让大模型自动决定何时停止检索，提升问答准确率
Stop-RAG: Value-Based Retrieval Control for Iterative RAG
- 将多跳检索过程建模为马尔可夫决策过程，用价值函数指导停止时机
- 在多个基准上优于固定次数检索和基于提示的停止方法，准确率显著提升
- 无需修改现有系统，可直接接入黑盒API，适合部署在实际对话系统中
迭代式检索增强生成（RAG）使大语言模型能够回答复杂的多跳问题，但每次循环都会增加延迟、成本和引入干扰信息的风险，因此需要高效的停止策略。现有方法要么采用固定的迭代次数，要么依赖信心代理，而这些代理无法准确反映进一步检索是否真正有益。本文将迭代RAG建模为有限时域马尔可夫决策过程，提出基于价值的停止控制器Stop-RAG，能自适应地决定何时停止检索。该模型通过完整轨迹的全宽前向视图Q(λ)目标进行训练，学习有效的停止策略，同时兼容黑盒API和现有流水线。在多跳问答基准测试中，Stop-RAG始终优于固定迭代基线和基于LLM提示的停止方法。结果表明，自适应停止是当前智能体系统的关键缺失组件，且基于价值的控制可有效提升RAG系统的准确性。
原文摘要 · Abstract (English)
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($λ$) 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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