@inproceedings{xia-etal-2026-search,
title = "Search-P1: Path-Centric Reward Shaping for Stable and Efficient Agentic {RAG} Training",
author = "Xia, Tianle and
Xu, Ming and
Hu, Lingxiang and
Sun, Yiding and
Li, Wenwei and
Shang, Linfang and
Liu, Liqun and
Shu, Peng and
Yu, Huan and
Jiang, Jie",
editor = "Li, Yunyao and
Rehm, Georg and
Tu, Mei",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 6: Industry Track)",
month = jul,
year = "2026",
address = "San Diego, California, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-industry.36/",
doi = "10.18653/v1/2026.acl-industry.36",
pages = "517--534",
ISBN = "979-8-89176-394-4",
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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<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.</abstract>
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%0 Conference Proceedings
%T Search-P1: Path-Centric Reward Shaping for Stable and Efficient Agentic RAG Training
%A Xia, Tianle
%A Xu, Ming
%A Hu, Lingxiang
%A Sun, Yiding
%A Li, Wenwei
%A Shang, Linfang
%A Liu, Liqun
%A Shu, Peng
%A Yu, Huan
%A Jiang, Jie
%Y Li, Yunyao
%Y Rehm, Georg
%Y Tu, Mei
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, USA
%@ 979-8-89176-394-4
%F xia-etal-2026-search
%X 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.
%R 10.18653/v1/2026.acl-industry.36
%U https://aclanthology.org/2026.acl-industry.36/
%U https://doi.org/10.18653/v1/2026.acl-industry.36
%P 517-534
Markdown (Informal)
[Search-P1: Path-Centric Reward Shaping for Stable and Efficient Agentic RAG Training](https://aclanthology.org/2026.acl-industry.36/) (Xia et al., ACL 2026)
ACL
- Tianle Xia, Ming Xu, Lingxiang Hu, Yiding Sun, Wenwei Li, Linfang Shang, Liqun Liu, Peng Shu, Huan Yu, and Jie Jiang. 2026. Search-P1: Path-Centric Reward Shaping for Stable and Efficient Agentic RAG Training. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track), pages 517–534, San Diego, California, USA. Association for Computational Linguistics.