What is the impact of sparsity ratio in token-level routing on the trade-off between inference latency and mul

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 impact of sparsity ratio in token-level routing on the trade-off between inference latency and multimodal reasoning performance in MoE VLMs on SEED-Bench and MMBench benchmarks?

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