What is the scaling behavior of SMoES-based VLMs from 1B to 13B parameters in terms of accuracy-latency Pareto

Abstract

Abstract: Mixture-of-Experts (MoE) architectures enable conditional computation by routing inputs to multiple expert subnetworks and are often motivated as a mechanism for scaling large language models. In this project, we instead study MoE behavior in an image classification setting, focusing on predictive performance, expert utilization, and generalization. We compare dense, SoftMoE, and SparseMoE classifier heads on the CIFAR10 dataset under comparable model capacity. Both MoE variants achieve slightly higher validation accuracy than the dense baseline while maintaining balanced expert utilization th

Research Question

What is the scaling behavior of SMoES-based VLMs from 1B to 13B parameters in terms of accuracy-latency Pareto frontier on multimodal benchmarks compared to dense and hard-routing MoE counterparts?

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