Łukasz Kaiser
21 statements across 1 episodes · 9 bullish · 2 bearish · 1 people on the record · first statement Nov 26, 2025 by Łukasz Kaiser · said 6 times in 2 episodes since 2025 · across every show →
On the record as a speaker too: Łukasz Kaiser's record, appearances and statements → this page counts the times other people say the name.
Mentions by year
brought up most by Mostafa Dehghani (3), Aidan Gomez (3)
tap a year for its mentions
2026 3 mentions in 1 episode
2025 3 mentions in 1 episode
Everything said about Łukasz Kaiser, oldest first
Nov 26, 2025 neutral
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.”
Nov 26, 2025 positive
Kaiser: Math reasoning in AI models transfers to generic web searching
“If you learn to think for math, you can, you will sometimes do some, you know, some strategies are the transfer very much like look up on the web and see what they say and use that information. So some of these things are very generic and they start to transfe…”
Nov 26, 2025
Nov 26, 2025 neutral
Nov 26, 2025 bullish
Nov 26, 2025 neutral
Nov 26, 2025 positive
Kaiser: Reinforcement learning causes AI models to self-correct mistakes
“Even for math and coding, you start seeing that the models start correcting their own mistakes, right? Earlier, if the model made a mistake, it generally just tell you what it did and insist that the mistake was right or something like that. With the thinking,…”
Nov 26, 2025 bullish
Kaiser: Reinforcement learning reasoning works better on larger pre-trained models
“Pre-training has always worked. And the beautiful thing is it even stacks with RL. So if you run this thinking RL process on top of a better model, it works even better. Than if you run it on top of a smaller model.”
Nov 26, 2025
Nov 26, 2025 bullish
Kaiser: Reasoning yields far greater AI capability gains per dollar than pre-training
“With the new paradigm of reasoning, you can get much more gains for the same amount of money because it's on this like lower and like, there are just discoveries to be made and these discoveries unlock insane capabilities.”
Nov 26, 2025
Łukasz Kaiser: Reasoning models require verifiable data, excelling in math and coding
“So currently, and current for at least the Most basic ways we use it currently, it needs to be fairly verifiable. So there is an, is your answer correct or not? You prepare data for that. You can do that in mathematics, coding very well. You can do this in sci…”
Nov 26, 2025 positive
Nov 26, 2025
Kaiser: ChatGPT uses a secondary model to summarize raw reasoning steps
“So in the current chat GPT, you will see a summary of the chain of thought on the side. So there is another model that takes the full chain of thought and shows you a summary because the full ones are usually not very nice to read.”
Nov 26, 2025 negative
Łukasz Kaiser: AI models still struggle with multimodal and sequential reasoning
“The models are just, they're starting, like you see the first example they manage, so they've clearly made some progress, but they have not yet learned to do good reasoning in multimodal domains, and they have not yet learned to use one reasoning in context to…”
Nov 26, 2025 positive
Nov 26, 2025 neutral
Nov 26, 2025 positive
Nov 26, 2025 negative
Nov 26, 2025 positive
Kaiser: RL is a major component in post-training tone steering
“I don't work on post-training and it certainly has a lot of quirks, but I think the main part is, is indeed RL where you say, okay, is this response cynical? Is this response like that? And you say, okay, if you were told to be cynical, this is how you should …”
Nov 26, 2025
Kaiser: Interpretability of large AI models faces fundamental complexity limits
“So the understanding of what the models are doing on a higher level has progressed a lot, but then it's still an understanding of what smaller models do, not the biggest ones. But it's not so much that these patterns don't apply to bigger models. They do. It's…”
Nov 26, 2025 neutral