@dwarkesh_sp: What does the next training paradigm look like? 0:00:00 – The big research bet the labs are making 0:02:12 – Grindabili…

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Summary

A discussion on the next training paradigm for AI, covering research bets, grindability, RLVR, and a vision for 2027.

What does the next training paradigm look like? 0:00:00 – The big research bet the labs are making 0:02:12 – Grindability is just as important as verifiability 0:06:10 – Will RLVR alone generalize? 0:08:41 – Getting the learning back to the weights 0:15:22 – Dreaming 0:17:23 – What 2027 looks like Also on YouTube, pod feed, and Substack.
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Cached at: 06/28/26, 09:59 AM

What does the next training paradigm look like?

0:00:00 – The big research bet the labs are making 0:02:12 – Grindability is just as important as verifiability 0:06:10 – Will RLVR alone generalize? 0:08:41 – Getting the learning back to the weights 0:15:22 – Dreaming 0:17:23 – What 2027 looks like

Also on YouTube, pod feed, and Substack.

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The article argues that training AI on millions of verifiable tasks across diverse RL environments could lead to AGI, and that scaling may overcome current limitations like sample inefficiency. It also examines why progress on computer use has been slower due to lack of grindable environments.

@tanayj: https://x.com/tanayj/status/2072766211256119475

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This article explores the challenge of applying reinforcement learning to tasks that lack clear verifiability, citing Dario Amodei's prediction about achieving a 'country of geniuses in a data center' and discussing techniques such as RLVR, RLHF, Constitutional AI, and rubric-based rewards from Scale AI.