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MetaEvo addresses the limitation of static LLM agents by introducing a two-stage framework that prioritizes enhancing the model's learning mechanisms over simple memory storage. By applying preference-based optimization to improve principle abstraction and enabling modular reuse of these principles, the approach facilitates continual reasoning improvement across iterations. This shift from passive execution to active meta-optimization offers a viable path for overcoming early performance plateaus in experience-driven agent systems.
MetaEvo: A Meta-Optimization Framework for Experience-Driven Agent Evolution
arXiv:2606.07603v1 Announce Type: new Abstract: Large language models (LLMs) exhibit strong reasoning capabilities, yet most LLM-based agents are statically deployed and unable to improve through task interactions. Existing experience-driven methods often rely on memory or heuristics without enhancing the model's ability to learn, treating it as a passive executor and leading to early performance plateaus and limited long-term improvement. To address this issue, we propose MetaEvo, a two-stage framework for continual agent evolution that focuses on improving how the model learns from tasks experience, rather than solely on what it stores. MetaEvo first applies preference-based optimization to enhance the model's ability of principle abstraction, then enables the accumulation and reuse of these principles within a modular agent architecture. Experimental results on diverse reasoning benchmarks demonstrate that MetaEvo consistently outperforms strong baselines, maintains reliable improvement across iterations. These findings validate the effectiveness of meta-optimization in enabling agents to learn from experience and continually enhance their reasoning capabilities.
Abstract:Large language models (LLMs) exhibit strong reasoning capabilities, yet most LLM-based agents are statically deployed and unable to improve through task interactions. Existing experience-driven methods often rely on memory or heuristics without enhancing the model's ability to learn, treating it as a passive executor and leading to early performance plateaus and limited long-term improvement. To address this issue, we propose MetaEvo, a two-stage framework for continual agent evolution that focuses on improving how the model learns from tasks experience, rather than solely on what it stores. MetaEvo first applies preference-based optimization to enhance the model's ability of principle abstraction, then enables the accumulation and reuse of these principles within a modular agent architecture. Experimental results on diverse reasoning benchmarks demonstrate that MetaEvo consistently outperforms strong baselines, maintains reliable improvement across iterations. These findings validate the effectiveness of meta-optimization in enabling agents to learn from experience and continually enhance their reasoning capabilities.
Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
arXiv:2606.07603 [cs.LG]
(or arXiv:2606.07603v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2606.07603 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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