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

TL;DR

This survey reviews how reinforcement learning techniques are applied to improve code generation and optimization in various domains, including compiler efficiency and resource management.

Contribution

It provides a comprehensive overview of RL applications in code optimization, highlighting recent advancements and future research directions.

Findings

RL enhances compiler optimization efficiency.

RL improves resource allocation in code generation.

Frameworks integrating RL boost code development capabilities.

Abstract

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.