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

arXiv:2605.29317v2 Announce Type: replace 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 diagonal Fisher score (under 1% of training cost) and trains the LoRA...

arXiv CS 9d ago

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