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The optimal LoRA rank is not intrinsic to the model but dynamically dictated by the data's geometric properties, enabling principled data selection for efficient fine-tuning.
Homogeneous neural networks can be trained with provable generalization guarantees using significantly larger stepsizes than previously thought possible.