强化学习改进基于大语言模型的建筑规范自动合规系统：P4IR 框架
Reinforcement learning to improve large language model-based automated code compliance systems
AI 导读
一篇 arXiv 论文提出 P4IR 两阶段框架，先用监督微调（SFT）为大语言模型注入建筑规范领域知识，再用 Group Relative Policy Optimization（GRPO）提升生成的代码骨架中间表示的准确性。
正文 · 原文
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 final reductions of up to 21.3% and 41.0% 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, evaluated via few-shot prompting. Additionally, the GRPO stage produced a 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.
| Comments: | 22 pages, 12 figures, 1 table |
| Subjects: | Software Engineering (cs.SE); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG) |
| ACM classes: | I.2.6; I.2.7; J.6 |
| Cite as: | arXiv:2606.22402 [cs.SE] |
| (or arXiv:2606.22402v2 [cs.SE] for this version) | |
| https://doi.org/10.48550/arXiv.2606.22402 arXiv-issued DOI via DataCite |
Submission history
From: Jack Wei Lun Shi [view email]
[v1]
Sun, 21 Jun 2026 09:17:02 UTC (774 KB)
[v2]
Thu, 29 Oct 2026 14:37:41 UTC (1,953 KB)
来源：arXiv cs.AI · arxiv.org