Sure! Basically traditional MoE has several linear layers, and the network learns to route down those paths, based on the training loss (similar to how CNNs learn through max-pooling, which is also non-differentiable). However, MoEs have been shown to specialiaze on tokens, not high-level semantics. This was eloquently explained by Fuzhao Xue, author of OpenMoE, in one of our reading groups: https://www.youtube.com/watch?v=k3QOpJA0A0Q&t=1547s

In contrast, our router sits at a higher level of the stack, sending prompts to different models and providers based on quality on the prompt distribution, speed and cost. Happy to clarify further if helpful!