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Forget PEFT and KD, reprogramming distillation offers a surprisingly effective and robust way to adapt large medical foundation models to diverse downstream tasks.
Injecting rare disease knowledge into data synthesis and using self-supervised RL on pseudo-labels dramatically improves medical reasoning in LLMs, outperforming existing methods by up to 5.93% on rare disease tasks.
