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Training-aware Low-Rank Adaptation Initialization (TaRA) is proposed, a method that initializes LoRA such that the gradients induced by the low-rank factors closely approximate the gradient of the corresponding full-rank weight matrix.
This work intervenes on one factor at a time inside a fixed pipeline, holding candidate scoring and voting constant while they vary the search dimension, the subspace that carries the perturbation, and its norm.
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