Omne-R1: Learning to Reason with Memory for Multi-hop Question Answering

• URL: http://arxiv.org/abs/2508.17330v1
• Date: Sun, 24 Aug 2025 12:36:48 GMT
• Title: Omne-R1: Learning to Reason with Memory for Multi-hop Question Answering
• Authors: Boyuan Liu, Feng Ji, Jiayan Nan, Han Zhao, Weiling Chen, Shihao Xu, Xing Zhou,
• Abstract summary: Omne-R1 is a novel approach designed to enhance multi-hop question answering capabilities on schema-free knowledge graphs.<n>Our method employs a multi-stage training workflow, including two reinforcement learning phases and one supervised fine-tuning phase.
• Score: 23.78587569108481
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper introduces Omne-R1, a novel approach designed to enhance multi-hop question answering capabilities on schema-free knowledge graphs by integrating advanced reasoning models. Our method employs a multi-stage training workflow, including two reinforcement learning phases and one supervised fine-tuning phase. We address the challenge of limited suitable knowledge graphs and QA data by constructing domain-independent knowledge graphs and auto-generating QA pairs. Experimental results show significant improvements in answering multi-hop questions, with notable performance gains on more complex 3+ hop questions. Our proposed training framework demonstrates strong generalization abilities across diverse knowledge domains.

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