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 \ours{}, consistently outperforms strong baselines across different search agents. Our codes are available at: https://github.com/8421BCD/Agentic-R.

翻译：智能体搜索作为一种新兴的强大范式，通过智能体在多步推理与按需检索之间的交替执行来解决复杂问题。尽管该范式已取得显著成效，但如何为智能体搜索设计检索器仍存在大量未探索的空间。现有搜索智能体通常依赖基于相似性的检索器，然而相似文本段落并非总能对最终答案生成产生助益。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。与为单轮检索增强生成（RAG）设计的仅依赖局部段落效用的检索器不同，我们提出同时利用局部查询-段落相关性与全局答案正确性来衡量多轮智能体搜索中的段落效用。进一步引入迭代训练策略，使搜索智能体与检索器能够进行双向迭代优化。区别于仅通过固定问题单次训练的RAG检索器，我们的检索器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。在七个单跳与多跳问答基准测试上的大量实验表明，我们提出的检索器（命名为\ours{}）在不同搜索智能体中均持续优于现有强基线模型。代码已开源：https://github.com/8421BCD/Agentic-R。