P4IR applies supervised fine-tuning followed by GRPO reinforcement learning to reduce tree edit distance by up to 23.8% and Levenshtein distance by up to 38.6% versus SFT baselines while outperforming several frontier LLMs on code structure and semantics for automated building code compliance.
Factually Consistent Summarization via Reinforcement Learning with Textual Entailment Feedback
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A comprehensive survey of knowledge distillation for LLMs structured around algorithms, skill enhancement, and vertical applications, highlighting data augmentation as a key enabler.
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Reinforcement learning to improve large language model-based automated code compliance systems
P4IR applies supervised fine-tuning followed by GRPO reinforcement learning to reduce tree edit distance by up to 23.8% and Levenshtein distance by up to 38.6% versus SFT baselines while outperforming several frontier LLMs on code structure and semantics for automated building code compliance.
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A Survey on Knowledge Distillation of Large Language Models
A comprehensive survey of knowledge distillation for LLMs structured around algorithms, skill enhancement, and vertical applications, highlighting data augmentation as a key enabler.