@inproceedings{xiong-etal-2025-mpo,
title = "{MPO}: Boosting {LLM} Agents with Meta Plan Optimization",
author = "Xiong, Weimin and
Song, Yifan and
Dong, Qingxiu and
Zhao, Bingchan and
Song, Feifan and
XWang and
Li, Sujian",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-emnlp.210/",
pages = "3914--3935",
ISBN = "979-8-89176-335-7",
abstract = "Recent advancements in large language models (LLMs) have enabled LLM-based agents to successfully tackle interactive planning tasks. However, despite their successes, existing approaches often suffer from planning hallucinations and require retraining for each new agent. To address these challenges, we propose the **M**eta **P**lan **O**ptimization (**MPO**) framework, , which enhances agent planning capabilities by directly incorporating explicit guidance. Unlike previous methods that rely on complex knowledge, which either require significant human effort or lack quality assurance, MPO leverages high-level general guidance through meta plans to assist agent planning and enables continuous optimization of the meta plans based on feedback from the agent{'}s task execution. Our experiments conducted on two representative tasks demonstrate that MPO significantly outperforms existing baselines. Moreover, our analysis indicates that MPO provides a plug-and-play solution that enhances both task completion efficiency and generalization capabilities in previous unseen scenarios."
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<abstract>Recent advancements in large language models (LLMs) have enabled LLM-based agents to successfully tackle interactive planning tasks. However, despite their successes, existing approaches often suffer from planning hallucinations and require retraining for each new agent. To address these challenges, we propose the **M**eta **P**lan **O**ptimization (**MPO**) framework, , which enhances agent planning capabilities by directly incorporating explicit guidance. Unlike previous methods that rely on complex knowledge, which either require significant human effort or lack quality assurance, MPO leverages high-level general guidance through meta plans to assist agent planning and enables continuous optimization of the meta plans based on feedback from the agent’s task execution. Our experiments conducted on two representative tasks demonstrate that MPO significantly outperforms existing baselines. Moreover, our analysis indicates that MPO provides a plug-and-play solution that enhances both task completion efficiency and generalization capabilities in previous unseen scenarios.</abstract>
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%0 Conference Proceedings
%T MPO: Boosting LLM Agents with Meta Plan Optimization
%A Xiong, Weimin
%A Song, Yifan
%A Dong, Qingxiu
%A Zhao, Bingchan
%A Song, Feifan
%A Li, Sujian
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%A XWang
%S Findings of the Association for Computational Linguistics: EMNLP 2025
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-335-7
%F xiong-etal-2025-mpo
%X Recent advancements in large language models (LLMs) have enabled LLM-based agents to successfully tackle interactive planning tasks. However, despite their successes, existing approaches often suffer from planning hallucinations and require retraining for each new agent. To address these challenges, we propose the **M**eta **P**lan **O**ptimization (**MPO**) framework, , which enhances agent planning capabilities by directly incorporating explicit guidance. Unlike previous methods that rely on complex knowledge, which either require significant human effort or lack quality assurance, MPO leverages high-level general guidance through meta plans to assist agent planning and enables continuous optimization of the meta plans based on feedback from the agent’s task execution. Our experiments conducted on two representative tasks demonstrate that MPO significantly outperforms existing baselines. Moreover, our analysis indicates that MPO provides a plug-and-play solution that enhances both task completion efficiency and generalization capabilities in previous unseen scenarios.
%U https://aclanthology.org/2025.findings-emnlp.210/
%P 3914-3935
Markdown (Informal)
[MPO: Boosting LLM Agents with Meta Plan Optimization](https://aclanthology.org/2025.findings-emnlp.210/) (Xiong et al., Findings 2025)
ACL
- Weimin Xiong, Yifan Song, Qingxiu Dong, Bingchan Zhao, Feifan Song, XWang, and Sujian Li. 2025. MPO: Boosting LLM Agents with Meta Plan Optimization. In Findings of the Association for Computational Linguistics: EMNLP 2025, pages 3914–3935, Suzhou, China. Association for Computational Linguistics.