Search-G1：用表征内在奖励训练会检索的智能体 Search-G1: Grounded Search Agents via Representation-Based Intrinsic Rewards

精读摘要

检索增强的语言智能体应当只在必要时检索外部信息，并把答案建立在检索到的证据之上。现有外部奖励要么只有稀疏的结果监督，要么依赖昂贵的标注或 LLM 裁判；内部奖励如熵、似然、信息增益又缺乏「依据证据作答」的约束。Search-G1 提出基于表征的内在奖励，让智能体学会何时检索、如何依据证据作答，且训练成本更低。 Search-augmented language agents should retrieve only when necessary and ground answers in retrieved evidence. External rewards are either sparse or require costly annotation and LLM judges, while policy-side signals like entropy or information gain are ungrounded. Search-G1 introduces representation-based intrinsic rewards that encourage grounded retrieval without expensive supervision.