用强化学习提升代码生成与优化效率，系统梳理关键技术路径。
Enhancing Code LLMs with Reinforcement Learning in Code Generation: A Survey
- 将强化学习用于编译器优化，提升代码执行效率
- 通过RL优化寄存器分配与系统资源利用，降低开销
- 适合关注代码生成、编译优化的开发者与研究者
随着大语言模型（LLM）的快速发展，强化学习（RL）已成为代码生成与优化中的关键技术。本文系统综述了强化学习在代码优化与生成中的应用，重点探讨其在编译器优化、资源分配及开发框架工具中的作用。首先分析编译器优化过程，展示RL如何提升执行效率与资源利用率；随后讨论注册分配与系统优化中RL的应用；最后探索集成强化学习的代码生成框架与工具的发展趋势。本综述旨在为关注强化学习驱动代码生成与优化的研究人员与实践者提供全面参考。
原文摘要 · Abstract (English)
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.
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