2 papers
cs.LG2026
LoopQuant: Accurate and Efficient Post-Training Quantization for Looped Transformers
Zhixiong Zhao, Zukang Xu, Xing Hu +3
Looped Language Models (LoopLMs) improve parameter efficiency by recursively reusing shared Transformer modules, yet their repeated computation over evolving recurrent states poses…
cs.LG2026
All for 1-Bit: Towards Genuine 1-Bit Post-Training Quantization for LLMs
Zhixiong Zhao, Zukang Xu, Guangyu Sun +2
Large language models (LLMs) have achieved remarkable progress, yet their massive storage and memory-bandwidth demands still hinder efficient deployment. Weight binarization is a p…