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Rohan Paul· @rohanpaul_ai · X· · Original publication time AI score54

Duke and Princeton paper finds parallel small agent teams outperform solo agents and one large team at equal computeMachine translation

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The Duke and Princeton paper compares ways to organize the same agent budget. With 100 agents, running 10 teams of 10 in parallel and keeping the best team’s result outperformed both 100 solo agents and one 100-agent team. Team members share a forum, and the approach requires a verifier that can score their work.

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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.

Source: Rohan Paul · x.com

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