With the rapid evolution of large language models (LLM), reinforcement learning (RL) has emerged as a pivotal technique for code generation and optimization in various domains. This paper presents a systematic survey of the application of RL in code optimization and generation, highlighting its role in enhancing compiler optimization, resource allocation, and the development of frameworks and tools. Subsequent sections first delve into the intricate processes of compiler optimization, where RL algorithms are leveraged to improve efficiency and resource utilization. The discussion then progresses to the function of RL in resource allocation, emphasizing register allocation and system optimization. We also explore the burgeoning role of frameworks and tools in code generation, examining how RL can be integrated to bolster their capabilities. This survey aims to serve as a comprehensive resource for researchers and practitioners interested in harnessing the power of RL to advance code generation and optimization techniques.

翻译：随着大语言模型（LLM）的快速发展，强化学习（RL）已成为各领域代码生成与优化的一项关键技术。本文系统综述了强化学习在代码优化与生成中的应用，重点阐述了其在增强编译器优化、资源分配以及框架与工具开发中的作用。后续章节首先深入探讨编译器优化的复杂过程，其中利用强化学习算法来提升效率与资源利用率。接着，讨论转向强化学习在资源分配中的功能，重点关注寄存器分配与系统优化。我们还探讨了框架与工具在代码生成中日益增长的作用，研究如何集成强化学习以增强其能力。本综述旨在为有兴趣利用强化学习推动代码生成与优化技术发展的研究人员和实践者提供全面的参考资源。