For training ResNet-50 on ImageNet, our 5-bit block Householder quantizer achieves only 0.5% validation accuracy loss relative to QA T, comparable to the existing INT8 baseline.

This heavily restricts the degree of control over generated objects as well as the possible applications of such models.

In this paper, we investigate integrating this inductive bias of sparse interactions into thelatent dynamics ofworld models trained from pixels.