Domain-Aware RAG: MoL-Enhanced RL for Efficient Training and Scalable Retrieval
This addresses the challenge of balancing domain-specific knowledge with query enhancement in RAG systems for specialized domains, though it appears incremental as it builds on existing coarse-ranking optimization approaches.
The paper tackled the problem of suboptimal retrieval performance in Retrieval-Augmented Generation (RAG) systems by proposing MoLER, a domain-aware method that uses MoL-Enhanced Reinforcement Learning, achieving state-of-the-art results on benchmark datasets.
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.