FoRA: Fisher-orthogonal Rank Adaptation for Parameter-Efficient Fine-Tuning 事件

FoRA: Fisher-orthogonal Rank Adaptation for Parameter-Efficient Fine-Tuning arXiv:2605.29317v1 Announce Type: new Abstract: Parameter-efficient fine-tuning(PEFT) has largely focused on LoRA and its accuracy-oriented variants, leaving the original goal of reducing trainable parameters has receivedcomparatively little attention. We introduce FoRA, which revisits this goal by reducing the number of adapted layers rather than adapter rank. FoRA selects task-informative layers via a single-pass diag