Richard Sutton is the father of reinforcement learning, winner of the 2024 Turing Award, and author of The Bitter Lesson. And he thinks LLMs are a dead end. After interviewing him, my steel man of Richard’s position is this: LLMs aren’t capable of learning on-the-job, so no matter how much we scale, we’ll need *some* new architecture to enable continual learning. And once we have it, we won’t need a special training phase — the agent will just learn on-the-fly, like all humans, and indeed, like all animals. This new paradigm will render our current approach with LLMs obsolete. In our interview, I did my best to represent the view that LLMs might function as the foundation on which experiential learning can happen… Some sparks flew. A big thanks to the Alberta Machine Intelligence Institute for inviting me up to Edmonton and for letting me use their studio and equipment. Enjoy! 𝐄𝐏𝐈𝐒𝐎𝐃𝐄 𝐋𝐈𝐍𝐊𝐒 * Transcript: https://www.dwarkesh.com/p/richard-sutton * Apple Podcasts: https://podcasts.apple.com/us/podcast/richard-sutton-father-of-rl-thinks-llms-are-a-dead-end/id1516093381?i=1000728584744 * Spotify: https://open.spotify.com/episode/3zAXRCFrHPShU4MuuIx4V5?si=c9f4bf24fb4c43e3 𝐒𝐏𝐎𝐍𝐒𝐎𝐑𝐒 * Labelbox makes it possible to train AI agents in hyperrealistic RL environments. With an experienced team of applied researchers and a massive network of subject-matter experts, Labelbox ensures your training reflects important, real-world nuance. Turn your demo projects into working systems at https://labelbox.com/dwarkesh * Gemini Deep Research is designed for thorough exploration of hard topics. For this episode, it helped me trace reinforcement learning from early policy gradients up to current-day methods, combining clear explanations with curated examples. Try it out yourself at https://gemini.google.com/ * Hudson River Trading doesn’t silo their teams. Instead, HRT researchers openly trade ideas and share strategy code in a mono-repo. This means you’re able to learn at incredible speed and your contributions have impact across the entire firm. Find open roles at https://hudsonrivertrading.com/dwarkesh To sponsor a future episode, visit https://dwarkesh.com/advertise 𝐓𝐈𝐌𝐄𝐒𝐓𝐀𝐌𝐏𝐒 00:00:00 – Are LLMs a dead end? 00:13:51 – Do humans do imitation learning? 00:23:57 – The Era of Experience 00:34:25 – Current architectures generalize poorly out of distribution 00:42:17 – Surprises in the AI field 00:47:28 – Will The Bitter Lesson still apply after AGI? 00:54:35 – Succession to AI
Richard Sutton is the father of reinforcement learning, winner of the 2024 Turing Award, and author of The Bitter Lesson. And he thinks LLMs are a dead end. After interviewing him, my steel man of Richard’s position is this: LLMs aren’t capable of learning on-the-job, so no matter how much we scale, we’ll need *some* new architecture to enable continual learning. And once we have it, we won’t need a special training phase — the agent will just learn on-the-fly, like all humans, and indeed, like all animals. This new paradigm will render our current approach with LLMs obsolete. In our interview, I did my best to represent the view that LLMs might function as the foundation on which experiential learning can happen… Some sparks flew. A big thanks to the Alberta Machine Intelligence Institute for inviting me up to Edmonton and for letting me use their studio and equipment. Enjoy! 𝐄𝐏𝐈𝐒𝐎𝐃𝐄 𝐋𝐈𝐍𝐊𝐒 * Transcript: https://www.dwarkesh.com/p/richard-sutton * Apple Podcasts: https://podcasts.apple.com/us/podcast/richard-sutton-father-of-rl-thinks-llms-are-a-dead-end/id1516093381?i=1000728584744 * Spotify: https://open.spotify.com/episode/3zAXRCFrHPShU4MuuIx4V5?si=c9f4bf24fb4c43e3 𝐒𝐏𝐎𝐍𝐒𝐎𝐑𝐒 * Labelbox makes it possible to train AI agents in hyperrealistic RL environments. With an experienced team of applied researchers and a massive network of subject-matter experts, Labelbox ensures your training reflects important, real-world nuance. Turn your demo projects into working systems at https://labelbox.com/dwarkesh * Gemini Deep Research is designed for thorough exploration of hard topics. For this episode, it helped me trace reinforcement learning from early policy gradients up to current-day methods, combining clear explanations with curated examples. Try it out yourself at https://gemini.google.com/ * Hudson River Trading doesn’t silo their teams. Instead, HRT researchers openly trade ideas and share strategy code in a mono-repo. This means you’re able to learn at incredible speed and your contributions have impact across the entire firm. Find open roles at https://hudsonrivertrading.com/dwarkesh To sponsor a future episode, visit https://dwarkesh.com/advertise 𝐓𝐈𝐌𝐄𝐒𝐓𝐀𝐌𝐏𝐒 00:00:00 – Are LLMs a dead end? 00:13:51 – Do humans do imitation learning? 00:23:57 – The Era of Experience 00:34:25 – Current architectures generalize poorly out of distribution 00:42:17 – Surprises in the AI field 00:47:28 – Will The Bitter Lesson still apply after AGI? 00:54:35 – Succession to AI
I have a much better understanding of Sutton’s perspective now. I wanted to reflect on it a bit. Read the transcript here: https://www.dwarkesh.com/p/thoughts-on-sutton TIMESTAMPS 00:00:00 The steelman 00:02:42 TLDR of my current thoughts 00:03:22 Imitation learning is continuous with and complementary to RL 00:08:26 Continual learning 00:10:31 Concluding thoughts
I have a much better understanding of Sutton’s perspective now. I wanted to reflect on it a bit. Read the transcript here: https://www.dwarkesh.com/p/thoughts-on-sutton TIMESTAMPS 00:00:00 The steelman 00:02:42 TLDR of my current thoughts 00:03:22 Imitation learning is continuous with and complementary to RL 00:08:26 Continual learning 00:10:31 Concluding thoughts