Keri Briske

Sometimes we have distribution limitations, but then we try and recreate or do some sort of synthetic derivative.

that's differential enough to be able to release it.

So what we've learned from the data set that we've acquired or paid for.

And so we're actually kind of paying for these data sets and then also giving them back out to the community.

The other thing is that the thing about reasoning and reinforcement learning

is that you're not necessarily limited by data.

You need data, but you can now do synthetic data generation.

And you can create synthetic environments.

And so if you think of an environment like a gym, where the more you exercise your model, the better it gets, and you give it different variations.

So when it sees a problem in the real world, then it can know, hey, this is similar to something I've done before, and I can

go solve this problem.

So if you think of all of the synthetic data, because you're just compute limited at that point.

So when we have just GPUs that are doing synthetic data generation, and then we package that up and put that out.

And so that's a lot of compute savings for developers in the community, because now we're not only giving away a synthetic data generation, we're also giving out these gym environments.

We're also giving out algorithms for the reinforcement learning.

And the research is another example.

So we published a paper earlier this year on what's called a hybrid transformer.

So it was both a LAMBA and a transformer put together.

which is really efficient because it reduces some of the attention memory size of the attention layers.

And so you're able to reduce the size of them all, have a highly efficient model for inference.