2026
RCPU: Rotation-Constrained Error Compensation for Structured Pruning of a Large Language Model
ICLR 2026poster
In this paper, we propose a rotation-constrained compensation method to address the errors introduced by structured pruning of large language models (LLMs). LLMs are trained on massive datasets and accumulate rich semantic knowledge in their representation space. In contrast, pruning is typically…