Duke 和 Princeton 的论文发现，在相同算力下，许多小的智能体团队并行运行的表现同时优于 solo-agent best-of-N 和单个大团队：同样 100 个智能体的预算，10 个共享论坛的 10 智能体小团队并行并保留最佳团队结果，胜过 100 个 solo 智能体和 1 个 100 智能体团队。作者建议在验证器可给智能体工作打分时，把预算拆成多个共享笔记的小团队并保留最佳答案。
New Duke and Princeton paper finds that, at equal compute, many small agent teams in parallel beat both solo-agent best-of-N and one giant team.
With the same budget of 100 AI agents, running 10 small teams of 10 agents in parallel and keeping the best team’s result beat both 100 solo agents and one 100-agent team.
With a verifier, teams usually run many solo agents and keep the best, but gains flatten fast. One large team does better at first, then stops improving as it grows.
They split the agent budget into teams of 10 that share a forum, then keep the best team's result. Teamwork raises each attempt's average, and several teams still give many tries.
If a verifier can score your agents' work, split the budget into several small teams that share notes, and keep the best team's answer.
来源：Rohan Paul · x.com