[State of RL/Reasoning] IMO/IOI Gold, OpenAI o3/GPT-5, and Cursor Composer — Ashvin Nair, Cursor

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How did Ashvin’s journey go from Berkeley robotics to OpenAI and then to leading ML at Cursor?

Unknown 0:00

Okay, we're here at New Rips. We're recording a special Lane Space coverage of just the folks at New Rips. And we're here with Ashrain from Cursor. Welcome.

Ashvin Nair 0:08

Hi. Yeah, thanks for having

Unknown 0:09

me. So I guess the like Ashren from Cursor is like a new identity. I didn't even know if I should say that, because you only joined Cursor for three months. Uh before that, you're openly iWork in O103. Before that, Berkeley uh PhD in RL just but focus on robotics. Robotics, yeah. Is it weird

Ashvin Nair 0:26

searching for robotics to language models? Okay, this is kind of interesting because a lot of people have been kind of doing this. Um I mean OpenAI Yeah, exactly for robotics. So actually no, no, uh I I actually was um at OpenAI in twenty seventeen also uh working on robotics. Uh yeah, I was interning like right before my PhD where I worked on robotics there.

Unknown 0:43

2017,

Ashvin Nair 0:43

is that

Unknown 0:43

like Japan and uh that is Japan with their career? He was all famously open AI's first intern.

Ashvin Nair 0:49

Oh really? Okay. Then he might have been before. Um but yeah, uh there was like a like fifteen interns. It was a very different company. It was just like um robotics, Dota, and like fifteen interns that summer all having like pretty exciting individual products. Like uh yeah, that set of interns if you look at where they are now, it's kinda cool. Yeah. Um but yeah, uh anyone from that class that like you would shout out? Um Like uh there's just like a lot of cool papers that came out, like Laro Pinto, now is it uh NYU, um Uh yeah. The uh the person who leads um reasoning at X AI, I forgot his name. Eric. Uh well he left. No Eric. But yeah, I forgot his name, but um he he worked on like K fac and stuff. I think um Vision dude, Greg?

Ashvin Nair 1:31

Not great, but yeah. Um but yeah, it was it was like an exciting time to be there. Um but yeah, I I think I think robotics is a pretty good fit for LMs because like the switch ends up being pretty um Like, you know, you can do similar things. Like you want to look at a lot of data. It's like kind of um hard to get like stuff get hard to get stuff working in robotics world. I think, you know, it kind of builds like very gritty people who like look at data a lot, that kind of thing. So um yeah, for whatever reason I think like that transfer is like, yeah, it's happening a lot and I think it makes a lot of sense.

Unknown 2:01

One of my New Eps highlights so far, uh had dinner with it's like a small group dinner with Lex Freeman yes uh yesterday. And Lex used to be in robotics and he was like, My assessment of robotics people robotics people are the best to talk to at a News because they're most rounded, he says. Because they don't have a choice. They work with the real world. So yeah, look at data and then like the the most unhinged, the most detached from reality are like the the simulation people. I see. Yeah, yeah.

Ashvin Nair 2:26

Yeah, yeah, yeah. I think I agree. Yeah. Um yeah. And I kind of I actually did a little bit bit of both during my PhD. Like I work in kind of like, you know, like prototype ideas in sim and then get them working on uh real world robotics. And yeah, I mean probably like robotics is where you kind of feel AGI'd the least, right? 'Cause it's just so far away from working. Um now I think like Uh I think over the last year maybe there's been demos that have been super interesting from like physical intelligence and like Sunday and stuff that

Unknown 2:53

Yeah, I'm starting to be like, okay, like this this kind of thing.

Ashvin Nair 2:58

I haven't seen them. No. Apparently they've been doing demos. Uh I mean I'm pretty keen to seeing them. Yeah, I've I've seen the uh physical intelligence ones live and yeah, it's pretty impressive. Like just on like a uh in like someone's living room uh folding laundry and stuff, like you know, you can just like toss it in there.

Unknown 3:11

must be materialized. Yeah, yeah, yeah, yeah. Okay, and and uh last thing on robotics and we can just kind of pivot to O103. Uh just OpenAI has like is like restarting a robotics that team. Is that uh serious? Is that

Ashvin Nair 3:27

I actually know very little about it, um because yeah, I was in like a pre-diff to Farsi org. So um yeah, I mean I I think it's serious.

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Chapters

8 chapters

1 How did Ashvin’s journey go from Berkeley robotics to OpenAI and then to leading ML at Cursor? 0:00–5:52 2 Why do robotics researchers think their work is more grounded than simulation‑only work? 5:52–13:08 3 What is the “IOI Gold paradox” and why didn’t achieving it change the world? 13:08–20:18 4 Why did the RL research era (2017‑2022) overfit to benchmarks and stall? 20:18–29:30 5 How did the o1/origin story unfold and what convinced the team to scale it? 29:30–38:08 6 Why does Cursor believe continual learning with policy updates every two hours is a game‑changer? 38:08–42:21 7 How does Composer enable product‑model co‑design and keep engineers in the loop? 42:21–44:58 8 What is the vision for infinite‑memory continual learning and why is it the next paradigm shift? 44:58–44:59

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Topics in this episode

OpenAI 6 Nvidia 1 Anthropic 1 Chris 2 LLM 1 Cursor 7 Jacob 1 Lee 1 TAB 2 Ilya Sutskever 1 Sasha 1 RL 2

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Recorded30 Dec 2025

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Show notes

From Berkeley robotics and OpenAI’s 2017 Dota-era internship to shipping RL breakthroughs on GPT-4o, o1, and o3, and now leading model development at Cursor, Ashvin Nair has done it all. We caught up with Ashvin at NeurIPS 2025 to dig into the inside story of OpenAI’s reasoning team (spoiler: it went from a dozen people to 300+), why IOI Gold felt reachable in 2022 but somehow didn’t change the world when o1 actually achieved it, how RL doesn’t generalize beyond the training distribution (and why that means you need to bring economically useful tasks into distribution by co-designing products and models), the deeper lessons from the RL research era (2017–2022) and why most of it didn’t pan out because the community overfitted to benchmarks, how Cursor is uniquely positioned to do continual learning at scale with policy updates every two hours and product-model co-design that keeps engineers in the loop instead of context-switching into ADHD hell, and his bet that the next paradigm shift is continual learning with infinite memory—where models experience something once (a bug, a mistake, a user pattern) and never forget it, storing millions of deployment tokens in weights without overloading capacity.We discuss:* Ashvin’s path: Berkeley robotics PhD → OpenAI 2017 intern (Dota era) → o1/o3 reasoning team → Cursor ML lead in three months* Why robotics people are the most grounded at NeurIPS (they work with the real world) and simulation people are the most unhinged (Lex Fridman’s take)* The IOI Gold paradox: “If you told me we’d achieve IOI Gold in 2022, I’d assume we could all go on vacation—AI solved, no point working anymore. But life is still the same.”* The RL research era (2017–2022) and why most of it didn’t pan out: overfitting to benchmarks, too many implicit knobs to tune, and the community rewarding complex ideas over simple ones that generalize* Inside the o1 origin story: a dozen people, conviction from Ilya and Jakob Pachocki that RL would work, small-scale prototypes producing “surprisingly accurate reasoning traces” on math, and first-principles belief that scaled* The reasoning team grew from ~12 to 300+ people as o1 became a product and safety, tooling, and deployment scaled up* Why Cursor is uniquely positioned for continual learning: policy updates every two hours (online RL on tab), product and ML sitting next to each other, and the entire software engineering workflow (code, logs, debugging, DataDog) living in the product* Composer as the start of product-model co-design: smart enough to use, fast enough to stay in the loop, and built by a 20–25 person ML team with high-taste co-founders who code daily* The next paradigm shift: continual learning with infinite memory—models that experience something once (a bug, a user mistake) and store it in weights forever, learning from millions of deployment tokens without overloading capacity (trillions of pretraining tokens = plenty of room)* Why off-policy RL is unstable (Ashvin’s favorite interview question) and why Cursor does two-day work trials instead of whiteboard interviews* The vision: automate software engineering as a process (not just answering prompts), co-design products so the entire workflow (write code, check logs, debug, iterate) is in-distribution for RL, and make models that never make the same mistake twice—Ashvin Nair* Cursor: https://cursor.com* X: https://x.com/ashvinnair_Full Video EpisodeTimestamps00:00:00 Introduction: From Robotics to Cursor via OpenAI00:01:58 The Robotics to LLM Agent Transition: Why Code Won00:09:11 RL Research Winter and Academic Overfitting00:11:45 The Scaling Era and Moving Goalposts: IOI Gold Doesn't Mean AGI00:21:30 OpenAI's Reasoning Journey: From Codex to O100:20:03 The Blip: Thanksgiving 2023 and OpenAI Governance00:22:39 RL for Reasoning: The O-Series Conviction and Scaling00:25:47 O1 to O3: Smooth Internal Progress vs External Hype Cycles00:33:07 Why Cursor: Co-Designing Products and Models for Real Work00:34:14 Composer and the Future: Online Learning Every Two Hours00:35:15 Continual Learning: The Missing Paradigm Shift00:44:00 Hiring at Cursor and Why Off-Policy RL is Unstable Get full access to Latent.Space at www.latent.space/subscribe

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