Learning to Retrieve from Agent Trajectories

Information retrieval (IR) systems have traditionally been designed and trained for human users, with learning-to-rank methods relying heavily on large-scale human interaction logs such as clicks and dwell time. With the rapid emergence of large language model (LLM) powered search agents, however, retrieval is increasingly consumed by agents rather than human beings, and is embedded as a core component within multi-turn reasoning and action loops. In this setting, retrieval models trained under human-centric assumptions can be mismatched with the way agents issue intermediate queries and consume results. In this work, we argue that retrieval models for agentic search should be trained directly from agent interaction data. We study learning to retrieve from agent trajectories as a trajectory-supervised training setting, where supervision is derived from multi-step agent interactions. Through a systematic analysis of search agent trajectories, we identify key behavioral signals that reveal document utility, including browsing actions, unbrowsed rejections, and post-browse reasoning traces. Guided by these insights, we propose LRAT, a simple yet effective framework that mines high-quality retrieval supervision from agent trajectories and incorporates relevance intensity through weighted optimization. To instantiate this setting at scale, we deploy the Tongyi-DeepResearch-30B model on 10K InfoSeekQA queries with four retrievers, collecting 26,482 agent trajectories and constructing 91,713 training pairs. Extensive experiments on both in-domain and out-of-domain deep research benchmarks demonstrate that retrievers trained with LRAT consistently improve evidence recall, end-to-end task success, and execution efficiency across diverse agent architectures and scales. Our results highlight agent trajectories as a practical and scalable supervision source for retrieval in agentic search.