data data subset that we have in uh pre-training recipes. Um so I mentioned synthetic data, but what is actually uh like how is synthetic data used? There's a lot of uh talk around synthetic data that, you know, blindly tossing it into a model can cause the model to collapse and uh and performance to tank, but there's been a lot of work and uh even at like a large scale, you know, example of this uh turning out really well. Um so in our own in our in our own uh model Trinity Lodge, we uh had a large amount of uh web uh web scale synthetic data um mostly through rephrasing, where you take a seed data item and you sort of upsample it in the mix by uh generating synthetic rephrases of the same information. So, the model sees the same information in like multiple ways. Um the bottom two uh screenshots are from Kimikay2, um an even larger scale model that uh broadly used this um across the whole pre-training data set. Um the top uh top right um screenshot is from a paper that uh resulted in the data sets Swallow Code and Swallow Math, which are early examples of this. But, the trend seems to be that um synthetic data not only allows you to get more and more tokens, uh but also, you know, clean up tokens, get higher quality tokens, and have um tokens that are shaped more like instruct or agentic tasks all the way back in pre-training and uh allowing the model to like learn those task representations from the very beginning.