Search-P1: Path-Centric Reward Shaping for Stable and Efficient Agentic RAG Training

• URL: http://arxiv.org/abs/2602.22576v1
• Date: Thu, 26 Feb 2026 03:31:00 GMT
• Title: Search-P1: Path-Centric Reward Shaping for Stable and Efficient Agentic RAG Training
• Authors: Tianle Xia, Ming Xu, Lingxiang Hu, Yiding Sun, Wenwei Li, Linfang Shang, Liqun Liu, Peng Shu, Huan Yu, Jie Jiang,
• Abstract summary: Agentic RAG enhances large language models by incorporating external knowledge.<n>Current RL-based training methods suffer from sparse outcome rewards that discard intermediate signals.<n>We propose Search-P1, a framework that introduces path-centric reward shaping for agentic RAG training.
• Score: 11.136092421166097
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Retrieval-Augmented Generation (RAG) enhances large language models (LLMs) by incorporating external knowledge, yet traditional single-round retrieval struggles with complex multi-step reasoning. Agentic RAG addresses this by enabling LLMs to dynamically decide when and what to retrieve, but current RL-based training methods suffer from sparse outcome rewards that discard intermediate signals and low sample efficiency where failed samples contribute nothing. We propose Search-P1, a framework that introduces path-centric reward shaping for agentic RAG training, comprising two key components: (1) Path-Centric Reward, which evaluates the structural quality of reasoning trajectories through order-agnostic step coverage and soft scoring that extracts learning signals even from failed samples, and (2) Dual-Track Path Scoring with offline-generated reference planners that assesses paths from both self-consistency and reference-alignment perspectives. Experiments on multiple QA benchmarks demonstrate that Search-P1 achieves significant improvements over Search-R1 and other strong baselines, with an average accuracy gain of 7.7 points.

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