Paper Detail
Marius Dragoi, Ioana Pintilie, Alexandra Dragomir, Antonio Barbalau, Florin Brad
Parameter-efficient finetuning methods based on spectral decomposition have enabled progress in Continual Learning. In this paper we introduce TailLoR, which utilizes the singular bases U and V of the pre-trained weights as a fixed reference frame to learn a low-rank update applied to the singular value matrix. A soft spectral penalty discourages updates aligned with dominant singular directions, reducing interference while routing fine-grained adaptation into the highly flexible, long-tail spectral coordinates.
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@article{dragoi2026taillor,
title = {TailLoR: Protecting Principal Components in Parameter-Efficient Continual Learning},
author = {Marius Dragoi and Ioana Pintilie and Alexandra Dragomir and Antonio Barbalau and Florin Brad},
year = {2026},
abstract = {Parameter-efficient finetuning methods based on spectral decomposition have enabled progress in Continual Learning. In this paper we introduce TailLoR, which utilizes the singular bases U and V of the pre-trained weights as a fixed reference frame to learn a low-rank update applied to the singular value matrix. A soft spectral penalty discourages updates aligned with dominant singular directions, reducing interference while routing fine-grained adaptation into the highly flexible, long-tail spec},
url = {https://arxiv.org/abs/2606.06494},
keywords = {cs.LG, Singular value decomposition, Computer science, Frame (networking), Decomposition, Mathematical optimization},
eprint = {2606.06494},
archiveprefix = {arXiv},
}
{}