Multi-Agent Coordination Lets Us Pour More Compute Into Post-Training

Training models to coordinate across multiple agents gives us a way to productively spend substantially more compute during post-training than current single-agent RL setups. From the recent Dwarkesh Podcast with Noam Brown : Noam Brown But that’s one data point. We don’t know how long it would take a single agent to solve Navier-Stokes , because we haven’t done that experiment yet. Maybe we will, but that’s also only one data point. If we want to do a thorough ablation , the experiments are just too expensive at that scale. So we have to do some kind of methodical science about what happens when you go to 64, 128, 256 or something and get a sense of the behavior. But it’s going to be very hard to push that all the way to 10,000 and know for sure what the benefit was that we actually got from using 10,000 agents versus 1,000. There’s one thing I want to make clear. The effort to solve a Millennium Prize Problem, this was not due to multi-agent. I wouldn’t even attribute 10% of the credit to multi-agent. The reality is that OpenAI has trained a very powerful model. We can get that model to operate over very long horizons. We can get it to think in parallel. But at its core, the reason why we’re able to do this is because we just have a general-purpose, very strong model. Things like multi-agent are flashy and new, and that probably gets disproportionate credit for that reason. But the core reason is this is just a very powerful model. Noam is referring to the GPT-5.6 announce…