So, reasoning models act to to repeat the uh uh You're asking like uh if there's any models that are quite a bit different training. The base the building blocks are very similar. Like you can train the same exact model, you can post train it which is how like usually reasoning is is uh is being taught to this model. You have a good base instruct model and then you post train it to be a reasoning model. This is very data set driven. So, you need very very high quality data. And you're going to use a loss that's like good enough to be able to uh to learn this data in a in a very good sense. Uh the complication of reasoning models is finding this good chain of thought data. Uh that's why like OpenAI has like all these labelers that are like PhD students that they write down the reasonings of like how to how they solve problems. Uh because this data needs to be very high quality cuz it's it teaches the model how to think. So, you can't just go on like Reddit and just get random posts. You're not going to learn how to think this way for sure. Uh you need like some very high value good quality data to teach this reasoning process. But in the end, reasoning is is essentially just adding to the context of the model like this to to the attention essentially. Uh this like logic that then the model can when it generates a response, it can go back and attend to those reasoning tokens and get a better response out. So, it could be like describing the model a bit better then it goes back, sees those tokens that it described, and say, "Oh, actually yeah, I already figured this out. I'm going to write it down now." Okay.