Tag
This paper proposes a structured pruning method for LLMs that addresses distribution mismatch, sign-information loss, and outlier influence when adapting unstructured pruning techniques, achieving comparable accuracy with 1.56-1.57x speedup on models like Llama-3-8B and Vicuna-v1.5-13B.
PowerOPD introduces a bounded power transformation to stabilize on-policy distillation for large language models, achieving significant gains in accuracy and sample efficiency while reducing computational cost.