LoRA’s Rank Excuse Has a Gradient Problem
TL;DR for operators LoRA is usually sold as a rank-and-cost compromise: train a small low-rank adapter instead of updating the whole model, accept some performance gap, and enjoy the budget meeting. The paper behind SDS-LoRA argues that this explanation is incomplete. The gap is not only because the adapter is low-rank. It is also because standard LoRA can distort the training signal that flows into that adapter.1 ...