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Forget static layer selection – GRASS dynamically adapts which layers to fine-tune based on gradient norms, unlocking significant memory savings and accuracy gains.
Neural synchronization, long hypothesized to support flexible coordination in biological brains, can now be harnessed to improve the learning efficiency of Vision Transformers.
Squeeze 34% more decode speed out of your MoE model without sacrificing accuracy by intelligently budgeting expert activations.
