As large language models (LLMs) continue to advance, improving them solely through human supervision is becoming increasingly costly and limited in scalability. As models approach human-level capabilities in certain domains, human feedback may no longer provide sufficiently informative signals for further improvement. At the same time, the growing ability of models to make autonomous decisions and execute complex actions naturally enables abstractions in which components of the model development process can be progressively automated. Together, these challenges and opportunities have driven increasing interest in self-improvement, where models autonomously generate data, evaluate outputs, and iteratively refine their own capabilities. In this paper, we present a system-level perspective on self-improving language models and introduce a unified framework that organizes existing techniques. We conceptualize the self-improvement system as a closed-loop lifecycle, consisting of four tightly coupled processes: data acquisition, data selection, model optimization, and inference refinement, along with an autonomous evaluation layer. Within this framework, the model itself plays a central role in driving each stage: collecting or generating data, selecting informative signals, updating its parameters, and refining outputs, while the autonomous evaluation layer continuously monitors progress and guides the improvement cycle across stages. Following this lifecycle perspective, we systematically review and analyze representative methods for each component from a technical standpoint. We further discuss current limitations and outline our vision for future research toward fully self-improving LLMs.

翻译：随着大规模语言模型（LLM）的持续发展，仅依赖人工监督进行改进的成本日益高昂且扩展性受限。当模型在特定领域接近人类水平能力时，人工反馈可能不再提供足够有效的信号以支撑进一步提升。与此同时，模型自主决策和执行复杂动作的能力不断增强，这自然催生了模型开发流程中各个组件的渐进式自动化抽象。这些挑战与机遇共同推动了学界对自我改进方法的日益关注——模型能够自主生成数据、评估输出并迭代优化自身能力。本文从系统层面提出自我改进语言模型的统一框架，系统梳理现有技术。我们将自我改进系统概念化为闭环生命周期，包含四个紧密耦合的过程：数据获取、数据选择、模型优化与推理优化，以及自主评估层。在该框架中，模型本身在各阶段扮演核心驱动角色：收集或生成数据、选择有效信号、更新参数、优化输出，而自主评估层则持续监控进展并跨阶段引导改进循环。基于该生命周期视角，我们从技术角度系统回顾并分析了各组成部分的代表性方法，进一步讨论了当前局限性，并展望了实现完全自主改进的大规模语言模型的未来研究方向。