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cs.LG2026
Beyond Activation Alignment:The Alignment-Diversity Tradeoff in Task-Aware LLM Quantization
Fei Wang, Chao Xue, Taoran Liu +3
Mixed-precision quantization (MPQ) has become a key technique for deploying large language models under stringent memory and compute constraints. We first identify a phenomenon tha…
cs.LG2026
Universally Empowering Zeroth-Order Optimization via Adaptive Layer-wise Sampling
Fei Wang, Li Shen, Liang Ding +3
Zeroth-Order optimization presents a promising memory-efficient paradigm for fine-tuning Large Language Models by relying solely on forward passes. However, its practical adoption…
cs.LG2024
Simultaneous Computation and Memory Efficient Zeroth-Order Optimizer for Fine-Tuning Large Language Models
Fei Wang, Li Shen, Liang Ding +3
Fine-tuning is powerful for adapting large language models to downstream tasks, but it often results in huge memory usages. A promising approach to mitigate this is using Zeroth-Or…