um knowledge or quality onto the base model itself. Now, in in like this realm of of how language models used to be, pre-training um and the base model kind of defined how good you were able to get a model. Um it was like the bulk of the compute uh budget and it was um, the the like core of the training process. Um, however, uh, this kind of changed a lot last year when um, OpenAI I guess 2024 actually. OpenAI released 01, um, pioneering reasoning models and DeepSeek uh, also released R1 in January 2025, um, allowing the whole world to know how to build these types of language models. And now we have this new uh, new um, use for reinforcement learning, which is no longer a cherry on top, but it can dramatically improve the performance of of the model on various different tasks. Um, the the famous graphs from 01 there talking about uh, AIME performance, um, competitive math contest. Um, and then even later in the year we we saw Cloud Code um, coming to being as a way for developers to easily um, kind of use uh, language models in a in a terminal to build out applications as models got stronger and stronger on things like function calling. Um, and then people realized you could uh, you know, RL this end-to-end. Um, and now models could learn how to interact with software environments and build software and uh, perform really useful work.