ion tuning to train the MoE models, they outperformed the standard models. This was true for both specific tasks and more general tasks that the models hadn't seen before.

3. Their most powerful model performed better than a leading model, but used only a third of the computational resources. 

So, the bottom line is that this approach could lead to AI systems that are more capable, more adaptable to new tasks, and more efficient in terms of computational resources. This could potentially reduce costs and 