Video summary
John Schulman on reasoning, RLHF, and AGI progress
In this Dwarkesh Patel interview, OpenAI cofounder John Schulman discusses reasoning, RLHF-style post-training, and what it may take for models to handle longer, more complex tasks. The conversation covers coding agents, generalization, bottlenecks, and possible paths toward more capable AI systems.
Pre-training vs. post-training
Schulman explains how pre-training builds a broad web-trained model, while post-training narrows it into a helpful chat assistant.
Long-horizon task capability
He discusses how models may move from short chatbot responses to longer, more autonomous coding and planning tasks.
Generalization and robustness
The interview explores sample efficiency, recovery from errors, and how better generalization may help models get unstuck.
AI-friendly interfaces
He also touches on UI design, multimodal use, and why human websites may still work well for AI agents.
Topics
Pre-training and post-training
How pre-training learns from web-scale data and why post-training aims for a more helpful assistant persona.
Long-horizon tasks
Why future models may handle multi-file coding projects and other longer, more autonomous tasks.
Generalization and robustness
The role of generalization, sample efficiency, and recovering from errors when models get stuck.
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Public transcript excerpt
Transcript
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it can also assign probabilities to everything. The base model can effectively take on all of these different personas or generate all different kinds of content. When we do post-training, we're usually targeting a narrower range of behaviors where we want the model to behave like a kind of chat assistant. It's a more specific persona where it's trying to be helpful. It's not trying to imitate a person. It's answering your questions or doing your tasks. We're optimizing on a different objective, which is more about producing outputs that humans will like and find useful, as opposed to just imitating this raw content from the web.
Maybe I should take a step back and ask this. Right now we have these models that are pretty
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Audience comments snapshot
What viewers are saying
Comments praise the depth of the interview and Dwarkesh Patel’s persistent follow-up questions. Several viewers highlight the discussion of pre-training vs. post-training, long-horizon tasks, and the possibility of near-term AGI timelines as especially memorable.
Comment themes
AI training concepts
The conversation is widely appreciated for its clear explanation of how pre-training and post-training differ.
Long-horizon capability
The audience was especially interested in how models may progress toward longer, more coherent task execution.
Thoughtful interview dynamics
Listeners valued the interviewer’s persistent questioning to surface clearer answers.
Audience signals
Strong positive reception
Multiple comments call the episode great and engaging, with appreciation for the interview style.
AGI timeline discussion stood out
Viewers specifically mention the discussion of dangerous AGI potentially emerging within a few years.
Notable takeaways were easy to follow
One comment summarizes key moments, including autonomous coding and long-horizon task ability.
Minor audio feedback
A viewer notes the audio and suggests lowering mic gain for cleaner sound.
Representative public comments
great episode, john schulman was interesting. i appreciated you pressing him on his view that dangerous AGI could emerge within "two or three years", at least with some likelihood where he found this topic worth discussing. i don't have enough info for a strong opinion on that myself, but i've noticed it's almost a...
Great delving there. Thanks guys.
00:30 Pre-training creates a model that can generate content from the web. Post-training targets a narrower range of behaviors like being a chat assistant. 03:44 Models evolving to perform complex coding tasks autonomously 10:29 Improvement in the ability to do long-horizon tasks is key to AI capabilities. 13:52 Mod...
Great interview, appreciate the interviewer challenging and persistent line of good questions and follow-ups to get the best answers
Another great episode. Thanks for such wonderful content.
great interview! if you want cleaner audio try reducing mic gain to avoid clipping ( it can be normalized later to get full volume)
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