📄 论文总结
FLEX：基于经验前向学习的智能体持续进化 / FLEX: Continuous Agent Evolution via Forward Learning from Experience
1️⃣ 一句话总结
这篇论文提出了一种名为FLEX的无梯度学习方法，让基于大语言模型的智能体能够像生物一样通过积累经验持续自我进化，在数学推理、化学合成和蛋白质预测等任务上取得了显著提升。
🤖 系统
Abstract - 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: this https URL.