Enhancing Code LLMs with Reinforcement Learning in Code Generation: A Survey

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 <PRE_TAG>optimization</POST_TAG>, resource allocation, and the development of <PRE_TAG>frameworks</POST_TAG> and tools. Subsequent sections first delve into the intricate processes of compiler <PRE_TAG>optimization</POST_TAG>, 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 <PRE_TAG>optimization</POST_TAG>. We also explore the burgeoning role of <PRE_TAG>frameworks</POST_TAG> 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.