FLEX: Continuous Agent Evolution via Forward Learning from Experience
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arXiv
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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866911305938501632 |
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| author | Cai, Zhicheng Guo, Xinyuan Pei, Yu Feng, Jiangtao Su, Jinsong Chen, Jiangjie Zhang, Ya-Qin Ma, Wei-Ying Wang, Mingxuan Zhou, Hao |
| author_facet | Cai, Zhicheng Guo, Xinyuan Pei, Yu Feng, Jiangtao Su, Jinsong Chen, Jiangjie Zhang, Ya-Qin Ma, Wei-Ying Wang, Mingxuan Zhou, Hao |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_06449 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | FLEX: Continuous Agent Evolution via Forward Learning from Experience Cai, Zhicheng Guo, Xinyuan Pei, Yu Feng, Jiangtao Su, Jinsong Chen, Jiangjie Zhang, Ya-Qin Ma, Wei-Ying Wang, Mingxuan Zhou, Hao Machine Learning Artificial Intelligence 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. |
| title | FLEX: Continuous Agent Evolution via Forward Learning from Experience |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2511.06449 |