The recent advancements in the field of Sparse Mixture of Experts (SMoE) models have significantly enhanced their scalability and performance, particularly in complex, compositional tasks. Researchers are increasingly focusing on optimizing the activation of experts within these models to improve generalization and robustness. The integration of momentum-based techniques into SMoE architectures has shown promising results in stabilizing training and enhancing adaptability to new data distributions. Additionally, the exploration of internal mechanisms within Mixture-of-Experts (MoE)-based Large Language Models (LLMs) has led to novel strategies for improving Retrieval-Augmented Generation (RAG) systems. Vision Mixture-of-Experts (ViMoE) models are also gaining traction, with studies highlighting the importance of expert routing and knowledge sharing configurations to achieve optimal performance in image classification tasks. The introduction of CartesianMoE models, which leverage Cartesian product routing for enhanced knowledge sharing among experts, represents a significant leap forward in the scalability and efficiency of large language models. These developments collectively underscore a shift towards more sophisticated and efficient expert activation and routing strategies in SMoE and MoE models, aiming to push the boundaries of model performance and applicability across various domains.

Noteworthy papers include: 'Enhancing Generalization in Sparse Mixture of Experts Models' which demonstrates that increasing expert activation improves performance on complex tasks, and 'MomentumSMoE' which integrates momentum to enhance stability and robustness in SMoE models.