[State of Post-Training] From GPT-4.1 to 5.1: RLVR, Agent & Token Efficiency — Josh McGrath, OpenAI
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What motivated Josh McGrath to move from pre‑training data curation to post‑training research at OpenAI?
So Light and space, the trunatoph Right up. Light and space. Well here with Josh from OpenAI. Welcome. How else you introduce yourself? How what
uh yeah, I work on a bunch of the thinking models at OpenAI. Um and like recently I've been sort of focused on doing search related stuff. But yeah, just a post training researcher at OpenAI.
Yeah, and you were on with us for GPT four point one. We were talking about with Michelle, who's on maternity leave. I I didn't know that. Um and uh now we're at five point one. It's been a it's been a whole generation.
Yeah, it's been wild. And like you know, 4.1 was a non thinking model. And then since then I, you know, we sort of switched into doing Is that your last? Was your last? Uh no, we're still we still are releasing non thinking models. Um, but that one was the one that we did that was like API specific non thinking. Um so you know, focus has shifted a little.
Yeah. How'd you get into post training?
Um, so previously before opening, I was doing like pre-training data curation stuff. And I think what I was seeing from like the news and looking at papers is like, oh, it seems like a lot of not pre training's dead, but I was like, oh, there's gonna be so much interesting stuff in post training. And at that point, I was like, I really wanna like make some contributions there. And I mean, it's not even necessarily that like pre training was dead, but it It was definitely changing and like, you know, do I wanna make compute efficiency wins of like three percent or do I wanna like change the behavior by forty percent? And honestly it just seemed more more exciting to go to post training and many late nights later.
Uh that's definitely true.
It's a different kind of data and engineering discipline too. It's very strange. Like the the the kind of work that you need uh in m especially R L
Like scaling it. Yeah, definitely. I think like uh, for example, the number of moving parts in an RL run is just a lot higher. Like in some ways, if order of magnitude, or I don't know if we could do order of magnitude, but if you think about like pre-training, you know, you're moving tokens to many machines, and then you're getting like a basically a scalar from them, and then you're back propping. Yeah. The issue with RL is like you're doing tasks. And each task could have like a a different grading setup. And each one of those different grading setups, that's like more infrastructure. And so, you know, when I'm staying up late trying to figure out what's going on with a run, it could be in way more things than there is in a pre training run generally.
Yeah. And does it matter if you own the code of the task or is it an outsourced third party person or um you know, I'm my sense of it and the external sense of it, obviously I don't see it up close, is that you work a lot with external partners and I'm sure also some internal stuff, but which is better?
Honestly, I don't think I'll comment like too much on like how many external partners Well
there there are some and there there's some internal. Yeah,
there
there's we do like it's like the but like the technical trade-off
of like well Shit, like I don't own this code. Okay, you know. So well, when it comes to I don't own this code, actually, like when, you know, when I'm babysitting a run or something, it doesn't really matter if it's like internal, external, whatever.
How does the RLVR era differ from the earlier PPO vs DPO debate and why does data quality matter more than the math?
Like, do I understand the system that's going underneath? And I think you end up having to like. jump into a lot more code that you're like, I actually don't know what this does. Because like I'll be watching the you know, I I work on my pieces of a run. Um and then there's also, you know, other people working on it. And like, do I understand what their code is doing? So at that way at like twelve thirty in the morning when I'm like something looks wrong and it's I'm like looking at this code, can I like get context fast enough to understand the thing wrong? Oh, I use codecs so much. It's really changed how I work. I feel like There's a degree to which like sometimes I feel trapped by Codex because if I spend like, you know, 30, 40 minutes writing something that looks like a design doc or something, Codex can do more work than I could do in a few hours in like 15 minutes.
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Chapters
7 chapters
1
What motivated Josh McGrath to move from pre‑training data curation to post‑training research at OpenAI?
0:00–3:14
2
How does the RLVR era differ from the earlier PPO vs DPO debate and why does data quality matter more than the math?
3:14–7:41
3
Why is scaling RL infrastructure more complex than pre‑training and what challenges arise when babysitting overnight runs?
7:41–13:15
4
What is the “shopping model,” how was it launched during Black Friday, and why is interruptibility a game‑changer?
13:15–17:29
5
How do personality toggles like Anton vs Clippy affect user experience and why are they becoming a key differentiator?
17:29–20:59
6
Why does token‑efficiency now matter more than wall‑clock time, and how did GPT‑5 → 5.1 slash token usage while improving evals?
20:59–24:47
7
What are the prospects for ultra‑long context windows and graph‑walk evaluations, and why might agents + graph walks matter more than 10 M‑token windows?
24:47–27:20
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