Preference data collapses to the mean without segmentation

models better. And I think one of the tricky things here is often times in the past you had kind of this uh collection of preference data that would collapse again to the mean because you would collect it from a bunch of different people without necessarily understanding who they are, what they like, why they like it, when they like it. And if you don't break up that problem accordingly, you then end up again with preferences that kind of don't agree with each other. Because that naturally happens in the world, right? I bet that some of you might like one style better than another. And that doesn't mean either of those things are wrong or it doesn't mean that the best answer is the average of what two people might like. It means that we need to fundamentally understand that the world is multi-preference and how do we do that matching accordingly. So this almost like understanding of how do we create like a preference vector let's say for someone and attach that to even something like preference data can help us to train in a way that allows for that more pluralism of preferences intentionally instead of that data being turned into something that's noisy.