Overfittng
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Fast convergence isn’t always a good sign.
this usually means the dataset is too small or too repetitive.Large pretrained models can memorize tiny datasets extremely fast. Once memorized, generalization collapses.
Reduce epochs, add regularization, or increase dataset diversity. Parameter-efficient tuning methods help limit overfitting.
Common mistakes:
Training full model on small data
Reusing near-duplicate samples
Ignoring validation signals