Ilya Sutskever (伊利亞·蘇茨克維)

Did a very different format with Reiner Pope - a blackboard lecture where he walks through how frontier LLMs are trained and served. It’s shocking how much you can deduce about what the labs are doing from a handful of equations, public API prices, and some chalk. It’s a bit technical, but I encourage you to hang in there – it’s really worth it. There are less than a handful of people who understa

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Nathan Lambert and Sebastian Raschka are machine learning researchers, engineers, and educators. Nathan is the post-training lead at the Allen Institute for AI (Ai2) and the author of The RLHF Book. Sebastian Raschka is the author of Build a Large Language Model (From Scratch) and Build a Reasoning Model (From Scratch). Thank you for listening ❤ Check out our sponsors: https://lexfridman.com/spons

Ilya & I discuss SSI’s strategy, the problems with pre-training, how to improve the generalization of AI models, and how to ensure AGI goes well. Watch on YouTube ; listen on Apple Podcasts or Spotify . Sponsors Gemini 3 is the first model I’ve used that can find connections I haven’t anticipated. I recently wrote a blog post on RL’s information efficiency, and Gemini 3 helped me think it all thro

Adam Marblestone is CEO of Convergent Research . He’s had a very interesting past life: he was a research scientist at Google Deepmind on their neuroscience team and has worked on everything from brain-computer interfaces to quantum computing to nanotech and even formal mathematics. In this episode, we discuss how the brain learns so much from so little, what the AI field can learn from neuroscien

What are we scaling? I’m confused why some people have short timelines and at the same time are bullish on the current scale up of reinforcement learning atop LLMs. If we’re actually close to a human-like learner, this whole approach of training on verifiable outcomes is doomed. Currently the labs are trying to bake in a bunch of skills into these models through “mid-training” - there’s an entire

What are we scaling? I’m confused why some people have short timelines and at the same time are bullish on the current scale up of reinforcement learning atop LLMs. If we’re actually close to a human-like learner, this whole approach of training on verifiable outcomes is doomed. Currently the labs are trying to bake in a bunch of skills into these models through “mid-training” - there’s an entire

The mission I originally titled my podcast The Lunar Society. I changed it to Dwarkesh Podcast eventually because people kept thinking it was a crypto podcast (”to the moon!!!”). I named it after The Lunar Society of Birmingham, an informal club that met in the late 18th century. Members included James Watt, Matthew Boulton, Erasmus Darwin, Joseph Priestley, and Josiah Wedgwood. These were the sci

Recently, people have been talking about how it takes way more FLOPs to get a single sample in RL than it does in supervised learning. In pretraining, you get a signal on every single token you train on. In RL, you have to unroll a whole thinking trajectory that’s 10s of 1000s of tokens long in order to get a single reward signal at the end (for example, did the unit test for my code pass/did I ge