Tworek: OpenAI's competitive programming performance was a research byproduct
Jerry Tworek · How GPT-5 Thinks — OpenAI VP of Research Jerry Tworek · Oct 16, 2025 · at 1:01:49
Jerry Tworek, VP of Research at OpenAI, explains how OpenAI models achieved benchmark performance in competitive programming contests like ICPC.
0:00 / 0:42exact quote · 43.0s
▶ Watch the full episode on YouTube → 720p mp4 · rendered on demand · StarZero watermark
“I think we used like specifically programming puzzles for a while as a, Very nice research test bed of our ideas. Those are nice problems to experiment on them, and they weren't ever, like, considered part of the product, but it's, those are pretty, like, complex problems, and they require a whole bunch of thinking, are very nice to give rewards to it, so all of researchers just, like, working on those problems as a way of trying out their oral ideas, like, you always need a data set, I will, I'll take a data set of programming puzzles and try it, and I think, like, Because of that, a little bit, our moles just were always very, very good at competitive programming as a kind of byproduct. We never tried to be good at it”
quote is from the automated transcript, cleaned for reading: filler sounds and stutters are removed, nothing is rephrased. names can be misheard (the analysis reads context, assessments check outside sources). how →
More from Jerry Tworek
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.”