Agentic-R：面向智能搜索的检索学习系统

Agentic-R: Learning to Retrieve for Agentic Search

January 17, 2026

作者: Wenhan Liu, Xinyu Ma, Yutao Zhu, Yuchen Li, Daiting Shi, Dawei Yin, Zhicheng Dou

cs.AI

摘要

近期，智能体搜索（Agentic Search）作为一种新兴的强大范式崭露头角，其通过智能体将多步推理与按需检索相结合来解决复杂问题。尽管该模式已取得显著成效，但如何为其设计专用检索器仍属探索不足的领域。现有搜索智能体通常依赖基于相似度的检索器，然而相似文本片段并非总能有效支撑最终答案的生成。本文提出一种专为智能体搜索设计的新型检索器训练框架。与面向单轮检索增强生成（RAG）的检索器仅关注局部段落效用不同，我们提出在多轮智能体搜索中同时利用局部查询-段落相关性和全局答案正确性来衡量段落效用。进一步引入迭代训练策略，使搜索智能体与检索器在双向互动中循环优化。相较于仅通过固定问题一次性训练的RAG检索器，我们的方法能持续利用智能体生成的动态演进且更高质量的查询进行改进。在七个单跳及多跳问答基准上的大量实验表明，本研究所提出的检索器（命名为）在不同搜索智能体上均能稳定超越现有强基线模型。代码已开源：https://github.com/8421BCD/Agentic-R。

English

Agentic search has recently emerged as a powerful paradigm, where an agent interleaves multi-step reasoning with on-demand retrieval to solve complex questions. Despite its success, how to design a retriever for agentic search remains largely underexplored. Existing search agents typically rely on similarity-based retrievers, while similar passages are not always useful for final answer generation. In this paper, we propose a novel retriever training framework tailored for agentic search. Unlike retrievers designed for single-turn retrieval-augmented generation (RAG) that only rely on local passage utility, we propose to use both local query-passage relevance and global answer correctness to measure passage utility in a multi-turn agentic search. We further introduce an iterative training strategy, where the search agent and the retriever are optimized bidirectionally and iteratively. Different from RAG retrievers that are only trained once with fixed questions, our retriever is continuously improved using evolving and higher-quality queries from the agent. Extensive experiments on seven single-hop and multi-hop QA benchmarks demonstrate that our retriever, termed , consistently outperforms strong baselines across different search agents. Our codes are available at: https://github.com/8421BCD/Agentic-R.