Reinforcement learning to improve large language model-based automated code compliance systems

• URL: http://arxiv.org/abs/2606.22402v1
• Date: Sun, 21 Jun 2026 09:17:02 GMT
• Title: Reinforcement learning to improve large language model-based automated code compliance systems
• Abstract summary: This paper introduces P4IR, a two-stage framework that uses supervised fine-tuning to instill domain knowledge in an LLM.<n>The framework achieves reductions of up to 23.8% and 38.6% in tree edit distance and token-level Levenshtein distance respectively.
• Score: 1.0760732143259102
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large language model (LLM)-based approaches for automated code compliance (ACC) of building regulations are prone to generating incorrect and hallucinated computer-processable rules. This paper introduces P4IR, a two-stage framework that uses supervised fine-tuning (SFT) to instill domain knowledge in an LLM, followed by Group Relative Policy Optimization (GRPO) to improve the accuracy of the generated intermediate representations in the form of high-level code skeletons. The framework achieved reductions of up to 23.8% and 38.6% in tree edit distance and token-level Levenshtein distance respectively, relative to the SFT baselines. Comparative analysis demonstrates that this approach in a zero-shot setting outperforms leading LLMs in both code structure and semantics, specifically Claude Opus and Sonnet 4.5, GPT-5.2, Qwen-3-Max, and GLM-4.7, evaluated via few-shot prompting. Additionally, the GRPO stage produced a small yet statistically significant reduction in false positives. By combining SFT with GRPO to optimize directly for domain-specific objectives, this approach offers a path toward more accurate and reliable LLM-based ACC systems.

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