Google patents model tweaks that skip steps to speed up AI

1. A computer system with improved inference efficiency, the computer system comprising: one or more processors; and one or more non-transitory computer-readable media that collectively store: a machine-learned conditional adapter model configured to receive and process a model input to generate a model output, wherein the machine-learned conditional adapter model comprises one or more layers, and wherein at least a first layer of the one or more layers comprises: a conditional processing branch; an adapter processing branch in parallel to the conditional processing branch; and a router sublayer comprising a plurality of learned parameter values, wherein the router sublayer is configured to: apply the plurality of learned parameter values to a plurality of input tokens of the first layer to generate a plurality of routing values respectively for the plurality of input tokens; and select a subset of the plurality of input tokens for processing with the conditional processing branch based on the plurality of routing values; and instructions that, when executed by the one or more processors, cause the computer system to use the machine-learned conditional adapter model to process the model input to generate the model output.