Beyond Stochastic Exploration: What Makes Training Data Valuable for Agentic Search

TL;DR

This paper introduces Hierarchical Experience (HiExp), a framework that improves reinforcement learning-based search agents by transforming raw reasoning trajectories into hierarchical knowledge, enhancing training stability and performance.

Contribution

The paper presents HiExp, a novel hierarchical experience framework that regularizes exploration and boosts the efficiency and stability of RL-based search agents.

Findings

Achieves significant performance improvements on complex reasoning benchmarks.

Demonstrates strong generalization across tasks and algorithms.

Enhances training stability of RL-based search agents.

Abstract

Reinforcement learning (RL) has become an effective approach for advancing the reasoning capabilities of large language models (LLMs) through the strategic integration of external search engines. However, current RL-based search agents often rely on a process of stochastic exploration guided by carefully crafted outcome rewards, leading to inefficient reasoning trajectories and unstable training. To address these issues, we propose a novel framework, Hierarchical Experience (HiExp), to enhance the performance and training stability of search agents. Specifically, we extract empirical knowledge through contrastive analysis and a multi-level clustering mechanism, transforming raw reasoning trajectories into hierarchical experience knowledge. By leveraging experience-aligned training, we effectively regularize stochastic exploration, evolving it into a strategic and experience-driven search process. Extensive evaluations on multiple complex agentic search and mathematical reasoning benchmarks demonstrate that our approach not only achieves substantial performance gains but also exhibits strong cross-task and cross-algorithm generalization.