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Traditionally, deep learning progress relied on scaling up data and parameters. However, recent findings suggest that "overtraining" on massive token budgets can actually make models harder to fine-tune for specific downstream tasks [16]. Fine-tuning is now viewed as an essential "orthogonal scaling path" that allows models to surpass the performance ceilings of their pre-training data [22]. Key Techniques in Modern Fine-Tuning Test-Time Adaptation (TTA):
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