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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2510.20548 |
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| _version_ | 1866917343628623872 |
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| author | Luo, Jinchang Cheng, Mingquan Wan, Fan Li, Ni Xia, Xiaoling Tian, Shuangshuang Bian, Tingcheng Wang, Haiwei Fu, Haohuan Tao, Yan |
| author_facet | Luo, Jinchang Cheng, Mingquan Wan, Fan Li, Ni Xia, Xiaoling Tian, Shuangshuang Bian, Tingcheng Wang, Haiwei Fu, Haohuan Tao, Yan |
| contents | Reinforcement learning has recently shown promise in improving retrieval-augmented generation (RAG). Despite these advances, its effectiveness in multi-hop question answering (QA) remains limited by two fundamental limitations: (i) global planning absence to structure multi-step reasoning, and (ii) unfaithful execution, which hinders effective query formulation and consistent use of retrieved evidence. We propose GlobalRAG, a reinforcement learning framework designed to enhance global reasoning in multi-hop QA. GlobalRAG decomposes questions into subgoals, coordinates retrieval with reasoning, and refines evidence iteratively. To guide this process, we introduce Planning Quality Reward and SubGoal Completion Reward, which encourage coherent planning and reliable subgoal execution. In addition, a progressive weight annealing strategy balances process-oriented and outcome-based objectives. Extensive experiments on both in-domain and out-of-domain benchmarks demonstrate that GlobalRAG significantly outperforms strong baselines while using only 8k training data (42% of the training data used by strong baselines), achieving average improvements of 14.2% in both EM and F1. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_20548 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | GlobalRAG: Enhancing Global Reasoning in Multi-hop Question Answering via Reinforcement Learning Luo, Jinchang Cheng, Mingquan Wan, Fan Li, Ni Xia, Xiaoling Tian, Shuangshuang Bian, Tingcheng Wang, Haiwei Fu, Haohuan Tao, Yan Computation and Language Artificial Intelligence Reinforcement learning has recently shown promise in improving retrieval-augmented generation (RAG). Despite these advances, its effectiveness in multi-hop question answering (QA) remains limited by two fundamental limitations: (i) global planning absence to structure multi-step reasoning, and (ii) unfaithful execution, which hinders effective query formulation and consistent use of retrieved evidence. We propose GlobalRAG, a reinforcement learning framework designed to enhance global reasoning in multi-hop QA. GlobalRAG decomposes questions into subgoals, coordinates retrieval with reasoning, and refines evidence iteratively. To guide this process, we introduce Planning Quality Reward and SubGoal Completion Reward, which encourage coherent planning and reliable subgoal execution. In addition, a progressive weight annealing strategy balances process-oriented and outcome-based objectives. Extensive experiments on both in-domain and out-of-domain benchmarks demonstrate that GlobalRAG significantly outperforms strong baselines while using only 8k training data (42% of the training data used by strong baselines), achieving average improvements of 14.2% in both EM and F1. |
| title | GlobalRAG: Enhancing Global Reasoning in Multi-hop Question Answering via Reinforcement Learning |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2510.20548 |