I just heard that OpenAI announced their new experimental reasoning model has achieved gold medal level performance on International Mathematical Olympiad problems. This sounds pretty impressive if true. Has anyone looked into the details of how well it actually performed? I’m curious about what kinds of math problems it can solve and whether this represents a real breakthrough in AI reasoning capabilities. Are there any technical papers or detailed analysis available about this achievement? I’d love to understand more about the methodology they used to test the model and how it compares to previous AI systems that have attempted similar mathematical reasoning tasks.
Yeah, they’re talking about o1, which crushed those math reasoning tests. What’s cool isn’t just the gold medal scores - it’s how the thing actually thinks through problems. Unlike other models that just spit out answers, this one takes time to reason through stuff like actual mathematicians do when working on proofs. It’s really good at multi-step logic and stays consistent through long mathematical arguments. But I’d be careful about that IMO comparison - testing conditions and which problems they picked can mess with results big time. The real win here is the reasoning process, not just memorizing patterns from training data.
From what I can tell, the breakthrough is that o1 doesn’t lock onto answers right away like other models do. It backtracks and tries different approaches when it gets stuck - basically how mathematicians tackle hard proofs. What bugs me is OpenAI hasn’t published proper technical papers yet, so we’re stuck with their own evaluations. I’ve seen people saying it still can’t handle geometry problems that need visual thinking, even though it crushes algebra and number theory. The big question is whether this reasoning works outside math or if it’s just really good pattern matching for mathematical problems.
the hype feels overblown. yeah, o1 crushed those problems, but imo questions don’t really test general reasoning - they’re more like advanced pattern matching. i’d love to see how it handles completely new math problems that definitely weren’t in the training data.
I’ve been tracking this closely and everyone’s missing the computational cost issue. o1 takes way more inference time than older models - we’re talking minutes per problem instead of seconds. Makes sense with their reasoning approach, but how’s this gonna scale? The IMO results are cool, but I want to see if it can tackle actual unsolved math problems or if it just maxes out at competition stuff. Their methodology gave the model tons of time to work through problems step by step, which is totally different from how we usually benchmark AI.
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