Large language models (LLMs) have significantly advanced in various fields and intelligent agent applications. However, current LLMs that learn from human or external model supervision are costly and may face performance ceilings as task complexity and diversity increase. To address this issue, self-evolution approaches that enable LLM to autonomously acquire, refine, and learn from experiences generated by the model itself are rapidly growing. This new training paradigm inspired by the human experiential learning process offers the potential to scale LLMs towards superintelligence. In this work, we present a comprehensive survey of self-evolution approaches in LLMs. We first propose a conceptual framework for self-evolution and outline the evolving process as iterative cycles composed of four phases: experience acquisition, experience refinement, updating, and evaluation. Second, we categorize the evolution objectives of LLMs and LLM-based agents; then, we summarize the literature and provide taxonomy and insights for each module. Lastly, we pinpoint existing challenges and propose future directions to improve self-evolution frameworks, equipping researchers with critical insights to fast-track the development of self-evolving LLMs.

翻译：大型语言模型（LLM）在多个领域及智能体应用中取得了显著进展。然而，当前依赖人类或外部模型监督学习的LLM成本高昂，且随着任务复杂度与多样性增加可能面临性能瓶颈。为解决此问题，能让LLM自主获取、优化并学习自身生成经验的自我进化方法正迅速发展。这种受人类经验学习过程启发的新型训练范式，为将LLM扩展至超级智能提供了潜力。本文对LLM中的自我进化方法进行了全面综述。首先，我们提出一个自我进化的概念框架，将进化过程概括为由四个阶段组成的迭代循环：经验获取、经验优化、更新与评估。其次，我们对LLM及基于LLM的智能体的进化目标进行了分类；随后，我们总结相关文献，并为每个模块提供分类法及见解。最后，我们指出现有挑战并提出未来方向以改进自我进化框架，为研究人员提供关键见解，加速自我进化型LLM的发展。