Dynamic Expert Capacity Allocation and Its Impact on HumanEval Accuracy in MoE Models

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

Research Question

How does dynamic expert capacity allocation affect pass@1 accuracy on HumanEval compared to fixed-capacity MoE models with equivalent parameter counts?

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