FLEX: Continuous Agent Evolution via Forward Learning from Experience
On the map: LLM reasoning → Reasoning for agents, planning and code
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Abstract (arXiv)
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.
Key facts. Cited by 51 papers in the map (data through 7 Oct 2026). Placed in Reasoning for agents, planning and code, one of 17 directions in LLM reasoning. Builds on 80 of its 115 references that are in the map. First posted on arXiv 9 Nov 2025.
Builds on
80 of 115 references.
Closest ancestors · 4
Same direction, newest first
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- 2023CREATOR: Tool Creation for Disentangling Abstract and Concrete Reasoning of Large Language Models23 May
Other references · 76
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