actually go about that? Cuz that seems like a pretty ludicrous task. >> Right. So letting the model to do cool stuff like replicating papers, optimizing kernel frameworks and stuff like that is always exciting for us researchers because it's like very related to our job. But training it can be very tricky because it's very long horizon and like the task itself would require GPUs. It has hardware constraints. So it's very interesting to train tasks like that and I would say the key there is still the environments and the data and how you formulate the problem, how you formulate the rewards, how you formulate the environment and how you change the reinforcement learning algorithm a little bit so that it's trained more efficiently so that you can see cool things emerging through the the iterations of RL runs.