Efficient training techniques for generative model based response systems

Abstract

Some implementations relate to receiving input data; generating, using a low-rank representation of a machine-learned generative model, a generative output from the input data; determining, based on a machine-learned reward model, a corresponding reward from the generative output, and updating, based on the corresponding reward, one or more parameters of the low-rank representation of the machine-learned model. Further, some additional or alternative implementations relate to receiving input data associated with a client device; generating, using a general purpose agent, responsive content to the input data, wherein the general purpose agent is configured based on a machine-learned generative model and a low-rank representation of the machine-learned generative model; and causing the client device to render the responsive content.

Dependencies are inferred from the claim text.