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

Topics: Graph RAG, Knowledge Graph, Retrieval Augmented Generation (RAG)

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This research paper document introduces Graph-R1, a new framework that addresses the limitations of current Retrieval-Augmented Generation (RAG) systems. It utilizes an agentic approach with end-to-end reinforcement learning to improve reasoning accuracy, retrieval efficiency, and generation quality by treating knowledge as a hypergraph and retrieval as a multi-turn interaction. The system aims to overcome challenges like high construction costs, fixed one-time retrieval, and dependence on large LLMs.

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