Łukasz Kaiser, Lead Research Scientist at OpenAI, explains why OpenAI abandoned naming models after underlying pre-training or reinforcement learning runs in favor of capability-based branding.
“Now the naming is by capability, right? GPT-Five is a capable model. 5.1 is a more capable model. Mini is the smaller model that's slightly less capable, but faster and cheaper. And the thinking models are the ones that do more research, right? In that sense, the naming is detached from any technical”
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More from Łukasz Kaiser
AssertionNot checkable as stated
Kaiser: Pre-training between GPT-4 and GPT-5 focused on reducing costs
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AssertionNot checkable as stated
Kaiser: Pre-training scaling laws still hold across OpenAI and Google
“What scaling clause says is that your loss will log linearly decrease with your compute. We totally see that and clearly Google sees that and all other labs.”
Łukasz KaiserNov 26, 2025▶ 4:35What’s Next for AI? OpenAI’s Łukasz Kaiser (Transformer Co-Author)
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Kaiser: Pre-training science is plateauing, but compute scaling still improves loss
“Pre-training, as I said, I think it has reached this upper level of the S-curve in terms of science, but it can scale smoothly. Meaning if you put More compute. You will get better losses if you do things right, which is extremely hard, and that's valuable.”
Łukasz KaiserNov 26, 2025▶ 33:07What’s Next for AI? OpenAI’s Łukasz Kaiser (Transformer Co-Author)
AssertionNot checkable as stated
Kaiser: Model hallucinations are dramatically lower than two years ago
“There was these things called hallucinations. It's still with us to some extent, but dramatically less than two years ago.”
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Kaiser: Test-time compute increases AI capabilities faster than pre-training
“Using more tokens to think increases your capability, and it increases it, given the computation, way faster than pre-training, right?”
Łukasz KaiserNov 26, 2025▶ 46:49What’s Next for AI? OpenAI’s Łukasz Kaiser (Transformer Co-Author)
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