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Back to AI researchers debate how close we are to recursive self-improvement, with John Schulman & Beren Millidge.

How Do AI Labs Plan to Train a Drop-in Remote Worker?

Dwarkesh Patel says the bet AI labs are making is to scale RLVR training across millions of diverse environments and hundreds of domains. What emerges is an agent with skills he calls basic, or less than basic: staying persistent, triaging information and context, and eventually doing end-to-end work with other agents. In-context learning keeps getting scaled so the agent can run for a long time. What comes out functions like a drop-in remote worker over a week or a month. The learning happens inside simulations in a data center. The agent learns how to learn there, then gets deployed without learning from real-world deployment.

Charlie O'Neill says that picture fits the models labs deploy to collect revenue, and that it is now hard to separate that effort from direct recursive self-improvement. The revenue models fund the next big training run. The environment sequence he describes is coding first, where data is easiest to turn into practice, then finance and spreadsheets, then PowerPoint, the long tail of the working economy. Other labs, including open-source labs, have followed that bet.

Grokipedia·Dwarkesh Patel

Transcript

AI researchers debate how close we are to recursive self-improvement ·

Dwarkesh Podcast

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  1. Why Should AI Eventually Dominate Research?Dwarkesh Patel says it would be weird if the climb since 2012, powered by huge amounts of compute scaling, never produced an AI that can dominate humans at…
  2. What Did Early OpenAI Think Next-word Training Would Miss?John Schulman remembers an intuition from the early OpenAI days: minimizing log loss was not going to reach intelligence.
  3. What Is the Last Human Job in AI Research?Dwarkesh Patel asked which innovations since 2012, engineering or conceptual, would be the last work humans still do before AI automates AI research.
  4. Why Does Distillation Fight AI Model Consolidation?John Schulman says distillation is the main thing that fights the centralizing force among AI model providers.
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  6. Why Do AI Labs Still Train Models on Finance?John Schulman says a model good enough at learning in context would not need training on finance.
  7. How Does Cursor Ship a New Composer Model?Charlie O'Neill says Cursor's online reinforcement learning for Composer has no groups.
  8. Is AI Taste Just a Longer Context Window?Charlie O'Neill asks whether research taste is just a longer context window.
  9. Why Does Reinforcement Learning Need a Math Curriculum?Charlie O'Neill says a recent paper trained a decent language model only through fifth-grade math and primary-school English, then tried reinforcement…
  10. Why Does Mid-training Do Most of the Work in Reinforcement Learning?Beren Millidge says reinforcement learning has worked better than a naive bit count predicts because strong mid-training often takes a model almost 80% of the…
  11. Can a Little Reinforcement Learning Change an AI Model?Beren Millidge argues that a little reinforcement learning can still change an AI model in a dramatic way.
  12. Does Reinforcement Learning Shrink Writing Diversity?John Schulman says reinforcement learning has cut writing diversity a lot.
  13. When Will AI Work as a Remote Worker?Charlie O'Neill puts a non-browser remote worker, one that can send Slack messages and do the rest of the job, at around a year, and a browser-bound worker at…

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