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

Page: https://stenobird.com/podcast/latent-space-ai-engineer/captaining-imo-gold-deep-think-on-policy-rl-feeling-the-agi-in-singapore-yi-tay
Text version: https://stenobird.com/podcast/latent-space-ai-engineer/captaining-imo-gold-deep-think-on-policy-rl-feeling-the-agi-in-singapore-yi-tay.md
Podcast: [Latent Space: The AI Engineer Podcast](https://stenobird.com/podcast/latent-space-ai-engineer)
Published: 2026-01-23T16:00:00+00:00
Episode link: https://www.latent.space/p/captaining-imo-gold-deep-think-on
Audio file: https://api.substack.com/feed/podcast/186610590/b986ed2096ba7f55a1fbad46cd0076d2.mp3
Processing state: processed
JSON: https://stenobird.com/v1/public/podcasts/latent-space-ai-engineer/episodes/captaining-imo-gold-deep-think-on-policy-rl-feeling-the-agi-in-singapore-yi-tay
Duration seconds: 5525

## Resource

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…

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