What is the impact of ExpertFlow's token scheduling policy on downstream task accuracy (e.g., VQA v2, MMBench)

Abstract

Abstract: Sparse Mixture-of-Experts (MoE) models can outperform dense large language models at similar computation by activating only a small set of experts per token. However, stacking many expert modules introduces substantial parameter memory, which makes MoE models difficult to deploy in memory-constrained environments such as single-GPU devices. Offloading alleviates this issue by storing inactive experts in CPU memory and loading them on demand, but existing methods remain limited: static caches disregard input-dependent routing, and methods that train separate models to predict expert usage ahead

Research Question

What is the impact of ExpertFlow's token scheduling policy on downstream task accuracy (e.g., VQA v2, MMBench) across varying expert cache sizes and memory budgets for MoE-VLMs?

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