Omne-R1: Learning to Reason with Memory for Multi-hop Question Answering
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2025
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866914002213273600 |
|---|---|
| author | Liu, Boyuan Ji, Feng Nan, Jiayan Zhao, Han Chen, Weiling Xu, Shihao Zhou, Xing |
| author_facet | Liu, Boyuan Ji, Feng Nan, Jiayan Zhao, Han Chen, Weiling Xu, Shihao Zhou, Xing |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_17330 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Omne-R1: Learning to Reason with Memory for Multi-hop Question Answering Liu, Boyuan Ji, Feng Nan, Jiayan Zhao, Han Chen, Weiling Xu, Shihao Zhou, Xing Computation and Language Artificial Intelligence 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. |
| title | Omne-R1: Learning to Reason with Memory for Multi-hop Question Answering |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2508.17330 |