J. Arunan

Tight Sample Complexity for Low-Rank Adaptation: Matching Bounds and Rank Selection

Three results characterize the statistical complexity of LoRA fine-tuning within the well-specified locally quadratic regime, and identify the empirically observed over-parameterization penalty as a property of unregularized empirical risk minimization rather than of the LoRA class itself.