O3 and the Next Leap in Reasoning with OpenAI’s Eric Mitchell and Brandon McKinzie

**Elad Gil** (0:05)
Hi, listeners, and welcome back to No Priors. Today, I'm speaking with Brandon McKinzie 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.

**Brandon McKinzie** (0:27)
Thanks for having us.

**Eric Mitchell** (0:28)
Yeah, thanks for having us.

**Sarah Tavel** (0:29)
Do you mind walking us through O3, what's different about it, what it was in terms of a breakthrough, in terms of a focus on reasoning, and you're adding memory and other things versus the Sikora Foundation Model, LLM, and what that is?

**Eric Mitchell** (0:41)
O3 is our most recent model in this O series line of models that are focused on thinking carefully before they respond.
These models are in some vaguely general sense smarter than models that don't think before they respond, similarly to humans. It's easier to be more accurate if you think before you respond. I think the thing that is really exciting about O3 is that not only is it just smarter, if you make an apples to apples comparison to our previous O series models, it's just better at giving you correct answers of math problems or factual questions about the world or whatever. This is true and it's great. We will continue to train models that are smarter. But it's also very cool because it uses a lot of tools that enhance its ability to do things that are useful for you. You can train a model that's really smart, but 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, there's just a limitation to how the sorts of things that an LLM can do efficiently, whereas a relatively simple Python program can solve a particular problem very easily. 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 further enhance its abilities and whether that's doing 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 review the results and adjust course as it sees fit, instead of you having to be so prescriptive about each step along the way. The model is able to take these high-level requests, like do some due diligence on this company and maybe run some reasonable forecasting models on so-and-so thing, and then write a summary for me, the model will infer a reasonable set of actions to do on its own. It gives you a higher level interface to doing some of these more complicated tasks.

**Sarah Tavel** (2:49)
That makes sense. It sounds like basically there's a few different changes between your core sort of GPT models, where now you have something that takes a pause to think about something. So at inference time, there's more compute happening, and then also it can do sequential steps because it can kind of infer what are those steps and then go act on them. How did you build or train this differently from just a core foundation model, or when you all did GPT 2.5 and 4 and all the various models that have come over time, what is different in terms of how you actually construct one of these?

**Brandon McKinzie** (3:20)
I guess the short answer is reinforcement learning is the biggest one. So yeah, rather than just having to predict the next token and some large pre-training corpus from everywhere essentially, now we have a more focused goal of the model solving very difficult tasks and taking as long as it needs to do to figure out the answers to those problems. Something that's kind of magical from a user experience for me was, we've in the past for our reasoning models talked a lot about test time scaling, and I think for a lot of problems, without tools, test time scaling might occasionally work, but at some point the model is just kind of ranting in its internal chain of thought, especially for some visual perception ones. It knows that it's not able to see the thing that it needs, and it just kind of loses its mind and goes insane. And I think tool use is a really important component now to continuing this test time scaling, and you can feel this when you're talking to O3. At least my impression when I first started using it was the longer it thinks, like I really get the impression that I'm going to get a better result, and you can kind of watch it do really intuitive things. And it's a very different experience, but being able to kind of trust that as you're waiting, like it's worth the wait and you're going to get a better result because of it. And the model is not just off doing some totally irrelevant thing.