TailLoR

TailLoR: Spectral-domain continual learning via protected principal components

TailLoR is a new parameter-efficient finetuning method for continual learning that uses the singular value decomposition of pre-trained weights as a fixed reference frame, applying low-rank updates only to the singular value matrix. A soft spectral penalty discourages updates aligned with dominant singular directions, reducing catastrophic interference while routing adaptation into long-tail spectral coordinates. The approach targets the forgetting problem in continual learning through a principled spectral lens.