Search-P1: Path-Centric Reward Shaping for Stable and Efficient Agentic RAG Training
arXiv:2602.22576v1 Announce Type: new 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 perspec
arXiv:2602.22576v1 Announce Type: new 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.
Executive Summary
This article proposes Search-P1, a framework that addresses the limitations of traditional Retrieval-Augmented Generation (RAG) training methods. By introducing path-centric reward shaping, Search-P1 enables the evaluation of structural quality in reasoning trajectories, extracting learning signals even from failed samples. Experiments on multiple QA benchmarks demonstrate significant improvements over strong baselines, with an average accuracy gain of 7.7 points. This framework has the potential to enhance the efficiency and effectiveness of RAG training, enabling large language models to tackle complex multi-step reasoning tasks. The approach also opens up new possibilities for training RAG models in real-world applications.
Key Points
- ▸ Search-P1 introduces path-centric reward shaping for agentic RAG training
- ▸ Path-Centric Reward evaluates the structural quality of reasoning trajectories
- ▸ Dual-Track Path Scoring assesses paths from self-consistency and reference-alignment perspectives
Merits
Strength in addressing sparse outcome rewards
Search-P1's path-centric reward shaping extracts learning signals from failed samples, addressing the issue of sparse outcome rewards in traditional RL-based training methods
Improvement in sample efficiency
Search-P1's approach enables the evaluation of intermediate signals, leading to improved sample efficiency and reduced computational costs
Enhanced agentic RAG training
Search-P1's framework enables the dynamic decision-making of LLMs, allowing for more efficient and effective agentic RAG training
Demerits
Potential for overfitting
The use of offline-generated reference planners may lead to overfitting, especially if the planners are not representative of the target domain
Dependence on quality of reference planners
The effectiveness of Search-P1 relies heavily on the quality of the reference planners, which may not always be feasible to generate or maintain
Expert Commentary
While Search-P1 demonstrates significant improvements over strong baselines, the approach relies heavily on the quality of the reference planners. This limitation highlights the need for further research on the development of more robust and representative reference planners. Additionally, the potential for overfitting should be carefully addressed to ensure the generalizability of Search-P1 in real-world applications. Nevertheless, the framework's ability to extract learning signals from failed samples and evaluate intermediate signals is a significant advancement in RAG training.
Recommendations
- ✓ Further research should focus on developing more robust and representative reference planners
- ✓ Investigate the potential of Search-P1 in other RAG training methods and language understanding applications
