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.

翻译：图检索增强生成（GraphRAG）通过将外部知识组织为结构化的实体关系图，已成功应用于多种知识密集型问答任务。它使得大语言模型（LLM）能够执行超越文本块检索的复杂推理。近期研究采用强化学习（RL）来训练智能化的GraphRAG框架，实现LLM与知识图谱之间的迭代交互。然而，现有的基于RL的框架（如Graph-R1）存在两个关键局限：（1）其检索主要依赖语义相似性，常常忽略底层的图结构；（2）它们依赖于稀疏的结果级奖励，未能捕捉中间检索步骤的质量及其依赖关系。为解决这些局限，我们提出了ProGraph-R1，一个用于基于图的检索与多步推理的进度感知智能化框架。ProGraph-R1引入了一种结构感知的超图检索机制，该机制同时考虑语义相关性与图连通性，鼓励沿多跳推理路径进行连贯遍历。我们还设计了一种基于进度的逐步策略优化方法，该方法通过根据图内的中间推理进度调整优势值来提供密集的学习信号，而非仅依赖最终结果。在多跳问答基准上的实验表明，ProGraph-R1在推理准确性和生成质量上持续优于现有的GraphRAG方法。