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    <abstract>Reward-guided search methods have demonstrated strong potential in enhancing tool-using agents by effectively guiding sampling and exploration over complex action spaces. As a core design, those search methods utilize process reward models (PRMs) to provide step-level rewards, enabling more fine-grained monitoring. However, there is a lack of systematic and reliable evaluation benchmarks for PRMs in tool-use settings. In this paper, we introduce ToolPRMBench, a large-scale benchmark specifically designed to evaluate PRMs for tool-using agents. ToolPRMBench is built on top of several representative tool-use benchmarks and converts agent trajectories into step-level test cases. Each case contains the interaction history, a correct action, a plausible but incorrect alternative, and relevant tool metadata. We respectively utilize offline sampling to isolate local single-step errors and online sampling to capture realistic multi-step failures from full agent rollouts. A multi-LLM verification pipeline is proposed to reduce label noise and ensure data quality. We conduct extensive experiments across large language models, general PRMs, and tool-specialized PRMs on ToolPRMBench. The results reveal clear differences in PRM effectiveness and highlight the potential of specialized PRMs for tool-using. Our code and dataset are available at: https://github.com/David-Li0406/ToolPRMBench[More resources on LLM-as-a-judge are on the website: \ensuremath&lt;https://llm-as-a-judge.github.io\ensuremath&gt;].</abstract>
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%0 Conference Proceedings
%T ToolPRMBench: Evaluating and Advancing Process Reward Models for Tool-using Agents
%A Li, Dawei
%A Yao, Yuguang
%A Tan, Zhen
%A Liu, Huan
%A Guo, Ruocheng
%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 li-etal-2026-toolprmbench
%X Reward-guided search methods have demonstrated strong potential in enhancing tool-using agents by effectively guiding sampling and exploration over complex action spaces. As a core design, those search methods utilize process reward models (PRMs) to provide step-level rewards, enabling more fine-grained monitoring. However, there is a lack of systematic and reliable evaluation benchmarks for PRMs in tool-use settings. In this paper, we introduce ToolPRMBench, a large-scale benchmark specifically designed to evaluate PRMs for tool-using agents. ToolPRMBench is built on top of several representative tool-use benchmarks and converts agent trajectories into step-level test cases. Each case contains the interaction history, a correct action, a plausible but incorrect alternative, and relevant tool metadata. We respectively utilize offline sampling to isolate local single-step errors and online sampling to capture realistic multi-step failures from full agent rollouts. A multi-LLM verification pipeline is proposed to reduce label noise and ensure data quality. We conduct extensive experiments across large language models, general PRMs, and tool-specialized PRMs on ToolPRMBench. The results reveal clear differences in PRM effectiveness and highlight the potential of specialized PRMs for tool-using. Our code and dataset are available at: https://github.com/David-Li0406/ToolPRMBench[More resources on LLM-as-a-judge are on the website: \ensuremath<https://llm-as-a-judge.github.io\ensuremath>].
%U https://aclanthology.org/2026.findings-acl.602/
%P 12378-12391

Markdown (Informal)

[ToolPRMBench: Evaluating and Advancing Process Reward Models for Tool-using Agents](https://aclanthology.org/2026.findings-acl.602/) (Li et al., Findings 2026)

• ToolPRMBench: Evaluating and Advancing Process Reward Models for Tool-using Agents (Li et al., Findings 2026)

ACL

• Dawei Li, Yuguang Yao, Zhen Tan, Huan Liu, and Ruocheng Guo. 2026. ToolPRMBench: Evaluating and Advancing Process Reward Models for Tool-using Agents. In Findings of the Association for Computational Linguistics: ACL 2026, pages 12378–12391, San Diego, California, United States. Association for Computational Linguistics.