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The field of natural language processing (NLP) has made significant stridesin recent years, particularly in the development of large-scale vision-languagemodels (VLMs). These models aim to bridge the gap between text and visualinformation, enabling a more comprehensive understanding of multimedia data.However, as these models become larger and more complex, they also become morechallenging to train and deploy. One approach to addressing this challenge isthe use of sparsely-gated mixture-of-experts (MoE) techniques, which divide themodel into smaller, specialized sub-models that can jointly solve a task. Inthis paper, we explore the effectiveness of MoE in scaling vision-languagemodels, demonstrating its potential to achieve state-of-the-art performance ona range of benchmarks over dense models of equivalent computational cost. Ourresearch offers valuable insights into stabilizing the training of MoE models,understanding the impact of MoE on model interpretability, and balancing thetrade-offs between compute performance when scaling VLMs. We hope our work willinspire further research into the use of MoE for scaling large-scalevision-language models and other multimodal machine learning applications.

Scaling Vision-Language Models with Sparse Mixture of Experts