title: MPO: Boosting LLM Agents with Meta Plan Optimization
summary: 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 Meta Plan Optimization (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.
paper_authors._id: 67c7c3d073299239b63f5378
paper_authors.name: Weimin Xiong
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paper_authors._id: 67c7c3d073299239b63f5379
paper_authors.name: Yifan Song
paper_authors._id: 67c7c3d073299239b63f537a
paper_authors.name: Qingxiu Dong
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paper_authors.fullname: Qingxiu Dong
paper_authors.type: user
paper_authors.user: Rsy24
paper_authors._id: 67c7c3d073299239b63f537b
paper_authors.name: Bingchan Zhao
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paper_authors.fullname: Bingchan Zhao
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paper_authors.user: Adagio
paper_authors._id: 67c7c3d073299239b63f537c
paper_authors.name: Feifan Song
paper_authors.status: admin_assigned
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paper_authors.avatarUrl: /avatars/5049856b5ed1b74533fff902e14b4c7c.svg
paper_authors.fullname: Feifan Song
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paper_authors.user: songff
paper_authors._id: 67c7c3d073299239b63f537d
paper_authors.name: Xun Wang
paper_authors._id: 67c7c3d073299239b63f537e
paper_authors.name: Sujian Li
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-05T10:30:37.203Z
paper_authors._id: 66021bd9d3d4aea81b12ee06
paper_authors.avatarUrl: /avatars/13e7ffe1ea0d64ef447a87a874923531.svg
paper_authors.fullname: Sujian Li
paper_authors.type: user
paper_authors.user: sujianli
paper_title: MPO: Boosting LLM Agents with Meta Plan Optimization
paper_summary: 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 Meta Plan Optimization (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.
paper_ai_summary: MPO framework enhances LLM-based agent planning with explicit high-level guidance, improving task completion and generalization through meta plans.
paper_ai_keywords: large language models LLM-based agents Meta Plan Optimization MPO planning hallucinations meta plans feedback task execution generalization