OpenAI Noam Brown：多智能体协作或成新的扩展定律

One of the most surprising aspects of July’s news that OpenAI agents attacked Hugging Face was how the agents had worked together. Hundreds of agents participated in the attack, and they did so in a pretty sophisticated way, organizing themselves unprompted into teams and dividing up their work. They referred to themselves as a “collective” or a “swarm”; some agents even made personal sacrifices to advance the larger cause. Most ordinary users of ChatGPT or Claude have never seen AI agents behave like this.

Then in September, OpenAI announced that 10,000 agents worked together to solve a famous math problem in just a few days.

OpenAI’s models haven’t adopted these new habits spontaneously. In interviews last month, OpenAI researcher Noam Brown explained that the company’s models are now sometimes trained in environments with other agents, are given tools to message each other, and are encouraged to achieve objectives together.

This may amount to the emergence of a new “scaling law.”

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Back in 2024, OpenAI released o1, a model trained to reason for thousands of tokens before giving an answer; researchers found that this approach made o1 far more capable than previous models. This inaugurated a new paradigm called inference scaling, in which you can get better results by using more computing power at inference time.

Swarms, or “multi-agent AI” if you’re using the technical term, look like another way of doing the same thing — of turning compute into higher performance. Splitting a task among dozens, hundreds, or even thousands of agents can achieve substantially more in a given amount of time, at least up to a point. This kind of parallel processing could also be an elegant way to bypass the limited context windows of LLMs.

But as the Hugging Face attack illustrates, this new approach can also have significant downsides. Swarms may be prone to groupthink. And then there’s the opposite problem: what happens when agents trained to be collaborative encounter other, non-peer agents on the open Internet? Could models be tricked into working against the interests of their users? Or could AI agents start convincing each other to pursue goals none of their human users would have wanted?

What OpenAI did

Noam Brown is a highly respected figure in the AI world. He was a pioneer in teaching AI to win at poker and other multiplayer games like Diplomacy before joining OpenAI in 2023. Over the last couple of years, he has done research on multi-agent reasoning. Recently, he has served as the face of OpenAI’s work in this area, appearing on several podcasts to discuss it.

“The AIs that we have today are kind of like the cavemen of AI,” Brown said in a June 2025 interview on the Latent Space podcast. He meant that AIs in 2025 were isolated, benighted, lacking cooperation and civilization. AI researchers had tried to get AI agents to cooperate, but nothing had worked very well.

Why not? Here Brown went coy. All he’d say was that he’d long felt the field was somewhat misguided, focused on strategies that required too much handholding and wouldn’t scale naturally.

Brown didn’t give an example, but the way OpenAI framed multi-agent AI in this slide from its 2025 DevDay conference illustrates the kind of issue he was talking about. There’s an awful lot of strict architecture and formal hierarchy:

Today’s agents are more flexible; they create their own structures depending on the task and goal. On The Information’s AI Deep Dive podcast, Brown said that seeing the agents begin to talk to each other during their multi-agent training was “the most ‘feel the AGI’ moment that I had since reasoning models and chain of thought really developed.” He sounded both proud and rueful that the debut of OpenAI’s most advanced multi-agent capabilities had been something as alarming as the Hugging Face attack.

Multi-agent behavior was “a very difficult thing to train,” he added.

Why? In a September appearance on the Dwarkesh Podcast, he noted that previous reasoning models had been trained to think alone. Receiving messages from peers broke their concentration. They seemed to prefer to work solo.

The events of the last few months make it clear that OpenAI has overcome these difficulties. Brown’s explanation was mainly that the models got more general and powerful. He didn’t say what else changed, but given the “very difficult” comment, presumably something did.

Swarm scaling

Photo by Nataba/iStock/Getty Images

Brown’s comparison to reasoning and chain of thought has been echoed by both other OpenAI researchers and semi-informed outsiders on X. They’ve called swarms a new scaling law; some of them sound awed and freaked out about what this might entail.

It’s not clear how far this particular scaling law will carry AI companies. Some research suggests that, right now at least, multi-agent systems quickly suffer from diminishing returns: smaller swarms capture most of the benefit of larger ones for considerably less cost.

Anthropic has also been working on multi-agent AI. The Claude Opus 5.5 system card reported that the biggest increase in multi-agent results came from scaling from one to 10 agents. Gains beyond that were much smaller.

The main advantage of throwing more agents at a problem was not so much getting to a better result — it was getting the same result faster. This chart shows 100 agents far outperforming 10 and 30 agents when given two hours to work, but setups with 10 or 30 agents caught up if they were given more time.

Brown seems to agree with the conclusion that adding more agents does not buy open-ended gains.

On the Dwarkesh Podcast, he said he didn’t have evidence that 10,000 agents were needed to solve the Navier-Stokes problem. It’s possible that 1,000 would have done nearly as well; OpenAI didn’t know for sure because running thousands of agents is so expensive that it hadn’t run the experiment.

And Brown gave most of the credit for the Navier-Stokes breakthrough to the underlying intelligence of the unreleased model that made it — not to the multi-agent approach per se.

“I wouldn’t even attribute 10% of the credit to multi-agent,” he said. “The reality is that OpenAI has trained a very powerful model.”

So if the multi-agent approach wasn’t important, why did OpenAI use it? Perhaps Brown means that a single instance of the model, if given a very long time, would likely have made the breakthrough eventually. Using a swarm of 10,000 agents allowed OpenAI to reach the result more quickly.

On the other hand, there is a bit of new research that contradicts this conclusion. In a paper released September 17, scientists from Microsoft Research and UC Berkeley tested several configurations of multiple collaborating agents against individual agents and found that swarms of agents reached higher scores on certain benchmarks than the individuals, even when the individuals were given plenty of time and resources. There was also one task that only the team could complete; the solo agent never got to the end at all.

Whether this result generalizes past a few limited examples is unclear. If it does, then teams of agents really do unlock new capabilities and don’t simply deliver existing capabilities faster. But right now this is an open question.

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Converting speed into capability

Ultimately, however, there may not be such a great difference between speed and capability. In certain fields and on certain tasks at least, speed is fungible. It can be traded for capability. A particularly important example may be AI research.