@inproceedings{zhang-etal-2026-criticsearch,
title = "{C}ritic{S}earch: Fine-Grained Credit Assignment for Search Agents via a Retrospective Critic",
author = "Zhang, Yaocheng and
Huang, Haohuan and
Song, Zijun and
Zhao, Zijie and
Zhang, Qichao and
Zhu, Yuanheng and
Zhao, Dongbin",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.596/",
pages = "12272--12290",
ISBN = "979-8-89176-395-1",
abstract = "Tool-Integrated Reasoning (TIR) with search engines enables large language models to iteratively retrieve up-to-date external knowledge, enhancing adaptability and generalization in complex question-answering tasks. However, existing search agent pipelines typically depend on reinforcement learning based optimization, which often suffers from sparse outcome rewards, leading to inefficient exploration and unstable training. We introduce CriticSearch, a fine-grained credit-assignment framework that supplies dense, turn-level feedback via a retrospective critic mechanism. During training, a frozen, asymmetric critique LLM retrospectively evaluates each turn using privileged information from the full trajectory and gold answers, converting these assessments into stable, dense rewards that guide policy improvement. Experimental results across diverse multi-hop reasoning benchmarks demonstrate that CriticSearch consistently outperforms existing baselines, achieving faster convergence, improved training stability, and higher performance."
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<abstract>Tool-Integrated Reasoning (TIR) with search engines enables large language models to iteratively retrieve up-to-date external knowledge, enhancing adaptability and generalization in complex question-answering tasks. However, existing search agent pipelines typically depend on reinforcement learning based optimization, which often suffers from sparse outcome rewards, leading to inefficient exploration and unstable training. We introduce CriticSearch, a fine-grained credit-assignment framework that supplies dense, turn-level feedback via a retrospective critic mechanism. During training, a frozen, asymmetric critique LLM retrospectively evaluates each turn using privileged information from the full trajectory and gold answers, converting these assessments into stable, dense rewards that guide policy improvement. Experimental results across diverse multi-hop reasoning benchmarks demonstrate that CriticSearch consistently outperforms existing baselines, achieving faster convergence, improved training stability, and higher performance.</abstract>
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%0 Conference Proceedings
%T CriticSearch: Fine-Grained Credit Assignment for Search Agents via a Retrospective Critic
%A Zhang, Yaocheng
%A Huang, Haohuan
%A Song, Zijun
%A Zhao, Zijie
%A Zhang, Qichao
%A Zhu, Yuanheng
%A Zhao, Dongbin
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F zhang-etal-2026-criticsearch
%X Tool-Integrated Reasoning (TIR) with search engines enables large language models to iteratively retrieve up-to-date external knowledge, enhancing adaptability and generalization in complex question-answering tasks. However, existing search agent pipelines typically depend on reinforcement learning based optimization, which often suffers from sparse outcome rewards, leading to inefficient exploration and unstable training. We introduce CriticSearch, a fine-grained credit-assignment framework that supplies dense, turn-level feedback via a retrospective critic mechanism. During training, a frozen, asymmetric critique LLM retrospectively evaluates each turn using privileged information from the full trajectory and gold answers, converting these assessments into stable, dense rewards that guide policy improvement. Experimental results across diverse multi-hop reasoning benchmarks demonstrate that CriticSearch consistently outperforms existing baselines, achieving faster convergence, improved training stability, and higher performance.
%U https://aclanthology.org/2026.findings-acl.596/
%P 12272-12290
Markdown (Informal)
[CriticSearch: Fine-Grained Credit Assignment for Search Agents via a Retrospective Critic](https://aclanthology.org/2026.findings-acl.596/) (Zhang et al., Findings 2026)
ACL
- Yaocheng Zhang, Haohuan Huang, Zijun Song, Zijie Zhao, Qichao Zhang, Yuanheng Zhu, and Dongbin Zhao. 2026. CriticSearch: Fine-Grained Credit Assignment for Search Agents via a Retrospective Critic. In Findings of the Association for Computational Linguistics: ACL 2026, pages 12272–12290, San Diego, California, United States. Association for Computational Linguistics.