Reinforcement learning to improve large language model-based automated code compliance systems
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...
| Authors | Jack Wei Lun Shi, Minghao Dang, Wawan Solihin et al. |
|---|---|
| Primary category | cs.SE |
| Published | 21 June 2026 |
| Last updated at source | 21 June 2026 |
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