GLM-4.7

P4IR framework uses SFT + GRPO to improve LLM-based automated building code compliance

Researchers introduce P4IR, a two-stage framework combining supervised fine-tuning (SFT) and Group Relative Policy Optimization (GRPO) to improve LLM accuracy in automated code compliance (ACC) for building regulations. The approach reduces tree edit distance and token-level Levenshtein distance by up to 23.8% and 38.6% respectively versus SFT baselines, and outperforms Claude Opus/Sonnet 4.5, GPT-5.2, Qwen-3-Max, and GLM-4.7 in zero-shot settings. The work targets a narrow but practically important domain where LLM hallucinations carry real regulatory consequences.