From Memorization to Reasoning in the Spectrum of Loss Curvature

arXiv — cs.CLMonday, November 3, 2025 at 5:00:00 AM
A recent study sheds light on how memorization is represented in transformer models, revealing that it can be disentangled in the weights of both language models and vision transformers. This finding is significant as it enhances our understanding of the loss landscape curvature, indicating that memorized training points exhibit sharper curvature compared to non-memorized ones. This insight could lead to improved model training techniques and better performance in AI applications.
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