TailLoR applies low-rank updates to the singular value matrix of pre-trained weights while using a soft spectral penalty to protect dominant singular directions during continual learning.
arXiv preprint arXiv:2602.21919 , year=
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TailLoR: Protecting Principal Components in Parameter-Efficient Continual Learning
TailLoR applies low-rank updates to the singular value matrix of pre-trained weights while using a soft spectral penalty to protect dominant singular directions during continual learning.