How GPT-5 Thinks — OpenAI VP of Research Jerry Tworek
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What does “thinking” mean when ChatGPT says it’s thinking?
O one, to be perfectly honest, it was really mostly good at solving puzzles. It was almost more like a technology demonstration. O3, there's really been something like a tectonic shift, the trajectory of AI. GPT-5 in some way I can know can be considered as like O3.1, but I am after as something that would be that would be the next pretty significant jump. We understand that there there's only one mo time. Time in history where AI is being built and deployed and developed. We are together in the skull that's larger than every one of us.
Hi, I'm Matt Turk from Firstmark. Welcome to the Matt Podcast. Today my guest is Jerry Tvorek, VP of research at OpenAI, and a member of the Metis list of the world's top AI researchers. In this episode, we go deep on how models actually reason. We also go behind the scenes at OpenAI, how a few big bets get staffed, why everyone knows everything, and how that culture shifts fast. Please enjoy this great conversation with Jerry. Hey Jerry, welcome. Hello.
Very happy to
be here. We are going to talk about reasoning a lot in this conversation. At a high level, what does reasoning actually mean when uh we talk to Chat GPT and ChatGPT says it's thinking? What actually is happening behind the scenes?
I think that that the thinking process is at least a good analogy as we were in the i in the early days of AI always had this goal, dream of trying to teach models to reason. We were thinking about it, spending more time to get better results. If like human is posed with a very hard problem in front of them, uh very rarely they have they have answers straight away. Sometimes they need To find that answer. Sometimes they need to perform certain computation. Sometimes they need to like look up some information. Sometimes they need to teach themselves something. And the process is reasoning, is like getting to an answer that you don't yet know. Like it in some way it can be called search, but it's not really like a very naive search.
Search is a loaded word. But reasoning is the process of getting to an answer and a work that you need to do that is like Longer than what usually is considered answering a question. I think that the difference is here, like answering a question usually means you already know the answer and you just just elicit the answer you know. And the process of reasoning is getting to the answer that you don't know. And usually the longer you spend on getting to this answer for whatever you need to do to get there, the better, the better it gets.
And uh we've all become familiar since you guys uh released uh O1, I guess a little over a year ago nine in September uh two thousand twenty-four, with the concept of a chain of thought, which is in layman's term, the the little messages that you see when you query uh ChatGPT and it tells you uh it shows its work, it's it tells you what what what it does. What does that actually do? Is that a a logical tree and it eliminates Option after option, w w what actually happens?
Language models do on on their own like fundamental level is they are they are often called as next token prediction machines. And I that that's not the completely accurate in the age of reinforcement learning, but they still operate on mostly on tokens that are mostly text. The the language models again are these days also multi-model and they they operate on on on on most C text. But to simplify a little bit for a second. Uh language models generate text and what chain of thought is, is their thinking process verbalized using human words and human concepts. So the the magic that we are seeing why this is all possible is that while you are training on all of internet on a lot of human knowledge and human thinking process, the model starts learning
In some ways, to think how humans do, and in some ways get to the answers how humans do from seeing humans do it a lot in the text that was that was pre-generated and that that that was based in the training data on humans. And then the chain of thought is basically eliciting that capability in language models of like thinking and getting to an answer like humans.
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Chapters
8 chapters
1
What does “thinking” mean when ChatGPT says it’s thinking?
0:00–8:34
2
How does chain‑of‑thought (CoT) turn token prediction into reasoning?
8:34–18:07
3
What are the key differences between O1, O3 and GPT‑5 in reasoning ability?
18:07–26:14
4
How did Jerry’s early life in Poland and trading career lead him to AI research?
26:14–35:50
5
How does OpenAI decide which research projects to prioritize and fund?
35:50–45:37
6
What is reinforcement learning (RL) and how is it analogous to training a dog?
45:37–53:56
7
What are the biggest technical challenges when scaling RL for large language models?
53:56–1:04:18
8
Can the combination of massive pre‑training and scaled RL eventually achieve AGI?
1:04:18–1:16:03
Speakers
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