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Recursive depth in masked diffusion models can dramatically enhance parameter efficiency, enabling models to perform as well as much larger counterparts without the added computational burden.
PriFT achieves state-of-the-art performance in supervised fine-tuning by leveraging a stable token reweighting signal from a frozen pretrained model, drastically improving generalization.
