Advances in Reinforcement Learning for Intelligent Program Synthesis

Program synthesis, as an automated program construction method, has become a research focus in both software engineering and artificial intelligence. With the increasing size of the program search space, reinforcement learning (RL) has emerged as a powerful tool capable of navigating vast search spaces to find the optimal code generation paths. This ability to optimize strategies provides an efficient, flexible, and scalable solution for intelligent synthesis, addressing the diverse application scenarios and complex software requirements. This paper first introduces the fundamental concepts and principles of program synthesis, summarizing the contributions of recent review literature in the field. It then discusses the effectiveness of RL-based methods in the program synthesis process from two perspectives: search strategy improvement and execution-guided improvement. Additionally, the paper provides a comparative analysis of these approaches, offering future directions and unresolved issues in applying RL techniques to program synthesis. Ultimately, this work aims to offer insights and guidance for the development of RL-based intelligent program synthesis algorithms in the future.