Reinforcement Learning from Human Feedback for Enhanced Code Generation and Debugging Capabilities in LLMs

Abstract

Reinforcement learning from human feedback (RLHF) has emerged as a transformative paradigm for improving the capabilities of large language models (LLMs) in code generation and debugging. This research focuses on the development and optimization of RLHF pipelines to enhance the coding accuracy of LLMs by integrating human feedback mechanisms. Traditional machine learning approaches, while effective in generating syntactically correct code, often fail to address nuanced requirements, logical errors, or adherence to best practices. By incorporating human feedback loops into reinforcement learning frameworks, LLMs can iteratively refine their outputs, achieving higher levels of correctness, efficiency, and language-agnostic applicability.

This paper delineates the architecture of RLHF-based pipelines, emphasizing key components such as reward modeling, policy optimization, and the curation of diverse feedback datasets. A critical aspect of this framework involves designing reward signals that balance syntactic correctness, semantic coherence, and execution reliability, informed by human evaluators’ domain expertise. The integration of domain-specific knowledge within the RLHF paradigm further facilitates the generation of robust, context-aware code, which is instrumental in automating complex programming tasks across diverse environments.

A significant portion of the research focuses on debugging workflows, where RLHF is employed to optimize error identification and correction processes. Traditional LLMs frequently overlook edge cases or fail to resolve multi-layered dependencies in large-scale codebases. Through iterative reinforcement, incorporating human-in-the-loop strategies, these limitations are systematically addressed. The feedback loop is operationalized by combining static analysis tools, runtime feedback, and expert human annotations, creating a synergistic mechanism for fine-tuning LLM behavior. Case studies demonstrate the application of this methodology in resolving intricate bugs across programming languages, highlighting notable improvements in debugging precision and response time.

The effectiveness of RLHF-enhanced LLMs is evaluated through extensive experimentation on benchmark datasets, including real-world programming challenges and competitive coding scenarios. Metrics such as compilation success rates, logical correctness scores, and efficiency of generated code are utilized to quantify performance gains. Comparative analysis with traditional supervised learning models reveals that RLHF not only improves accuracy but also significantly reduces error propagation in iterative workflows. The research also explores generalization capabilities, showcasing the adaptability of RLHF-enhanced LLMs to novel programming languages and paradigms, thus extending their utility in diverse application domains.

This study further addresses scalability and computational efficiency challenges inherent in RLHF pipelines. Techniques such as prioritized replay, efficient feedback sampling, and parallelized training architectures are investigated to mitigate resource constraints, enabling broader adoption in industrial and academic settings. Ethical considerations are also examined, particularly concerning the quality and bias of human feedback, ensuring that RLHF models uphold fairness and inclusivity standards.

The paper concludes by outlining future research directions, including the integration of RLHF with multi-modal learning systems, the development of self-optimizing reward models, and the exploration of hierarchical RL techniques for complex task decomposition. By advancing the state of the art in LLMs for code generation and debugging, RLHF not only enhances the productivity of developers but also paves the way for more intelligent, autonomous coding systems. The insights and methodologies presented in this research aim to catalyze further innovation in leveraging human feedback for reinforcement learning, driving advancements in AI-driven programming tools.