In a solvable attention model, pre-training followed by rank-one LoRA admits sharp asymptotic predictions for test errors and representation alignment via an effective noise term.
Why LoRA resists label noise: A theoretical framework for noise-robust parameter-efficient fine-tuning, 2026
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High-Dimensional Theory of LoRA Fine-Tuning in a Solvable Attention Model
In a solvable attention model, pre-training followed by rank-one LoRA admits sharp asymptotic predictions for test errors and representation alignment via an effective noise term.