AI RESEARCH
LOFT: Low-Rank Orthogonal Fine-Tuning via Task-Aware Support Selection
arXiv CS.LG
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ArXi:2605.11872v1 Announce Type: new Orthogonal parameter-efficient fine-tuning (PEFT) adapts pretrained weights through structure-preserving multiplicative transformations, but existing methods often conflate two distinct design choices: the subspace in which adaptation occurs and the transformation applied within that subspace. This paper