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The number of discrete class-separability jumps observed during ResNet finetuning is examined empirically as a predictor of final test accuracy and is presented as an in-distribution training-quality probe among a family of candidate probes.
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.
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