No Priors Ep. 113 | With OpenAI's Eric Mitchell and Brandon McKinzie

(instrumental music) . Hi, listeners, and welcome back to No Priors. Today, I'm speaking with Brandon McKinley and Eric Mitchell, two of the minds behind OpenAI's O3 model. O3 is the latest in the line of reasoning models from OpenAI, super powerful with the ability to figure out what tools to use, and then use them across multi-step tasks. We'll talk about how it was made, what's next, and how to reason about reasoning. Brandon and Eric, welcome to No Priors.

Thanks for having us.

Yeah, thanks for having us.

Do you mind walking us through, um, O3, what's different about it, what it, what it was in terms of a breakthrough in terms of, like, you know, a focus on reasoning and you're adding memory and other things versus just a core foundation model at LLM and what that is?

So O3 is, like, our most recent model in this O series line of models that, um, are focused on thinking carefully before they respond, and these models are in sort of some vaguely general sense smarter than, like, models that don't think before they respond. You know, similarly to humans, um, it's easier to be, you know, more accurate if you think before you respond. I think the thing that is really exciting about O3, um, is that not only is it just smarter if you make, like, an apples to apples comparison to our previous O series models, you know, it's just better at, like, giving you correct answers of math problems or factual questions about the world or whatever. Um, this is true and it's great, and we, you know, will continue to train models that are smarter, um, but it's also very cool because it uses a lot of tools that, um, uh, that, that enhance its ability to do things that are useful for you. So yeah, like, you can train a model that's really smart, but, like, if it can't browse the web and get up-to-date information, there's just a limitation on how much useful stuff that model can do for you. If the model can't actually write and execute code, um, there's just a limitation to, um, how, you know, the, the sorts of things that an LLM can do efficiently, whereas, like, a relatively simple Python program can, you know, solve a particular problem very easily. So, um, not only is the model it's, on its own smarter than our previous O series models, which is great, but it's also able to use all these tools that, like, further enhance its abilities and whether that's doing, like, research on something where you want up-to-date information or you want the model to do some data analysis for you, or you want the model to be able to do the data analysis and then kind of review the results and adjust course as it sees fit instead of you having to be so sort of prescriptive about, like, each step along the way. The model's sort of able to take these, like, high-level requests, like do some due diligence on this company and, you know, maybe run some reasonable, like, forecasting models on so and so thing, and then, you know, write a summary for me. Like, the model will kind of, like, infer a reasonable set of actions to do on its own. So it gives you kind of like a higher level interface to, to doing some of these more complicated, uh, tasks.