Sparse Mixture

Sparse Mixture of Experts (MoE) models aim to improve the efficiency and scalability of large language and multimodal models by dividing computation across multiple smaller "expert" networks, each specializing in a subset of the input data. Current research focuses on addressing challenges like representation collapse (where experts become redundant), improving routing mechanisms (which determine which expert processes each input), and developing efficient training and inference strategies, including the use of low-rank adaptations and dynamic expert activation. These advancements hold significant promise for reducing the computational cost of training and deploying very large models, impacting fields ranging from natural language processing to medical image analysis.