Noam Brown is an OpenAI researcher discussing reasoning models, agent teams and results from his employer on the Dwarkesh Podcast.
Loading distillations…
Brown argues that parallel agents can reduce waiting time while increasing total compute. He expects better models to organize themselves more effectively and credits the underlying model with most of the mathematical result, while saying very large teams remain poorly compared with smaller ones.
Open conversationNoam Brown describes agent teams as a way to finish sooner by spending more compute. He expects stronger models to coordinate better, while acknowledging limited measurement of very large teams.
Follow extended conversations with researchers, builders and historians into specific claims and disagreements.
Interview statements belong to their speakers; an appearance does not establish expertise on every topic.
Public episode pages, audio, transcripts and an RSS feed; access varies by post