ProGraph-R1: Progress-aware Reinforcement Learning for Graph Retrieval Augmented Generation

• URL: http://arxiv.org/abs/2601.17755v1
• Date: Sun, 25 Jan 2026 08:58:44 GMT
• Title: ProGraph-R1: Progress-aware Reinforcement Learning for Graph Retrieval Augmented Generation
• Authors: Jinyoung Park, Sanghyeok Lee, Omar Zia Khan, Hyunwoo J. Kim, Joo-Kyung Kim,
• Abstract summary: We propose ProGraph-R1, a progress-aware agentic framework for graph-based retrieval and multi-step reasoning.<n>ProGraph-R1 introduces a structure-aware hypergraph retrieval mechanism that jointly considers semantic relevance and graph connectivity.<n> Experiments on multi-hop question answering benchmarks demonstrate that ProGraph-R1 consistently improves reasoning accuracy and generation quality over existing GraphRAG methods.
• Score: 37.11787010202267
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Graph Retrieval-Augmented Generation (GraphRAG) has been successfully applied in various knowledge-intensive question answering tasks by organizing external knowledge into structured graphs of entities and relations. It enables large language models (LLMs) to perform complex reasoning beyond text-chunk retrieval. Recent works have employed reinforcement learning (RL) to train agentic GraphRAG frameworks that perform iterative interactions between LLMs and knowledge graphs. However, existing RL-based frameworks such as Graph-R1 suffer from two key limitations: (1) they primarily depend on semantic similarity for retrieval, often overlooking the underlying graph structure, and (2) they rely on sparse, outcome-level rewards, failing to capture the quality of intermediate retrieval steps and their dependencies. To address these limitations, we propose ProGraph-R1, a progress-aware agentic framework for graph-based retrieval and multi-step reasoning. ProGraph-R1 introduces a structure-aware hypergraph retrieval mechanism that jointly considers semantic relevance and graph connectivity, encouraging coherent traversal along multi-hop reasoning paths. We also design a progress-based step-wise policy optimization, which provides dense learning signals by modulating advantages according to intermediate reasoning progress within a graph, rather than relying solely on final outcomes. Experiments on multi-hop question answering benchmarks demonstrate that ProGraph-R1 consistently improves reasoning accuracy and generation quality over existing GraphRAG methods.

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