Agentic-R: Learning to Retrieve for Agentic Search
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.
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Wenhan Liu
69707ea4a8be625b19c2b00e
Xinyu Ma
69707ea4a8be625b19c2b00f
Yutao Zhu
69707ea4a8be625b19c2b010
Yuchen Li
69707ea4a8be625b19c2b011
Daiting Shi
69707ea4a8be625b19c2b012
Dawei Yin
69707ea4a8be625b19c2b013
Zhicheng Dou
claimed_verified
2026-02-10T09:08:24.637Z
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/avatars/be97941e60064e5dd806c6fe9db3c537.svg
Zhicheng Dou
douzc
user
douzc
Agentic-R: Learning to Retrieve for Agentic Search
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.
A novel retriever training framework for agentic search that uses both local relevance and global answer correctness metrics with iterative optimization between the search agent and retriever.
agentic search multi-step reasoning on-demand retrieval similarity-based retrievers retrieval-augmented generation multi-turn agentic search iterative training strategy bidirectional optimization evolving queries answer correctness passage utility