Stop-RAG: Value-Based Retrieval Control for Iterative RAG

• URL: http://arxiv.org/abs/2510.14337v1
• Date: Thu, 16 Oct 2025 06:17:38 GMT
• Title: Stop-RAG: Value-Based Retrieval Control for Iterative RAG
• Authors: Jaewan Park, Solbee Cho, Jay-Yoon Lee,
• Abstract summary: Iterative retrieval-augmented generation (RAG) enables large language models to answer complex multi-hop questions.<n>Existing methods either use a predetermined number of iterations or rely on confidence proxies that poorly reflect whether more retrieval will actually help.<n>We introduce Stop-RAG, a value-based controller that adaptively decides when to stop retrieving.
• Score: 10.378290102256534
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Iterative retrieval-augmented generation (RAG) enables large language models to answer complex multi-hop questions, but each additional loop increases latency, costs, and the risk of introducing distracting evidence, motivating the need for an efficient stopping strategy. Existing methods either use a predetermined number of iterations or rely on confidence proxies that poorly reflect whether more retrieval will actually help. We cast iterative RAG as a finite-horizon Markov decision process and introduce Stop-RAG, a value-based controller that adaptively decides when to stop retrieving. Trained with full-width forward-view Q($\lambda$) targets from complete trajectories, Stop-RAG learns effective stopping policies while remaining compatible with black-box APIs and existing pipelines. On multi-hop question-answering benchmarks, Stop-RAG consistently outperforms both fixed-iteration baselines and prompting-based stopping with LLMs. These results highlight adaptive stopping as a key missing component in current agentic systems, and demonstrate that value-based control can improve the accuracy of RAG systems.

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