Related papers: FLEX: Continuous Agent Evolution via Forward Learning from Experience

FLEX: Continuous Agent Evolution via Forward Learning from Experience

URL: http://arxiv.org/abs/2511.06449v1
Date: Sun, 09 Nov 2025 16:31:39 GMT
Title: FLEX: Continuous Agent Evolution via Forward Learning from Experience
Authors: Zhicheng Cai, Xinyuan Guo, Yu Pei, JiangTao Feng, Jiangjie Chen, Ya-Qin Zhang, Wei-Ying Ma, Mingxuan Wang, Hao Zhou,
Abstract summary: Autonomous agents driven by Large Language Models (LLMs) have revolutionized reasoning and problem-solving but remain static after training.<n>We introduce Forward Learning with EXperience (FLEX), a gradient-free learning paradigm that enables LLM agents to continuously evolve through accumulated experience.
Score: 54.796367711967754
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: 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.

Related papers

SkillRL: Evolving Agents via Recursive Skill-Augmented Reinforcement Learning [83.98129545309277]
We propose SkillRL, a framework that bridges the gap between raw experience and policy improvement.<n>Our approach introduces an experience-based distillation mechanism to build a hierarchical skill library SkillBank.<n> Experimental results on ALF, WebShop and seven search-augmented tasks demonstrate that SkillRL achieves state-of-the-art performance.
arXiv Detail & Related papers (2026-02-09T03:17:17Z)
Towards AGI A Pragmatic Approach Towards Self Evolving Agent [0.0]
Large Language Model (LLM) based agents are powerful yet fundamentally static after deployment.<n>This work introduces a hierarchical self-evolving multi-agent framework that integrates a Base LLM, an operational SLM agent, a Code-Generation LLM, and a Teacher-LLM.
arXiv Detail & Related papers (2026-01-15T20:43:44Z)
Evolution in Simulation: AI-Agent School with Dual Memory for High-Fidelity Educational Dynamics [10.185612854120627]
Large language models (LLMs) based Agents are increasingly pivotal in simulating and understanding complex human systems and interactions.<n>We propose the AI-Agent School (AAS) system, built around a self-evolving mechanism that leverages agents for simulating complex educational dynamics.
arXiv Detail & Related papers (2025-10-13T11:27:53Z)
Learning on the Job: An Experience-Driven Self-Evolving Agent for Long-Horizon Tasks [42.78572295558531]
Large Language Models have demonstrated remarkable capabilities across diverse domains, yet significant challenges persist when deploying them as AI agents for real-world long-horizon tasks.<n>Existing LLM agents suffer from a critical limitation: they are test-time static and cannot learn from experience, lacking the ability to accumulate knowledge and continuously improve on the job.<n>We propose MUSE, a novel agent framework that introduces an experience-driven, self-evolving system centered around a hierarchical Memory Module.
arXiv Detail & Related papers (2025-10-09T09:40:34Z)
Your Agent May Misevolve: Emergent Risks in Self-evolving LLM Agents [58.69865074060139]
We study the case where an agent's self-evolution deviates in unintended ways, leading to undesirable or even harmful outcomes.<n>Our empirical findings reveal that misevolution is a widespread risk, affecting agents built even on top-tier LLMs.<n>We discuss potential mitigation strategies to inspire further research on building safer and more trustworthy self-evolving agents.
arXiv Detail & Related papers (2025-09-30T14:55:55Z)
A Survey on Self-Evolution of Large Language Models [116.54238664264928]
Large language models (LLMs) have significantly advanced in various fields and intelligent agent applications. To address this issue, self-evolution approaches that enable LLMs to autonomously acquire, refine, and learn from experiences generated by the model itself are rapidly growing.
arXiv Detail & Related papers (2024-04-22T17:43:23Z)
Empowering Large Language Model Agents through Action Learning [85.39581419680755]
Large Language Model (LLM) Agents have recently garnered increasing interest yet they are limited in their ability to learn from trial and error. We argue that the capacity to learn new actions from experience is fundamental to the advancement of learning in LLM agents. We introduce a framework LearnAct with an iterative learning strategy to create and improve actions in the form of Python functions.
arXiv Detail & Related papers (2024-02-24T13:13:04Z)
ExpeL: LLM Agents Are Experiential Learners [57.13685954854463]
We introduce the Experiential Learning (ExpeL) agent to allow learning from agent experiences without requiring parametric updates.<n>Our agent autonomously gathers experiences and extracts knowledge using natural language from a collection of training tasks.<n>At inference, the agent recalls its extracted insights and past experiences to make informed decisions.
arXiv Detail & Related papers (2023-08-20T03:03:34Z)

This list is automatically generated from the titles and abstracts of the papers in this site.