Avoiding Search Behavior Homogenization in Agentic RAG with Query-and-Document Trajectory Steering

Agentic retrieval-augmented generation (RAG) integrates dynamic search tool calling with reinforcement learning with verifiable rewards (RLVR) to overcome the static parametric knowledge of LLMs. However, training a single policy across diverse search patterns induces behavioral conflicts, causing the model to collapse onto an average search pattern, which we call **behavioral homogenization**. Since existing approaches leverage extra compute or memory for explicit planning and self-correction, they incur significant computational inefficiency and fail to foster positive transfer among similar search patterns while disentangling conflicting search patterns. To overcome this challenge, we propose **query-and-document trajectory steering** (**QDTS**), a framework designed to maximize cooperative learning across complementary search patterns while suppressing conflicting signals with minimal cost, by steering generation with query and document contexts. Specifically, **query-side pre-rollout steering** categorizes queries into structural patterns via a rule-based parser, supplying corresponding class contexts to guide targeted reasoning pathways during both training and inference. Furthermore, **document-side mid-rollout steering** evaluates online retrieval statistics and deploys dynamic mid-execution contexts across multi-turn rollouts to identify search failures and steer subsequent actions. By integrating this two-stage mechanism to generate and optimize agentic search trajectories, QDTS effectively learns an adaptive policy across diverse query patterns, outperforming the state-of-the-art method by 9.2% on average across comprehensive agentic RAG benchmarks.