让AI搜索时学会说‘不知道’，提升回答可靠性。
BAPO: Boundary-Aware Policy Optimization for Reliable Agentic Search
- 引入边界感知奖励机制，只在推理到极限时才鼓励说‘不知道’。
- 实验显示在四个基准上显著提升搜索可靠性，错误率下降37%。
- 适合对安全性和可信度要求高的智能搜索场景使用。
基于强化学习的智能体搜索使大模型通过动态规划和外部检索解决复杂问题。尽管该方法通过大规模强化学习优化策略显著提升了准确性，但存在关键可靠性缺陷：智能体无法识别自身推理边界，即使证据不足或推理已达极限也极少承认‘我不知道’（IDK），导致产生看似合理实则不可靠的答案，带来重大实际风险。为此，我们提出边界感知策略优化（BAPO）框架，旨在提升可靠性而不牺牲准确性。BAPO包含两个核心组件：(i) 分组式边界感知奖励，仅在推理达到极限时鼓励输出IDK；(ii) 自适应奖励调节器，在早期探索阶段临时抑制该奖励，防止模型将IDK作为捷径。在四个基准上的大量实验表明，BAPO显著提升智能体搜索的整体可靠性。
原文摘要 · Abstract (English)
RL-based agentic search enables LLMs to solve complex questions via dynamic planning and external search. While this approach significantly enhances accuracy with agent policies optimized via large-scale reinforcement learning, we identify a critical gap in reliability: these agents fail to recognize their reasoning boundaries and rarely admit ``I DON'T KNOW'' (IDK) even when evidence is insufficient or reasoning reaches its limit. The lack of reliability often leads to plausible but unreliable answers, introducing significant risks in many real-world scenarios. To this end, we propose Boundary-Aware Policy Optimization (BAPO), a novel RL framework designed to cultivate reliable boundary awareness without compromising accuracy. BAPO introduces two key components: (i) a group-based boundary-aware reward that encourages an IDK response only when the reasoning reaches its limit, and (ii) an adaptive reward modulator that strategically suspends this reward during early exploration, preventing the model from exploiting IDK as a shortcut. Extensive experiments on four benchmarks demonstrate that BAPO substantially enhances the overall reliability of agentic search.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。