Unraveling Reinforcement Learning in AI

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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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