Nested Sparsity for Scalable Pretraining and Continual Learning

Overview

arXiv:2610.31818v1 Announce Type: new Abstract: Mixture-of-experts (MoE) transformers, the standard backbone for modern large-scale deep learning, employ conditional computation: only a sparse subset of model weights is used in each forward pass. In MoE transformers, this sparsity arises in the MoE operator, which generalizes the multi-layer perceptron in the classical transformer. In this work, we explore an alternative mechanism for conditional computation that analogously generalizes the atte

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