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. Our corresponding GitHub repository is available at https://github.com/AlibabaResearch/DAMO-ConvAI/tree/main/Awesome-Self-Evolution-of-LLM

翻译：大语言模型（LLMs）已在诸多领域与智能体应用中取得显著进展。然而，当前依赖人类或外部模型监督进行学习的LLMs不仅成本高昂，且随着任务复杂性与多样性的增加可能面临性能瓶颈。为解决这一问题，使LLM能够自主获取、精炼并学习模型自身生成经验的自演进方法正快速发展。这种受人类经验学习过程启发的新型训练范式，为LLM向超智能方向扩展提供了潜力。本文系统综述了LLMs中的自演进方法。我们首先提出自演进的概念框架，将其演进过程描述为由经验获取、经验精炼、模型更新与评估四个阶段构成的迭代循环。其次，我们分别对LLMs及基于LLM的智能体的演进目标进行分类，继而总结相关文献，为各模块提供分类体系与研究洞见。最后，我们指出当前面临的挑战并提出未来改进自演进框架的方向，为研究者加速开发自演进LLMs提供关键见解。相关GitHub仓库地址为：https://github.com/AlibabaResearch/DAMO-ConvAI/tree/main/Awesome-Self-Evolution-of-LLM