@inproceedings{cao-etal-2025-pgpo,
title = "{PGPO}: Enhancing Agent Reasoning via Pseudocode-style Planning Guided Preference Optimization",
author = "Cao, Zouying and
Wang, Runze and
Yang, Yifei and
Ma, Xinbei and
Zhu, Xiaoyong and
Zheng, Bo and
Zhao, Hai",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-acl.774/",
doi = "10.18653/v1/2025.findings-acl.774",
pages = "14966--14985",
ISBN = "979-8-89176-256-5",
abstract = "Large Language Model (LLM) agents have demonstrated impressive capabilities in handling complex interactive problems. Existing LLM agents mainly generate natural language plans to guide reasoning, which is verbose and inefficient. NL plans are also tailored to specific tasks and restrict agents' ability to generalize across similar tasks. To this end, we explore pseudocode-style plans (P-code Plan) to capture the structural logic of reasoning. We find that P-code Plan empowers LLM agents with stronger generalization ability and more efficiency. Inspired by this finding, we propose a pseudocode-style $\underline{P}$lanning $\underline{G}$uided $\underline{P}$reference $\underline{O}$ptimization method called PGPO for effective agent learning. With two planning-oriented rewards, PGPO further enhances LLM agents' ability to generate high-quality P-code Plans and subsequent reasoning. Experiments show that PGPO achieves superior performance on representative agent benchmarks and outperforms the current leading baselines. Analyses reveal the advantage of PGPO in reducing action errors and omissions during reasoning."
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<abstract>Large Language Model (LLM) agents have demonstrated impressive capabilities in handling complex interactive problems. Existing LLM agents mainly generate natural language plans to guide reasoning, which is verbose and inefficient. NL plans are also tailored to specific tasks and restrict agents’ ability to generalize across similar tasks. To this end, we explore pseudocode-style plans (P-code Plan) to capture the structural logic of reasoning. We find that P-code Plan empowers LLM agents with stronger generalization ability and more efficiency. Inspired by this finding, we propose a pseudocode-style \underlinePlanning \underlineGuided \underlinePreference \underlineOptimization method called PGPO for effective agent learning. With two planning-oriented rewards, PGPO further enhances LLM agents’ ability to generate high-quality P-code Plans and subsequent reasoning. Experiments show that PGPO achieves superior performance on representative agent benchmarks and outperforms the current leading baselines. Analyses reveal the advantage of PGPO in reducing action errors and omissions during reasoning.</abstract>
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%0 Conference Proceedings
%T PGPO: Enhancing Agent Reasoning via Pseudocode-style Planning Guided Preference Optimization
%A Cao, Zouying
%A Wang, Runze
%A Yang, Yifei
%A Ma, Xinbei
%A Zhu, Xiaoyong
%A Zheng, Bo
%A Zhao, Hai
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Findings of the Association for Computational Linguistics: ACL 2025
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-256-5
%F cao-etal-2025-pgpo
%X Large Language Model (LLM) agents have demonstrated impressive capabilities in handling complex interactive problems. Existing LLM agents mainly generate natural language plans to guide reasoning, which is verbose and inefficient. NL plans are also tailored to specific tasks and restrict agents’ ability to generalize across similar tasks. To this end, we explore pseudocode-style plans (P-code Plan) to capture the structural logic of reasoning. We find that P-code Plan empowers LLM agents with stronger generalization ability and more efficiency. Inspired by this finding, we propose a pseudocode-style \underlinePlanning \underlineGuided \underlinePreference \underlineOptimization method called PGPO for effective agent learning. With two planning-oriented rewards, PGPO further enhances LLM agents’ ability to generate high-quality P-code Plans and subsequent reasoning. Experiments show that PGPO achieves superior performance on representative agent benchmarks and outperforms the current leading baselines. Analyses reveal the advantage of PGPO in reducing action errors and omissions during reasoning.
%R 10.18653/v1/2025.findings-acl.774
%U https://aclanthology.org/2025.findings-acl.774/
%U https://doi.org/10.18653/v1/2025.findings-acl.774
%P 14966-14985
Markdown (Informal)
[PGPO: Enhancing Agent Reasoning via Pseudocode-style Planning Guided Preference Optimization](https://aclanthology.org/2025.findings-acl.774/) (Cao et al., Findings 2025)
ACL