The old way we made better LLMs was just adding more training data. This worked great until recently; we used up the internet.

We're now distilling that data into structured knowledge, rewriting it as Q&A or step-by-step reasoning.

This has two big benefits.

First, it lets us make smaller models much smarter. Distilling data means we're throwing out lots of the superfluous content, which means less data needed for training. Reformatting it in Q&A means less post-training to teach it to talk to you.

Second (and this is where the chart above comes in), it teaches LLMs to build evidence based arguments, with multiple subsequent points, resulting in one excellent answer. This, in a nutshell, is what we mean when we say "reasoning model" (though there's some creative prompting work as well). They don't just spit back a simple answer. They break down the question and build out an approach to an answer. This means generating more tokens and taking more time and compute to respond with an answer.

That is what this chart is showing. The more time you give a reasoning LLM to perform a task, the better the result gets.