MPO: Boosting LLM Agents with Meta Plan Optimization
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.
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Weimin Xiong
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2025-03-05T10:11:35.055Z
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xwm
user
xwm
67c7c3d073299239b63f5379
Yifan Song
67c7c3d073299239b63f537a
Qingxiu Dong
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2025-03-05T10:31:12.605Z
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Rosy24
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Rsy24
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Bingchan Zhao
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2025-03-05T10:31:05.438Z
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Bingchan Zhao
user
Adagio
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Feifan Song
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2025-03-05T10:30:59.446Z
6447ca6ca478b20f1755b294
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Feifan Song
user
songff
67c7c3d073299239b63f537d
Xun Wang
67c7c3d073299239b63f537e
Sujian Li
admin_assigned
2025-03-05T10:30:37.203Z
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Sujian Li
user
sujianli
MPO: Boosting LLM Agents with Meta Plan Optimization
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.
MPO framework enhances LLM-based agent planning with explicit high-level guidance, improving task completion and generalization through meta plans.
large language models LLM-based agents Meta Plan Optimization MPO planning hallucinations meta plans feedback task execution generalization