This was an idea that sounded somewhat silly until it was shown it worked. The idea is that you encourage through training a bunch of “experts” to diversify and “get good” at different things. These experts are say 1/10 to 1/100 of your model size if it were a dense model. So you pack them all up into one model, and you add a layer or a few layers that have the job of picking which small expert model is best for your given token input, route it to that small expert, and voila — you’ve turned a full run through the dense parameters into a quick run through a router and then a 1/10 as long run through a little model. How do you get a “picker” that’s good? Well, it’s differentiable, and all we have in ML is a hammer — so, just do gradient descent on the decider while training the experts!
This generally works well, although there are lots and lots of caveats. But it is (mostly) a free lunch, or at least a discounted lunch. I haven’t seen a ton of analysis on what different experts end up doing, but I believe it’s widely agreed that they tend to specialize. Those specializations (especially if you have a small number of experts) may be pretty esoteric / dense in their own right.
Anthropic’s interpretability team would be the ones to give a really high quality look, but I don’t think any of Anthropic’s current models are MoE.
Anecdotally, I feel MoE models sometimes exhibit slightly less “deep” thinking, but I might just be biased towards more weights. And they are undeniably faster and better per second of clock time, GPU time, memory or bandwidth usage — on all of these - than dense models with similar training regimes.