OpenAI researcher Josh McGrath explains why OpenAI builds standalone models like Deep Research and the Shopping model and how model capabilities evolve over time.
Insight
McGrath: RLHF and RLVR differ by data quality, not optimization math
“Really, at the end of the day, like, RLHF, RLVR,
They're both policy gradient methods, but the, what's different is just like the input data.”
Insight
McGrath: DeepSeek Math's real breakthrough is verifiable reward trust, not GRPO
“As you said, it came out in the deep seek math paper, and like, it's an interesting optimization method, but it's like the more interesting thing that they have a new reward signal that they sort of like re that we can really, really trust. Like when, you know…”
Insight
McGrath: RL runs have far more infrastructure failure points than pre-training
“The issue with RL is, like, you're doing tasks, and each task could have, like, 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…”
Insight
McGrath: Design specs let Codex complete hours of coding in 15 minutes
“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 can do in a few hours in, like, 15 minutes.”
Assertion Supported
McGrath: GPT-5 Thinking Matches or Beats Deep Research on Published Evals
“I mean, I think if you look at our published evals, they're, they look, like, basically on par if it's not better, so, like, I mean, that's personally what I do.”
Assertion Partly supported
McGrath: GPT-5.1 dramatically reduced token usage over GPT-5 while boosting evals
“Yeah, and so you can see, like, from five to 5.1, our overall evals, you know, we bumped some. But if you look at a two D plot of how many tokens it takes for us to get that, it went way down.”