---
id: 20261024-T0-07
title: "让检索Agent知道自己“查没查到”：自评估强化学习方法"
title_en: "Self-Assessment RL Teaches Search Agents to Know What They've Found"
url: https://ai.daily.yangsir.net/daily/20261024-T0-07
issue_date: 2026-10-24
publish_date: 2026-10-23T04:00:00.000Z
category: research
source_name: "arXiv cs.CL (NLP)"
source_url: https://arxiv.org/abs/2610.30435
---

# 让检索Agent知道自己“查没查到”：自评估强化学习方法

arXiv 新论文针对检索增强大模型中的搜索 Agent 提出改进。这类 Agent 能通过检索外部信息处理复杂任务，但普遍缺乏“搜索状态感知”，即无法判断自己已经掌握了哪些信息、还缺什么。论文用自评估强化学习训练这种能力，让模型在检索过程中评估自身信息状态并据此调整后续搜索。实际影响：搜索 Agent 可以减少无效检索轮次，在多步问答和需要多次查证的任务中表现更稳。

## English Version

**Self-Assessment RL Teaches Search Agents to Know What They've Found**

A new arXiv paper targets search agents in retrieval-augmented LLMs. These agents can handle complex tasks by retrieving and synthesizing external information, but they often lack search-state awareness, meaning they can't tell what information they already have and what's still missing. The paper trains this ability with self-assessment reinforcement learning, so the model evaluates its own information state during search and adjusts subsequent retrieval accordingly. Practical impact: fewer wasted retrieval rounds and more reliable performance on multi-step QA and tasks that require repeated verification.

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**来源**：[arXiv cs.CL (NLP)](https://arxiv.org/abs/2610.30435)

**详情页**：https://ai.daily.yangsir.net/daily/20261024-T0-07

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