Captaining IMO Gold, Deep Think, On-Policy RL, Feeling the AGI in Singapore — Yi Tay 2

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What does Yi Tay say about re‑joining Google DeepMind and the new “Reasoning and AGI” team?

Yi Tay 0:00

The thing that I find the most useful about like these models in general is like when I have these big spreadsheets of a lot of results and I just need to make plots of it. I think models can quite go use a screenshot and make a plot of this. I hate making this method like stuff about it's so annoying. There were so many moments this year where AI suddenly crossed that like that immersion thing. Why did AI code that we just discussed? I think like Nano Bernard and I also got to the point where I usually like if you make these images it's just like for fun, you just tr troll your friend or something like that. But like Nano Manana actually really got so good.

Swyx 0:36

Welcome back. How are you? Yeah, I'm good. I'm good. Great to be back. It's been one one and a half years. Yeah, it's been one and a half years. Feels like a long time. So last time we talked, you were at Rekah. Yeah. And then you joined GDM again, working for Cork again. Yeah. And more recently you've started GDM Singapore. Yeah. Is it GDM Singapore or Gemini Singapore? I don't know if you've named the team. Oh I I I think we have a Gemini team in Singapore, yeah. Gemini team in Singapore. It's called Reasoning and AGI. Yeah, reasoning and AGI. Is it important to have AGI in the name?

Yi Tay 1:08

It was like a light thing that we put AGI in. Yeah, I think that like one reason why we work on these models is that we want to get to AGI. And I just with some bike thing that we added the A GI to the job posting. Yeah. There is no like formal name of the team yet yet, but it's basically the Gemini team Singapore. Yeah.

Swyx 1:25

I mean I think people are like Trying to f triangulate Amazon has an A G I T and you guys have an A. I T and then Let's say Meta now has a super intelligence team. What are people signaling when they choose these names for their teams? Do they have, oh, we have a pen? Or is it just vibes?

Unknown 1:41

You

Swyx 1:41

try to official hot take somebody? No. You have officially AGI in your job title.

Yi Tay 1:46

No, it's not a DIM name. It's this is not Latin area. Yeah, it's just, you know, we we just want to signal the North Star of We pretend these models get to AGI. Yeah.

Swyx 1:54

Yeah. No. I wasn't really fishing politics. Okay, so you rejoined GDM. Yeah. And I think last time when you talked about I listened back to the whole thing, it was an amazing episode last time. You were talking about how it's like externally. We're in Brain and came out and now you're back in G DM. Yeah. I wonder what's your just your general reflections just plugging back into the Google infrastructure?

Yi Tay 2:15

Oh yeah, so I guess coming back, it's very interesting because it felt and returned to Google, like everything, including your LDAP, your username, it's all the same. It it's like you play Pokemon, you you leave it aside and then you go back and you click continue. Yeah, you save game and continue game. It's like that. Obviously the last one point five years, while I was away, many things have changed. Brain is now part of GDM and stuff. So I think that obviously a lot of things have changed. But I think overall the m coming back has been pretty seamless.

Why did the team add “AGI” to the job title and how do they view that label?

Yi Tay 2:43

Obvious obviously I I love Google infrastructure and I think debuts are great and stuff like that. Yeah. And I'm very glad to be back to to Google Infra. Yeah.

Swyx 2:52

And was the intention always that

Yi Tay 2:54

you were going to work on D Think? No, not really. I think I miss research a lot, like doing like research. Not like super fundamental research, but like close to model research, right? But I really miss being at the frontier and trying to s go beyond that, right? So I really miss that a lot. And I think when I came back a big thing wasn't a thing and I don't think there was any plans actually. It was just like I'm just going to work on research and see what happens, yeah.

Swyx 3:19

But I'm sure I guess there was some inclination that reasoning is the next frontier and that's like obviously the most rewarding research path, especially this year.

Yi Tay 3:29

Yeah, I think reasoning these days reasoning and RL is like probably quite it's m RL reasoning comes from I spent a lot of my past life, I call it the past art, working on like architectures and pre training.

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Chapters

8 chapters

1 What does Yi Tay say about re‑joining Google DeepMind and the new “Reasoning and AGI” team? 0:00–2:43 2 Why did the team add “AGI” to the job title and how do they view that label? 2:43–14:02 3 How do Yi and the host explain the difference between on‑policy and off‑policy reinforcement learning? 14:02–27:24 4 What are the latest experiences with AI‑assisted coding and the Pokémon benchmark? 27:24–45:33 5 Is attention the only thing needed for modern AI models? 45:33–47:13 6 Why do researchers think we’re stuck in a transformer local‑minimum? 47:13–50:28 7 How important is data‑efficiency versus compute for future breakthroughs? 50:28–54:06 8 What are the hiring priorities and talent strategy for the new GDM Singapore team? 54:06–1:31:54

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Topics in this episode

YouTube 6 AI 3 Google 3 OpenAI 3 Anthropic 1 London 1 Gemini 11 Jason 1 Singapore 2 Lean 2 AGI 6 Jonathan 2

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Recorded23 Jan 2026

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Show notes

From shipping Gemini Deep Think and IMO Gold to launching the Reasoning and AGI team in Singapore, Yi Tay has spent the last 18 months living through the full arc of Google DeepMind's pivot from architecture research to RL-driven reasoning—watching his team go from a dozen researchers to 300+, training models that solve International Math Olympiad problems in a live competition, and building the infrastructure to scale deep thinking across every domain, and driving Gemini to the top of the leaderboards across every category. Yi Returns to dig into the inside story of the IMO effort and more! We discuss: Yi's path: Brain → Reka → Google DeepMind → Reasoning and AGI team Singapore, leading model training for Gemini Deep Think and IMO Gold The IMO Gold story: four co-captains (Yi in Singapore, Jonathan in London, Jordan in Mountain View, and Tong leading the overall effort), training the checkpoint in ~1 week, live competition in Australia with professors punching in problems as they came out, and the tension of not knowing if they'd hit Gold until the human scores came in (because the Gold threshold is a percentile, not a fixed number) Why they threw away AlphaProof: "If one model can't do it, can we get to AGI?" The decision to abandon symbolic systems and bet on end-to-end Gemini with RL was bold and non-consensus On-policy vs. off-policy RL: off-policy is imitation learning (copying someone else's trajectory), on-policy is the model generating its own outputs, getting rewarded, and training on its own experience—"humans learn by making mistakes, not by copying" Why self-consistency and parallel thinking are fundamental: sampling multiple times, majority voting, LM judges, and internal verification are all forms of self-consistency that unlock reasoning beyond single-shot inference The data efficiency frontier: humans learn from 8 orders of magnitude less data than models, so where's the bug? Is it the architecture, the learning algorithm, backprop, off-policyness, or something else? Three schools of thought on world models: (1) Genie/spatial intelligence (video-based world models), (2) Yann LeCun's JEPA + FAIR's code world models (modeling internal execution state), (3) the amorphous "resolution of possible worlds" paradigm (curve-fitting to find the world model that best explains the data) Why AI coding crossed the threshold: Yi now runs a job, gets a bug, pastes it into Gemini, and relaunches without even reading the fix—"the model is better than me at this" The Pokémon benchmark: can models complete Pokédex by searching the web, synthesizing guides, and applying knowledge in a visual game state? "Efficient search of novel idea space is interesting, but we're not even at the point where models can consistently apply knowledge they look up" DSI and generative retrieval: re-imagining search as predicting document identifiers with semantic tokens, now deployed at YouTube (symmetric IDs for RecSys) and Spotify Why RecSys and IR feel like a different universe: "modeling dynamics are strange, like gravity is different—you hit the shuttlecock and hear glass shatter, cause and effect are too far apart" The closed lab advantage is increasing: the gap between frontier labs and open source is growing because ideas compound over time, and researchers keep finding new tricks that play well with everything built before Why ideas still matter: "the last five years weren't just blind scaling—transformers, pre-training, RL, self-consistency, all had to play well together to get us here" Gemini Singapore: hiring for RL and reasoning researchers, looking for track record in RL or exceptional achievement in coding competitions, and building a small, talent-dense team close to the frontier — Yi Tay Google DeepMind: https://deepmind.google X: https://x.com/YiTayML Chapters 00:00:00 Introduction: Returning to Google DeepMind and the Singapore AGI Team 00:04:52 The Philosophy of On-Policy RL: Learning from Your Own Mistakes 00:12:00 IMO Gold Medal: The Journey from AlphaProof to End-to-End Gemini 00:21:33 Training IMO Cat: Four Captains Across Three Time Zones 00:26:19 Pokemon and Long-Horizon Reasoning: Beyond Academic Benchmarks 00:36:29 AI Coding Assistants: From Lazy to Actually Useful 00:32:59 Reasoning, Chain of Thought, and Latent Thinking 00:44:46 Is Attention All You Need? Architecture, Learning, and the Local Minima 00:55:04 Data Efficiency and World Models: The Next Frontier 01:08:12 DSI and Generative Retrieval: Reimagining Search with Semantic IDs 01:17:59 Building GDM Singapore: Geography, Talent, and the Symposium 01:24:18 Hiring Philosophy: High Stats, Research Taste, and Student Budgets 01:28:49 Health, HRV, and Research Performance: The 23kg Journey

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