval that lacks structural semantics. GraphRAG methods improve RAG by modeling knowledge as entity-relation graphs, but still face challenges in high construction cost, fixed one-time retrieval, and reliance on long-context reasoning and prompt design. To address these challenges, we propose Graph-R1, an agentic GraphRAG framework via end-to-end reinforcement learning (RL). It introduces lightweight knowledge hypergraph construction, models retrieval as a multi-turn agent-environment interaction, and optimizes the agent process via an end-to-end reward mechanism. Experiments on standard RAG datasets show that Graph-R1 outperforms traditional GraphRAG and RL-enhanced RAG methods in reasoning accuracy, retrieval efficiency, and generation quality. |
| title | Graph-R1: Towards Agentic GraphRAG Framework via End-to-end Reinforcement Learning |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2507.21892 |