Improving End-to-End Training of Retrieval-Augmented Generation Models via Joint Stochastic Approximation

arXiv:2508.18168v3 Announce Type: replace Abstract: Retrieval-augmented generation (RAG) has become a widely recognized paradigm to combine parametric memory with non-parametric memories. An RAG model consists of two serial connecting components (retriever and generator). A major challenge in end-to-end optimization of the RAG model is that marginalization over relevant passages (modeled as discrete latent variables) from a knowledge base is required. Traditional top-K marginalization and variational RAG (VRAG) suffer from biased or high-variance gradient estimates. In this paper, we propose and develop joint stochastic approximation (JSA) based end-to-end training of RAG, which is referred to as JSA-RAG. The JSA algorithm is a stochastic extension of the EM (expectation-maximization) algorithm and is particularly powerful in estimating discrete latent variable models. Extensive experiments are conducted on five datasets for two tasks (open-domain question answering, knowledge-grounded dialogs) and show that JSA-RAG significantly outperforms both vanilla RAG and VRAG. Further analysis shows the efficacy of JSA-RAG from the perspectives of generation, retrieval, and low-variance gradient estimate.

Related

• MegaRAG: Multimodal Knowledge Graph-Based Retrieval Augmented Generation
• Guaranteeing Knowledge Integration with Joint Decoding for Retrieval-Augmented Generation
• Self-Correcting RAG: Enhancing Faithfulness via MMKP Context Selection and NLI-Guided MCTS
• Feedback Adaptation for Retrieval-Augmented Generation
• Beyond Black-Box Interventions: Latent Probing for Faithful Retrieval-Augmented Generation
• LTRR: Learning To Rank Retrievers for LLMs

Source: arXiv cs.CL | 2026-04-23