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cs.LG2026
ZO-Act: Efficient Zeroth-Order Fine-Tuning via One-Shot Activation-Informed Low-Rank Subspaces
Xun Dong, Yibo Xu, Naigang Wang +3
Zeroth-order (ZO) optimization enables fine-tuning large language models when backpropagation is unavailable or memory-prohibitive, but existing methods often perturb full model we…
cs.LG2025
DiaBlo: Diagonal Blocks Are Sufficient For Finetuning
Selcuk Gurses, Aozhong Zhang, Yanxia Deng +5
Fine-tuning is a critical step for adapting large language models (LLMs) to domain-specific downstream tasks. To mitigate the substantial computational and memory costs of full-mod…
cs.LG2025
CLoQ: Enhancing Fine-Tuning of Quantized LLMs via Calibrated LoRA Initialization
Yanxia Deng, Aozhong Zhang, Selcuk Gurses +3
Fine-tuning large language models (LLMs) using low-rank adaptation (LoRA) has become a highly efficient approach for downstream tasks, particularly in scenarios with limited comput…