TailLoR：在参数高效持续学习中保护主成分
TailLoR: Protecting Principal Components in Parameter-Efficient Continual Learning
AI总结：
提出TailLoR方法，利用预训练权重的奇异基作为固定参考系，对奇异值矩阵进行低秩更新，并通过软谱惩罚抑制与主导奇异方向对齐的更新，从而减少干扰并实现细粒度适应。
摘要：
基于谱分解的参数高效微调方法推动了持续学习的进展。本文介绍TailLoR，该方法利用预训练权重的奇异基U和V作为固定参考系，学习应用于奇异值矩阵的低秩更新。软谱惩罚抑制与主导奇异方向对齐的更新，减少干扰，同时将细粒度适应引导到高度灵活的长尾谱坐标中。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
