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These results establish recipe-dependent interactions and identify concrete precision assignments, rather than universal layer-sensitivity rules, rather than universal layer-sensitivity rules for vision-language-action models.
Homogeneous neural networks can be trained with provable generalization guarantees using significantly larger stepsizes than previously thought possible.
