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For years, the story of AI progress has been one of moving goalposts. First, it was chess. Deep Blue beat Kasparov in 1997, and people said, fine, chess is a well defined game of search and calculation, not true intelligence. Then it was Go, which has a state space so vast it requires "intuition." AlphaGo prevailed in 2016, and the skeptics said, alright, but these are still just board games with clear rules and win conditions. "True" intelligence is about ambiguity, creativity, and language. Then came the large language models, and the critique shifted again: they are just "stochastic parrots," excellent mimics who remix their training data without any real understanding. They can write a sonnet or a blog post, but they cannot perform multi step, abstract reasoning.
I present an existence proof:
OpenAI just claimed that a model of theirs qualifies for gold in the IMO:
To be clear, this isn't a production-ready model. It's going to be kept internal, because it's clearly unfinished. Looking at its output makes it obvious why that's the case, it's akin to hearing the muttering of a wild-haired maths professor as he's hacking away at a chalkboard. The aesthetics are easily excused, because the sums don't need one.
The more mathematically minded might enjoy going through the actual proofs. This unnamed model (which is not GPT-5) solved 5/6 of the problems correctly, under the same constraints as a human sitting the exam-
As much as AI skeptics and naysayers might wish otherwise, progress hasn't slowed. It certainly hasn't stalled outright. If a "stochastic parrot" is solving the IMO, I'm just going to shut up, and let it multiply on my behalf. If you're worse than a parrot, then have the good grace to feel ashamed about it.
The most potent argument against AI understanding has been its reliance on simple reward signals. In reinforcement learning for games, the reward is obvious: you won, or you lost. But how do you provide a reward signal for a multi page mathematical proof? The space of possible proofs is infinite, and most of them are wrong in subtle ways. Wei notes that their progress required moving beyond "the RL paradigm of clear cut, verifiable rewards."
How did they manage that? Do I look like I know? It's all secret-sauce. The recent breakthroughs in reasoning models like o1 and onwards relied heavily on "RLVR", which stands for reinforcement learning with verifiable reward. At its core, RLVR is a training method that refines AI models by giving them clear, objective feedback on their performance. Unlike Reinforcement Learning from Human Feedback (RLHF), which relies on subjective human preferences to guide the model, RLVR uses an automated "verifier" to tell the model whether its output is demonstrably correct. Presumably, Wei means something different here, instead of simply scaling up RLVR.
It's also important to note that previous SOTA, DeepMind's AlphaGeometry, a specialized system, had previously achieved a silver-medal level performance and was within spitting distance of gold. A significant milestone in its own right, but OpenAI's result comes from a general-purpose reasoning model. GPT-5 won't be as good at maths, either because it's being trained to be more general at the cost of sacrificing narrow capabilities, or because this model is too unwieldy to serve at a profit. I'll bet the farm on it being used to distill more mainstream models, and the most important fact is that it exists at all.
It was actually AlphaProof that was the previous SOTA.
Thanks. Looks like AG came out six months before it.
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