Domain-Aware RAG: MoL-Enhanced RL for Efficient Training and Scalable Retrieval
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866916939801034752 |
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| author | Lin, Hao Xie, Peitong Chen, Jingxue Lin, Jie Tang, Qingkun Lu, Qianchun |
| author_facet | Lin, Hao Xie, Peitong Chen, Jingxue Lin, Jie Tang, Qingkun Lu, Qianchun |
| contents | Retrieval-Augmented Generation (RAG) systems rely heavily on the retrieval stage, particularly the coarse-ranking process. Existing coarse-ranking optimization approaches often struggle to balance domain-specific knowledge learning with query enhencement, resulting in suboptimal retrieval performance. To address this challenge, we propose MoLER, a domain-aware RAG method that uses MoL-Enhanced Reinforcement Learning to optimize retrieval. MoLER has a two-stage pipeline: a continual pre-training (CPT) phase using a Mixture of Losses (MoL) to balance domain-specific knowledge with general language capabilities, and a reinforcement learning (RL) phase leveraging Group Relative Policy Optimization (GRPO) to optimize query and passage generation for maximizing document recall. A key innovation is our Multi-query Single-passage Late Fusion (MSLF) strategy, which reduces computational overhead during RL training while maintaining scalable inference via Multi-query Multi-passage Late Fusion (MMLF). Extensive experiments on benchmark datasets show that MoLER achieves state-of-the-art performance, significantly outperforming baseline methods. MoLER bridges the knowledge gap in RAG systems, enabling robust and scalable retrieval in specialized domains. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2509_06650 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Domain-Aware RAG: MoL-Enhanced RL for Efficient Training and Scalable Retrieval Lin, Hao Xie, Peitong Chen, Jingxue Lin, Jie Tang, Qingkun Lu, Qianchun Computation and Language Information Retrieval Retrieval-Augmented Generation (RAG) systems rely heavily on the retrieval stage, particularly the coarse-ranking process. Existing coarse-ranking optimization approaches often struggle to balance domain-specific knowledge learning with query enhencement, resulting in suboptimal retrieval performance. To address this challenge, we propose MoLER, a domain-aware RAG method that uses MoL-Enhanced Reinforcement Learning to optimize retrieval. MoLER has a two-stage pipeline: a continual pre-training (CPT) phase using a Mixture of Losses (MoL) to balance domain-specific knowledge with general language capabilities, and a reinforcement learning (RL) phase leveraging Group Relative Policy Optimization (GRPO) to optimize query and passage generation for maximizing document recall. A key innovation is our Multi-query Single-passage Late Fusion (MSLF) strategy, which reduces computational overhead during RL training while maintaining scalable inference via Multi-query Multi-passage Late Fusion (MMLF). Extensive experiments on benchmark datasets show that MoLER achieves state-of-the-art performance, significantly outperforming baseline methods. MoLER bridges the knowledge gap in RAG systems, enabling robust and scalable retrieval in specialized domains. |
| title | Domain-Aware RAG: MoL-Enhanced RL for Efficient Training and Scalable Retrieval |
| topic | Computation and Language Information Retrieval |
| url | https://arxiv.org/abs/2509.06650 |