Reinforcement Learning-Based Code Generation and Program Repair with Language Models
Keywords:
Reinforcement learning; code generation; program repair; large language models; software engineering infrastructure; alignment; deployment governanceAbstract
Large language models have transformed automated software engineering by enabling the generation and repair of source code from natural language intent. However, the standard supervised fine-tuning paradigm often fails to capture long-horizon correctness, execution semantics, and maintainability requirements that real-world software systems demand. Reinforcement learning offers a complementary mechanism for aligning code-producing models with execution feedback, test outcomes, and human preferences. This paper presents a system-level analysis of reinforcement learning-based code generation and program repair. It examines structural trade-offs across model architectures, training workflows, reward design, deployment infrastructure, and governance. The discussion emphasizes the interaction between policy optimization, semantic feedback, and software engineering constraints such as test coverage, regression risk, fairness, and auditability. Rather than focusing on algorithmic notation, the paper develops a conceptual framework for integrating reinforcement learning into code-oriented language model pipelines. It further explores deployment concerns including inference cost, safety, monitoring, and the organizational processes required for responsible operation. By connecting reinforcement learning with program repair, the analysis shows that learned repair policies can improve code quality but also introduce risks related to reward hacking, distributional drift, and reduced human oversight. The paper concludes with implications for sustainable system design and policy development in AI-assisted software engineering.
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