Is RL + LLMs enough for AGI? — Sholto Douglas & Trenton Bricken
Descripción del Episodio
New episode with my good friends Sholto Douglas & Trenton Bricken. Sholto focuses on scaling RL and Trenton researches mechanistic interpretability, both at Anthropic.
We talk through what’s changed in the last year of AI research; the new RL regime and how far it can scale; how to trace a model’s thoughts; and how countries, workers, and students should prepare for AGI.
See you next year for v3. Here’s last year’s episode, btw. Enjoy!
Watch on YouTube; listen on Apple Podcasts or Spotify.
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* Scale is building the infrastructure for safer, smarter AI. Scale’s Data Foundry gives major AI labs access to high-quality data to fuel post-training, while their public leaderboards help assess model capabilities. They also just released Scale Evaluation, a new tool that diagnoses model limitations. If you’re an AI researcher or engineer, learn how Scale can help you push the frontier at scale.com/dwarkesh.
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TIMESTAMPS
(00:00:00) – How far can RL scale?
(00:16:27) – Is continual learning a key bottleneck?
(00:31:59) – Model self-awareness
(00:50:32) – Taste and slop
(01:00:51) – How soon to fully autonomous agents?
(01:15:17) – Neuralese
(01:18:55) – Inference compute will bottleneck AGI
(01:23:01) – DeepSeek algorithmic improvements
(01:37:42) – Why are LLMs ‘baby AGI’ but not AlphaZero?
(01:45:38) – Mech interp
(01:56:15) – How countries should prepare for AGI
(02:10:26) – Automating white collar work
(02:15:35) – Advice for students
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