There’s an important shift happening in the world of large language models (LLMs)—one that could redefine how we interact with artificial intelligence. And the answer, previewed today by OpenAi, might just lie in a new approach to how these models learn and adapt.

Let me explain.

Traditionally, LLMs follow a linear training process. These models spend most of their life in pre-training, which involves digesting a massive amount of text data. This phase is computationally intensive, taking up the bulk of resources and time. After this comes post-training or fine-tuning, where the model is adjusted to specialize in certain tasks. Finally, you get to inference, where the model generates responses based on what it’s learned.

Here’s the catch: in most LLMs, the inference stage is pretty static. Once trained, the model doesn’t change much during use. It just........

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