Graph-R1: Towards Agentic GraphRAG Framework via End-to-end Reinforcement Learning

Abstract: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, the first 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. Our software and data are publicly available at this https URL.

Computation and Language (cs.CL)

arXiv:2507.21892 [cs.CL]

(or arXiv:2507.21892v2 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2507.21892 Focus to learn more arXiv-issued DOI via DataCite

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