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

2024-12-29

AI Summary

Reinforcement learning (RL) faces significant challenges in practical deployment for end-to-end code generation and optimization—spanning compilation, runtime systems, and toolchains—particularly in compiler optimizations (e.g., register allocation), resource scheduling, and generative tool augmentation. Method: We propose the first unified analytical framework for cross-layer collaborative optimization, integrating mainstream RL algorithms (e.g., PPO, DQN) with program analysis, intermediate representation (IR)-based modeling, and neuro-symbolic joint learning. Our taxonomy systematically classifies over 120 studies. Contribution/Results: We identify three fundamental bottlenecks hindering RL-driven code optimization and synthesize five emerging technical pathways to overcome them. The framework provides a reusable, methodology-oriented guide for aligning industrial-scale code large language models with low-level system optimization, bridging the gap between high-level AI-driven code synthesis and foundational compiler/runtime engineering.

Technology Category

• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Search and Optimization: Learning to Search
• Intelligent Robots: Learning & Optimization for ROB

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

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.

Problem

• Reinforcement Learning
• Code Optimization
• Resource Allocation

Innovation

• Reinforcement Learning
• Code Optimization
• Resource Efficiency