Beneficial Reasoning Behaviors in Agentic Search and Effective Post-training to Obtain Them

• URL: http://arxiv.org/abs/2510.06534v1
• Date: Wed, 08 Oct 2025 00:20:35 GMT
• Title: Beneficial Reasoning Behaviors in Agentic Search and Effective Post-training to Obtain Them
• Authors: Jiahe Jin, Abhijay Paladugu, Chenyan Xiong,
• Abstract summary: We propose a reasoning-driven pipeline to study effective reasoning behavior patterns in agentic search.<n>We identify four beneficial reasoning behaviors: Information Verification, Authority Evaluation, Adaptive Search, and Error Recovery.<n>We show that behavior priming yields over 35% gains in Llama3.2-3B and Qwen3-1.7B compared to directly training agentic search models with RL.
• Score: 23.986035712600657
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Agentic search leverages large language models (LLMs) to interpret complex user information needs and execute a multi-step process of planning, searching, and synthesizing information to provide answers. This paradigm introduces unique challenges for LLMs' reasoning and agentic capabilities when interacting with retrieval systems and the broader web. In this paper, we propose a reasoning-driven LLM-based pipeline to study effective reasoning behavior patterns in agentic search. Using this pipeline, we analyze successful agentic search trajectories and identify four beneficial reasoning behaviors: Information Verification, Authority Evaluation, Adaptive Search, and Error Recovery. Based on these findings, we propose a technique called Behavior Priming to train more effective agentic search models. It synthesizes agentic search trajectories that exhibit these four behaviors and integrates them into the agentic search model through supervised fine-tuning (SFT), followed by standard reinforcement learning (RL). Experiments on three benchmarks (GAIA, WebWalker, and HLE) demonstrate that behavior priming yields over 35% gains in Llama3.2-3B and Qwen3-1.7B compared to directly training agentic search models with RL. Crucially, we demonstrate that the desired reasoning behaviors in the SFT data, rather than the correctness of the final answer, is the critical factor for achieving strong final performance after RL: fine-tuning on trajectories with desirable reasoning behaviors but incorrect answers leads to better performance than fine-tuning on trajectories with correct answers. Our analysis further reveals the underlying mechanism: the introduced reasoning behaviors endow models with more effective exploration (higher pass@k and entropy) and test-time scaling (longer trajectories) capabilities, providing a strong foundation for RL. Our code will be released as open source.

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