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Annotator disagreement on tasks like natural language inference (NLI) reflects genuine linguistic ambiguity, yet most fine-tuning recipes treat every example as equally learnable. We ask whether this external signal of ambiguity predicts per-example learning dynamics under LoRA, the most widely used parameter-efficient fine-tuning method, and find that it does. Correlating annotation entropy (from ChaosNLI’s 100 labels per example) with per-example area under the loss curve (AULC) on SNLI and MNLI, the correlation is positive in all 25conditions tested (Spearman 𝜌= 0.06-0.43), with decoder-only models showing stronger correlations than encoders at matched LoRA rank. More strikingly, under LoRA contested examples exhibit un-learning: their gold-label loss increases during training, a pattern that is largely absent under full fine-tuning and IA3 in the encoder setting where matched comparisons are available, and that we also observe under LoRA on two decoder-only models. The effect survives partial-correlation controls and replicates across seeds and datasets. A preliminary noise-injection experiment is consistent with these findings.