FLEX: Continuous Agent Evolution via Forward Learning from Experience
Autonomous agents driven by Large Language Models (LLMs) have revolutionized
reasoning and problem-solving but remain static after training, unable to grow
with experience as intelligent beings do during deployment. We introduce
Forward Learning with EXperience (FLEX), a gradient-free learning paradigm that
enables LLM agents to continuously evolve through accumulated experience.
Specifically, FLEX cultivates scalable and inheritable evolution by
constructing a structured experience library through continual reflection on
successes and failures during interaction with the environment. FLEX delivers
substantial improvements on mathematical reasoning, chemical retrosynthesis,
and protein fitness prediction (up to 23% on AIME25, 10% on USPTO50k, and 14%
on ProteinGym). We further identify a clear scaling law of experiential growth
and the phenomenon of experience inheritance across agents, marking a step
toward scalable and inheritable continuous agent evolution. Project Page:
https://flex-gensi-thuair.github.io.
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Zhicheng Cai
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Xinyuan Guo
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2025-11-11T19:44:16.291Z
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Xinyuan Guo
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Yu Pei
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JiangTao Feng
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Jiangjie Chen
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Ya-Qin Zhang
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Wei-Ying Ma
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Mingxuan Wang
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Hao Zhou
FLEX: Continuous Agent Evolution via Forward Learning from Experience
Autonomous agents driven by Large Language Models (LLMs) have revolutionized
reasoning and problem-solving but remain static after training, unable to grow
with experience as intelligent beings do during deployment. We introduce
Forward Learning with EXperience (FLEX), a gradient-free learning paradigm that
enables LLM agents to continuously evolve through accumulated experience.
Specifically, FLEX cultivates scalable and inheritable evolution by
constructing a structured experience library through continual reflection on
successes and failures during interaction with the environment. FLEX delivers
substantial improvements on mathematical reasoning, chemical retrosynthesis,
and protein fitness prediction (up to 23% on AIME25, 10% on USPTO50k, and 14%
on ProteinGym). We further identify a clear scaling law of experiential growth
and the phenomenon of experience inheritance across agents, marking a step
toward scalable and inheritable continuous agent evolution. Project Page:
https://flex-gensi-thuair.github.io.
FLEX, a gradient-free learning paradigm, enables Large Language Model agents to continuously evolve through experience, improving performance in tasks like mathematical reasoning, chemical retrosynthesis, and protein fitness prediction.
Large Language Models Forward Learning gradient-free learning experience library continual reflection mathematical reasoning chemical retrosynthesis protein fitness prediction AIME25 USPTO50k ProteinGym experiential growth experience inheritance