Mixtures of Experts and scaling laws

Mixture of Experts (MoE) is a model architecture used in large language models (LLMs) like Mixtral, DeepSeek-V2, and Qwen2-57B-A14B, which allows for efficient handling of computational resources by utilizing multiple independent "experts" within a model. Each expert is a version of a feedforward block (FFN) and a router determines which experts to activate for processing each token, making the model less demanding during inference. MoE models are known for having a large number of parameters but activate only a subset at a time, enhancing efficiency compared to traditional dense models. Research indicates that increasing the number of experts generally improves model performance, though this benefit diminishes as the base model size grows. Recent studies have explored the concept of expert granularity and optimized routing to further enhance model quality, suggesting potential improvements if the routing processes are refined. This concept also shows promise in lifelong learning scenarios, where adding new experts to an existing model can help adapt to new data without forgetting previous information.

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