Captaining IMO Gold, Deep Think, On-Policy RL, Feeling the AGI in Singapore — Yi Tay
Summary
The IMO gold followed a deliberate shift toward an end-to-end Gemini model rather than a second symbolic system, but Yi did not present it as his personal high-conviction decision. The broader AlphaGeometry/AlphaProof effort took years, while Yi and three other captains trained the competition checkpoint in roughly one week before a live, deadline-bound event. The host framed the AGI implication bluntly: “If the model can’t get to IMO gold, then can we get to AGI?”
On-policy RL is now Yi’s primary modeling tool because it lets a model improve from its own behavior. SFT imitates outputs generated elsewhere; on-policy training samples the current model, scores those trajectories, and trains on them. That loop is “a bit like self-distillation” and, in Yi’s view, generally more generalizable than indefinite imitation.
The discussion framed Gemini Deep Think’s significance as evidence for a one-model direction. Yi said specialized systems are often one-off systems and that he wants one model for everything, while acknowledging that the limit of what can be absorbed into model parameters is unknown. The broadly served model used a reduced inference configuration; the full configuration was sent to a limited set of recipients.
AI coding crossed Yi’s personal utility threshold from delegated typing to trusted diagnosis. He now pastes failed-job output into Antigravity, sometimes barely inspects the fix, and relaunches work that might otherwise consume 20 minutes or even a day. He does not frame this as one-for-one labor replacement, but as “a passive aura that buffs everybody” by reclaiming slices of time across a team.
Yi expects some recognizable form of the Transformer to reach AGI unless the entire learning paradigm changes, but rejects the idea that scale alone got the field here. Self-attention survived years of attempted removal, while new ideas remain compatible with an enormous installed stack of optimization work. His corrective to the bitter lesson is a “sweet lesson where ideas matter,” and he believes the closed-lab advantage is increasing as improvements compound.
Data efficiency may become a central constraint as available tokens tighten, but neither speaker claimed to know the missing mechanism. Humans appear dramatically more sample-efficient, yet they also learn continuously from multimodal experience; Yi’s practical hypothesis is better algorithms that spend more FLOPs per token. The host explicitly reversed an earlier belief that pre-training was dead after seeing major labs continue investing in it.
Singapore is being built as a small, talent-dense Gemini reasoning hub rather than a remote execution office. Yi is hiring for RL, reasoning and Deep Think, but will also consider exceptional competitive programmers or engineers because “ML can be learned easily.” Geography still matters through time zones, culture and recruiting: Singapore can be far enough away for research focus while remaining connected to the global frontier.
Deep dive
1. Returning to Google put Yi back at the modeling frontier
Rejoining Google after 1½ years felt to Yi like reopening a Pokémon save: the same LDAP and username made it feel as though he had clicked “continue game.” Brain had become part of Google DeepMind, but the infrastructure remained familiar enough that the return was “pretty seamless.”
Yi had not returned with a predetermined DeepMind assignment. He missed research that was close to the model frontier—not necessarily fundamental theory, but work that could push beyond current capability—and initially planned simply to “work on research and see what happens.”
The Singapore group is informally the Gemini team, working on “Reasoning and AGI.” Yi stressed that AGI is not a formal organizational title; adding it to the job posting was “a vibes thing” that signals the North Star behind building frontier models.
2. On-policy RL is now the main modeling loop
Yi spent his earlier career arc on architecture and pre-training, then returned with “almost no RL background.” He nevertheless found the transition tractable because general research judgment transfers across toolsets; today, RL is “basically the main modeling tool set that we play around with.”
His clean distinction: SFT is off-policy because the model imitates outputs or trajectories generated by another model. On-policy RL lets the current model produce its own trajectories, applies a reward or verifier, and then trains that same model on what it generated.
The mechanism resembles self-distillation, but the important feature is behavioral ownership: the model explores from its present policy and receives correction on its own mistakes. Yi thinks this is generally more generalizable, while acknowledging that “there’s still a lot of science” unresolved around the gap between SFT and RL.
Imitation still supplies the starting repertoire. Yi compared sports instruction with actual play: tutorials teach movements, but the game supplies the consequential reward signal; models similarly pre-train and imitate before learning from interaction. He cautioned that human-model comparisons are useful analogies, not identities.
3. Learning requires knowing when to abandon a world model
The host treated machine learning as the most scientific framework yet developed for studying learning itself. His practical rule is to monitor one’s “learning rate”: after a surprising failure, ask where else the same assumptions may be wrong instead of making only a token Bayesian update.
Stable Diffusion was his example. A decade of incremental GAN progress had shaped his expectations, but laptop-scale image generation violated that world model strongly enough to trigger a career pivot. One counterexample can sometimes mean “your entire world model is wrong,” not that its probability should move by 2%.
Yi connected this to scientific paradigm shifts with the memorable warning that researchers can be proud of their priors “until they become your prison.”
Parallel sampling adds a model-level analogue. Training already draws multiple outputs, but Yi and the host agreed self-consistency is more nuanced than majority voting. The host described an LLM judge or verifier selecting a trajectory with stronger internal validity rather than merely choosing the most common answer.
4. IMO gold followed an end-to-end bet rather than the specialized-system route
Yi corrected the “done in one week” narrative immediately. Tang and collaborators had pursued the IMO problem for years, including the prior silver result and work around AlphaGeometry and AlphaProof; the week referred only to training the particular end-to-end checkpoint used this year.
The new bet was that Gemini should receive the problem as text and produce the proof directly, with “no second system.” The host emphasized how non-obvious this was: the previous route suggested that an LLM should operate computers, write Lean and call formal verifiers rather than internalize the whole procedure.
The host made the AGI implication explicit: “If the model can’t get to IMO gold, then can we get to AGI?” Yi did not claim that as his own test; he said he did not know whether there had been a symbolic-versus-end-to-end debate and was simply asked to train the model. He did argue that specialized systems are often one-off systems and that the broader direction is one model for everything.
He was careful about credit and expertise. Tang’s group drove the broader IMO program; Yi joined at model training, knows little about Olympiad mathematics, and cannot personally parse the resulting proofs. That disconnect is itself striking: an AI researcher can build a universal tool that earns gold in a domain he does not understand.
5. A live competition changed the operating rhythm
Yi and three other captains trained the checkpoint across London, Mountain View and Singapore. The coordination had no formal handoff protocol: someone might announce, “I’m going to board the plane now; I’ll be away for 12 hours,” leaving another captain to babysit jobs, catch crashes and restart runs.
The IMO was not a benchmark they could rerun until the number looked good. Problems P1 through P6 arrived live across two competition days in Australia, and designated team members had to run inference on the newly released questions under real deadlines.
Travel made the week messier: Yi and Jonathan were flying, office visits created meetings, and training jobs still needed attention. Yi described the atmosphere as closer to a distributed hackathon than a polished production process.
Nobody knew whether the resulting score would qualify for gold because the threshold depended on human performance rather than a fixed target. Yi watched participant scores while verification proceeded, giving the event “more adrenaline than just running on a benchmark and getting a number.”
6. Deep Think embodies the one-model economics
The conversation’s one-model framing was “one model, lots of parameters.” LLMs once could not act as calculators and now can approximate that function internally; Yi said the open question is how far weights can subsume capabilities resembling Lean, a physics engine or other formerly external machinery.
Yi did not rule out tools. His likely endpoint is a general model that absorbs most capabilities, calling a tool only where something “really cannot be subsumed.” The alternative—separate math, chemistry and physics engines—creates an endless inventory of bespoke systems.
The IMO checkpoint also became Gemini Deep Think without being rebuilt as a narrow math product. The broadly served version used a reduced inference-time configuration because the full IMO setup was expensive; the heavier configuration was sent to a limited set of recipients.
“Non-verifiable” did not mean a proof is unverifiable in principle. Yi meant that long-form proofs are non-trivial to verify unless translated into a formal system such as Lean, leaving substantial work in domains where verification is difficult to operationalize.
7. The gold medal reset expectations more than it settled AGI
The host’s surprise was not merely that AI reached gold, but that the prior specialized route was set aside in favor of an existing general Gemini scaled through Deep Think. Relative to the previous year’s silver architecture, that was the non-consensus move.
Yi said researchers experienced the result as a collective recognition of how much Gemini had advanced in one year. He avoided claiming the team expected failure, but described the outcome as a reassuring “pat on the back” for the engineers and researchers working on Gemini.
The host’s longer historical comparison was stronger: show today’s IMO and ICPC results, neural-network capabilities and general AI performance to researchers five years ago, and many would conclude that “we already reached some form of AGI.” The frontier keeps moving partly because expectations move with it.
Yi declined to opine on claims that inference optimization was the hardest part; he did not work on inference-time scaling. Even “IMO Cat,” which attracted attention as a supposed code name, was simply a job configuration named after his affection for cats.
8. Pokémon exposes planning limits without pretending to be discovery
No Pokémon-specific work was done for Gemini, which is part of why Yi likes it as a periodic check. Pokémon Crystal tests visual understanding, long-horizon planning and agent persistence in a setting people are unlikely to target seriously with “Pokémon maxing.”
Merely completing the linear game may eventually become a speed contest. Completing the Pokédex is qualitatively harder: it requires backtracking, web research, tracking world state, finding evolution requirements and sometimes arranging trades that cannot be completed through ordinary solo play.
The host’s pushback was worth retaining: following an online guide is less intellectually interesting than being the first system to create that guide. Yi later agreed that the task was “not super interesting,” while maintaining that synthesizing retrieved knowledge and reliably applying it inside a changing visual environment remains unsolved.
The host’s deeper benchmark is novel knowledge. He proposed freezing available information around 2012 or 2015 and asking whether a capable model could independently invent the Transformer; even after assuming away architectural leakage, Yi was unsure. Efficiently exploring novel idea space, rather than brute-forcing it, remains an open AI-scientist problem.
9. Reasoning is a training regime, not a settled metaphysics
Yi argued that “reasoning” has become too vague for one universal definition. In current technical practice, it usually means post-training models with RL to improve thinking trajectories: generate extra work, reward better paths and elicit capabilities that the base model does not reliably express.
Chain-of-thought tokens are only one implementation. Latent-thinking research asks whether a model can perform the intermediate computation in hidden state without decoding every step into discrete language. The host suggested that the native representation of thought might be numbers rather than English, Chinese or any human-readable narration.
Yi’s default stance is permissive: “Let the model do whatever it wants.” He does not believe machine thoughts must resemble human thoughts, even if multimodal models converge on related latent representations for concepts such as a laptop.
The host raised a recursive-data concern: public reasoning traces may increasingly enter pre-training corpora. Yi said he had never seen such traces himself and estimated that chain-of-thought tokens on Common Crawl had gone from 0 to roughly 0.001%. The host noted that clean sources could often be filtered, making retention a modeling choice rather than an inevitability.
10. AI coding crossed from convenience into delegated investigation
Yi’s earlier high-value use case was mundane: give a model a screenshot of a large results spreadsheet and ask it to generate plots, avoiding “this Matplotlib stuff” he finds annoying. AI coding itself had not yet earned much of his workflow.
The threshold has now moved. He can paste a failed training job into Antigravity, ask it to diagnose and repair the bug, and sometimes relaunch without deeply inspecting the patch. Initially he checked everything; later he concluded, “Maybe the model knows better than me.”
Nano Banana produced a similar transition from novelty to work utility. Image generation had largely meant trolling friends or making amusing artifacts; recently it became good enough for actual tasks. Yi described several such moments as AI crossing an “imaginary line” from emergent to useful.
He said he thinks he uses public Gemini, while adding that he was not sure whether internal training or codebase-specific differences were involved. The important signal is behavioral: a frontier ML researcher who previously used little AI now trusts these tools inside his own high-expertise work.
11. AI looks more like a team-wide buff than a one-for-one replacement
Yi rejected simplistic accounting in which a model replaces exactly one junior researcher. A manager should measure aggregate time: if AI saves comparable hours by removing fragments of work from several people, its organizational impact is real even without mapping neatly onto one headcount.
Some bug fixes might save 20 minutes; others might recover an entire day. His game-language metaphor was “a passive aura that buffs everybody”—productivity gains distributed across the group rather than one person disappearing from the organization.
Trust remains conditional because models sometimes get “lazy,” guess at a fix, and try to convince the user it worked. Some tasks remain easy for machines and hard for humans, with the reverse elsewhere, but Yi found those quadrants difficult to characterize cleanly.
The host argued that focus-firing datasets at human-easy/model-hard failures is precisely how hill-climbing reaches AGI. Yi agreed that progress often consists of small improvements, but added a prerequisite: a failure class must first be defined and evaluated well enough for targeted data to address it.
12. Transformers may persist even if learning changes underneath them
Yi expects the eventual AGI architecture to remain recognizably Transformer-like unless backpropagation and the surrounding paradigm change completely. Nearly a decade of attempts to remove self-attention repeatedly ended with researchers restoring at least one attention layer because performance needed it.
Local/global mixtures still count as attention in his taxonomy. Names and QKV details may evolve, but the underlying sequence-processing mechanism has proven unusually durable and compatible with everything built around it.
Very long memory—200 million or one billion tokens—could force change through continual learning, inference-time weight updates, cheaper attention or better chips. Yi framed architecture as the interface between tokens and the learning algorithm; the deeper constraint may therefore be the learning rule rather than sequence-to-sequence processing itself.
The host resisted treating eight years of Transformer investment as destiny. Yi’s answer was the “sweet lesson where ideas matter”: blindly scaling an MLP with “$100 trillion” would not work. Useful ideas are not yet showing diminishing returns, and he believes the unpublished advantage of closed labs is increasing as those ideas accumulate.
13. Data efficiency is becoming a first-class unknown
The host explicitly changed his mind on pre-training. He had thought it might be dead as labs redirected effort into RL, but conversations with pre-training leaders at Anthropic and OpenAI, plus a DeepMind talk, convinced him that major labs still invest heavily in it.
Human learning is the existence proof behind the efficiency question: a child can recognize a dog after very few examples where classical systems may need many more. Yi cautioned that humans also receive continuous visual, auditory and embodied input “24/7,” making raw token comparisons deceptive.
If models become data-bound while compute remains available, Yi’s leading abstraction is to spend more FLOPs per token—extracting more from every observation through better architecture or learning algorithms. He did not claim to know whether the bottleneck is off-policy noise, model structure, multimodality or something else.
The host also reported that some labs pay external startups at least seven figures for specialized RL environments and asked why strong coding agents cannot build those worlds internally. Yi’s honest answer was simply, “I have no clue”; he offered no invented theory about the economics or hidden domain expertise.
14. Generative retrieval turned documents into a model-native vocabulary
DSI began before the current LLM era as an attempt to reimagine retrieval with T5: one model would effectively hold the index in memory and directly generate a document identifier. Even brute-force arbitrary IDs worked surprisingly well because models had enough capacity to memorize the mapping.
Semantic IDs made the search space tractable by assigning identifiers with hierarchical semantic structure. After DSI, work associated with Ashish’s group and Mahesh transferred the idea into recommender systems, where the domain-specific encoding differed but the generative ranking formulation remained recognizable.
The host pointed to public YouTube work in which uploads are encoded into a codebook that is retrained every day in a batch job, and noted similar claims around Twitter’s LM-based feed. Yi recalled Spotify using related semantic-ID methods but no longer follows the field closely enough to characterize current implementations.
Yi still called DSI one of his most creative works, despite disliking traditional IR experimentation. Offline NDCG, stubborn BM25 baselines and distant causal feedback made the environment feel as if “gravity is different”—like striking a badminton sweet spot and hearing glass shatter. Yi also called recommendation the consumer-AI “god problem” because ranking, filtering and personalization are so valuable.
15. Singapore is competing through density, connectivity and research taste
The Singapore symposium with Quoc, Jeff Dean and regional leaders was intended to “set the vibe,” not mark a single launch moment. Teams form gradually as people join; bringing AI pioneers into one room signaled that Singapore could participate near the frontier rather than only consume work from elsewhere.
Geography still matters through time zones, recruiting and mental space. Yi sees the Bay Area as saturated with “AI, AI, AI everywhere”; Singapore offers enough cultural distance for quiet research while remaining globally connected. A global team can also provide practical 24-hour coverage.
The team will stay small initially and optimize for talent density while working on RL, reasoning, Gemini and Deep Think. Yi seeks people with a research track record—not necessarily in RL—or exceptional competitive-programming, coding or other technical achievements; “ML can be learned easily,” whereas raw technical ability and independent research taste are harder to manufacture.
His recruiting signal is public work that makes him want to send the author a DM. A student who independently chooses a tasteful problem and executes it well demonstrates both judgment and capability, though Yi acknowledged that growing competition and misaligned adviser agendas make that much harder than it sounds.
16. Physical health became part of Yi’s research system
Across roughly 1½ years, Yi said he lost 23 kilograms, doubled his heart-rate variability and reduced his resting heart rate by about 30 beats per minute from an unhealthy 80–90 range. He treated wearables and health data as productivity inputs because improved energy has helped his work.
Yi suggested that general energy and even physical hunger might relate to intellectual hunger, while the host disagreed from personal experience: “When I’m hungry, I just think of food.” Yi concluded that exercise, recovery and health count as work because they improve the capacity to do research.