FLEX：基于经验前向学习的智能体持续进化机制

摘要

基于大语言模型（LLM）的自主智能体虽已实现推理与问题解决能力的革命性突破，但其在训练后即处于静态，无法像智能生命那样通过部署过程中的经验持续成长。我们提出经验驱动的前向学习框架（FLEX），这一无需梯度的学习范式使LLM智能体能够借助累积经验实现持续进化。具体而言，FLEX通过与环境交互过程中对成功与失败的持续反思，构建起结构化的经验库，从而实现可扩展、可传承的智能体进化。该框架在数学推理、化学逆合成及蛋白质适应性预测任务中取得显著提升（在AIME25上提升达23%，USPTO50k上提升10%，ProteinGym上提升14%）。我们进一步揭示了经验增长的显著缩放定律以及跨智能体的经验传承现象，标志着可扩展、可传承的持续智能体进化迈出关键一步。项目页面：https://flex-gensi-thuair.github.io。

English

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：基于经验前向学习的智能体持续进化机制

FLEX: Continuous Agent Evolution via Forward Learning from Experience

摘要

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