[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 to move from pre‑training data curation to post‑training research at OpenAI?

Swyx 0:00

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

Josh McGrath 0:15

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.

Swyx 0:26

Yeah, and you were on with us for GPT four point one. We were talking uh 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.

Josh McGrath 0:38

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.

Swyx 0:58

Yeah. How'd you get into post training?

Josh McGrath 1:00

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 is 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 is 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.

Josh McGrath 1:39

I that's definitely true.

Swyx 1:40

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 RL sc like scaling it.

Josh McGrath 1:52

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 of 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 backpropping. 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's in a pre-training run generally.

Swyx 2:32

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 with some internal stuff, but which is better?

Josh McGrath 2:51

Honestly, I don't think I'll comment like too much on like how many external partners

Swyx 2:55

Well there there are some and there there's some internal.

Josh McGrath 2:58

Yeah, there

Swyx 2:58

there's we do like the but the technical trade-off

Josh McGrath 3:01

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. 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.

How does the RL infrastructure for post‑training differ from pre‑training and why is it harder?

Josh McGrath 3:33

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

8 chapters

1 What motivated Josh to move from pre‑training data curation to post‑training research at OpenAI? 0:00–3:33 2 How does the RL infrastructure for post‑training differ from pre‑training and why is it harder? 3:33–6:58 3 How has Codex changed Josh’s daily workflow and why does he feel “trapped” by short agent sprints? 6:58–10:15 4 What is the core difference between RLHF and RLVR if both are policy‑gradient methods? 10:15–14:14 5 Why was the GRPO method from DeepSeek Math underappreciated and why do verifiable reward signals matter? 14:14–18:00 6 How does token‑efficiency (GPT‑5 → 5.1) impact evaluation speed, tool‑calling and agent workflows? 18:00–21:32 7 What are the “Anton vs Clippy” personality toggles and why do users care about model personality? 21:32–24:54 8 Why is hiring talent that can blend distributed‑systems engineering with ML research a bottleneck for the frontier? 24:54–27:20

Speakers

1 identified

Josh McGrath 16 min

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Topics in this episode

OpenAI 3 Anthropic 1 ChatGPT 1 Codex 2 AGI 1 RL 1 RLHF 1 Grok 4 1 Agent 1 shopping model 2 Graph Walks 1 Josh McGrath 1

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Recorded31 Dec 2025

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Show notes

From pre-training data curation to shipping GPT-4o, o1, o3, and now GPT-5 thinking and the shopping model, Josh McGrath has lived through the full arc of OpenAI's post-training evolution—from the PPO vs DPO debates of 2023 to today's RLVR era, where the real innovation isn't optimization methods but data quality, signal trust, and token efficiency. We sat down with Josh at NeurIPS 2025 to dig into the state of post-training heading into 2026: why RLHF and RLVR are both just policy gradient methods (the difference is the input data, not the math), how GRPO from DeepSeek Math was underappreciated as a shift toward more trustworthy reward signals (math answers you can verify vs. human preference you can't), why token efficiency matters more than wall-clock time (GPT-5 to 5.1 bumped evals and slashed tokens), how Codex has changed his workflow so much he feels "trapped" by 40-minute design sessions followed by 15-minute agent sprints, the infrastructure chaos of scaling RL ("way more moving parts than pre-training"), why long context will keep climbing but agents + graph walks might matter more than 10M-token windows, the shopping model as a test bed for interruptability and chain-of-thought transparency, why personality toggles (Anton vs Clippy) are a real differentiator users care about, and his thesis that the education system isn't producing enough people who can do both distributed systems and ML research—the exact skill set required to push the frontier when the bottleneck moves every few weeks. We discuss: Josh's path: pre-training data curation → post-training researcher at OpenAI, shipping GPT-4o, o1, o3, GPT-5 thinking, and the shopping model Why he switched from pre-training to post-training: "Do I want to make 3% compute efficiency wins, or change behavior by 40%?" The RL infrastructure challenge: way more moving parts than pre-training (tasks, grading setups, external partners), and why babysitting runs at 12:30am means jumping into unfamiliar code constantly How Codex has changed his workflow: 40-minute design sessions compressed into 15-minute agent sprints, and the strange "trapped" feeling of waiting for the agent to finish The RLHF vs RLVR debate: both are policy gradient methods, the real difference is data quality and signal trust (human preference vs. verifiable correctness) Why GRPO (from DeepSeek Math) was underappreciated: not just an optimization trick, but a shift toward reward signals you can actually trust (math answers over human vibes) The token efficiency revolution: GPT-5 to 5.1 bumped evals and slashed tokens, and why thinking in tokens (not wall-clock time) unlocks better tool-calling and agent workflows Personality toggles: Anton (tool, no warmth) vs Clippy (friendly, helpful), and why Josh uses custom instructions to make his model "just a tool" The router problem: having a router at the top (GPT-5 thinking vs non-thinking) and an implicit router (thinking effort slider) creates weird bumps, and why the abstractions will eventually merge Long context: climbing Graph Blocks evals, the dream of 10M+ token windows, and why agents + graph walks might matter more than raw context length Why the education system isn't producing enough people who can do both distributed systems and ML research, and why that's the bottleneck for frontier labs The 2026 vision: neither pre-training nor post-training is dead, we're in the fog of war, and the bottleneck will keep moving (so emotional stability helps) — Josh McGrath OpenAI: https://openai.com https://x.com/j_mcgraph Chapters 00:00:00 Introduction: Josh McGrath on Post-Training at OpenAI 00:04:37 The Shopping Model: Black Friday Launch and Interruptability 00:07:11 Model Personality and the Anton vs Clippy Divide 00:08:26 Beyond PPO vs DPO: The Data Quality Spectrum in RL 00:01:40 Infrastructure Challenges: Why Post-Training RL is Harder Than Pre-Training 00:13:12 Token Efficiency: The 2D Plot That Matters Most 00:03:45 Codex Max and the Flow Problem: 40 Minutes of Planning, 15 Minutes of Waiting 00:17:29 Long Context and Graph Blocks: Climbing Toward Perfect Context 00:21:23 The ML-Systems Hybrid: What's Hard to Hire For 00:24:50 Pre-Training Isn't Dead: Living Through Technological Revolution

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