Jerry Tworek
CEO & Co-founder, Core Automation. On 1 show, 1 appearance. The Shows tab opens the full record on each.
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Jerry Tworek is the CEO and co-founder of Core Automation and the former VP of Research at OpenAI. At OpenAI, he led research on reinforcement learning, code generation, and reasoning models.
1shows
1appearances
36statements
1resolved
1supported
0contradicted
100%fully supported
Everything Jerry Tworek said on any show that made the record, most notable first. Each card names its show and opens the statement there.
Tworek: GPT-5 can effectively be considered an iteration like 'o3.1'
“Like GPT-Five in some way I can be considered as like, oh, 3.1. It's a little bit of like, you know, iteration of like the same thing and the same concept”
Tworek: OpenAI team was initially underwhelmed by pre-trained GPT-4
“When we trained GPT-IV, we were pretty underwhelmed internally, and then there was a lot of moments, oh, we trained this small, we spent a lot of money on it, and it's kind of like, you know, pretty dumb, at least, like, you know, we have GPT-IV, GPT-III alrea…”
Tworek: Traditional human data labeling is becoming obsolete as models advance
“I think, like, in a way, I think it's getting more and more to be a thing of the past as the models are getting smarter and smarter. This is becoming less of a thing, but I think a few years back, and especially in GPT-IV days, this was the thing.”
Tworek: Calling LLMs strictly next-token predictors is inaccurate in RL era
“Language models do on their own, like fundamental level is they are often called as next token prediction machines. And that's not completely accurate in the age of reinforcement learning, but they still operate on mostly on tokens that are mostly text.”
Tworek: OpenAI's o1 was mostly a tech demo for solving puzzles
“O-one like, to be perfectly honest, it was really mostly good at solving puzzles and like maybe a few kind of thinking problems here and there, but it wasn't like, it wasn't a very useful model. It was almost more like a technology demonstration.”
Tworek: Coding agents are the first successful agentic AI products
“Like, coding agents are at the moment the first, like, pretty successful agentic products built on top of AI.”
Tworek: The landmark 2012 ImageNet AI results were not that significant
“From my perspective, and again, this is just how my brain works, that the 20 12 ImageNet results, like, weren't that significant.”
Tworek: Uninformed researchers pose a greater risk than IP leaks
“It is like, yeah, it is some like risk of losing IP, but I think the risk of not doing the right thing and of people not being informed about research and not being able to do the best research is much higher in my personal opinion and how, how I approach thos…”
Tworek: OpenAI's o1 release caught US AI labs unprepared for RL
“As far as I know, like our O-one release mostly caught a lot of us labs by surprise. They didn't have like similarly advanced RL research program to my knowledge, basically no one.”
Jerry Tworek: Pre-training AI models is mathematically simple compared to RL
“The first thing that is important to know and understand, RL is hard. Like, conceptually, if you think about it, and there's still a lot of depth to it, but very conceptually, mathematically speaking, pre-training is dead simple.”
Tworek: Pre-training and RL are necessary for AGI, but not sufficient
“I generally think something that we are doing, like, pre-training today is necessary. I think something that, like, we are doing RL today is necessary, and there will surely be a few things more, and like, we have a lot of, Very ambitious research programs on …”
Tworek: Reinforcement learning and pre-training require each other to succeed
“And like, I don't like in terms of a pure RL, I don't think like really pure RL makes sense. RL needs Pre-training to be successful. And I think pre-training, as I said before, needs RL to be successful as well.”
Tworek: OpenAI's high and low reasoning modes use the exact same model
“Where you can have like a high rezoning model and the low rezoning models. And this is like in the end, the same model. You just, we just tweak the parameter, which says we want you to think longer or shorter.”
Tworek: OpenAI focuses research on only three or four large-scale projects
“We all work on a very few projects total. There are not that many projects. OpenAI is not trying to do everything. We are not trying to like have portfolio. We are trying to have like multiple different bets. Always the idea is we do a few core things really, …”
Tworek: Top-down management does not work in AI research organizations
“Top down structuring of research doesn't work in research organizations. I really don't believe in it because like you are not kind of hiring some of the smartest people in the world and open air has incredibly, incredibly smart people. To kind of tell them wh…”
OpenAI VP Jerry Tworek pays $200 per month for ChatGPT Pro
“I think I am pretty heavy user of ChatGPT right now, happily paying like 200 dollars a month for it”
Tworek: Ilya Sutskever set OpenAI's current RL research roadmap in 2019
“And what he said at the beginning of 2019 was to train large generative model on all data we can and then do reinforcement learning on it. That was the OpenAI research Plan at the beginning of 2019. And this is exactly what we are doing today.”
Tworek: OpenAI's RL algorithm is not GRPO but shares similar components
“Like what we, what OpenAI is doing is not exactly GRPO. It is slightly different in many different ways, but like some parts are definitely similar.”
Tworek: OpenAI internal models can currently reason for hours on tasks
“The models I can think for, like, 30 minutes, hour, two hours these days on certain, certain types of Tasks and problems like even, even, even longer than that.”
Tworek: OpenAI does not currently train models online via live interactions
“This is not what I am aware, at least, like, not, not, not what OpenAI is doing at the moment”
Tworek: Online RL shouldn't be used at ChatGPT's scale without robust safeguards
“So I, at least until, until we have a really good safeguards, I don't think we should try to do that in anything like as complex and large scale as ChatGPT.”
Tworek: AI models need deep understanding of consequences for alignment
“I don't think like you can just tell them all like, A few show with a few good things to do, and it will do them all needs to deeply understand its action and consequences to really be able to choose the right thing.”
Tworek: AI alignment is a never-ending pursuit as human goals evolve
“And it's I think it's a never ending pursuit because like, even, even for humans, it's not super easy to define what's, what do we consider a light? And I think as our civilization will evolve, it will, the notion of alignment and the goals of humanity will, K…”
Tworek: AI reasoning spent on unknown answers yields better results with time
“I think that, that, that difference is here, like answering a question usually means you already know the answer and you 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 spe…”
One line per show, most statements first. The link opens Jerry's full record on that show: the calibration, argument clarity, speaking style and every statement made there.
| Show | Role there | Eps | Statements | Record | |
|---|---|---|---|---|---|
 MADLEDGER | CEO & Co-founder, Core Automation | 1 | 36 | 100% 1/1 | full record on the MAD Podcast → |