To what extent does token scheduling in sparse MoE inference improve robustness to distribution shift in docum

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

To what extent does token scheduling in sparse MoE inference improve robustness to distribution shift in document-based QA (e.g., out-of-distribution chart styles or document layouts) measured by accuracy degradation and throughput consistency compared to dense baselines?

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