The Unreasonable Effectiveness of Scaling Agents for Computer Use
Computer-use agents (CUAs) hold promise for automating everyday digital
tasks, but their unreliability and high variance hinder their application to
long-horizon, complex tasks. We introduce Behavior Best-of-N (bBoN), a method
that scales over agents by generating multiple rollouts and selecting among
them using behavior narratives that describe the agents' rollouts. It enables
both wide exploration and principled trajectory selection, substantially
improving robustness and success rates. On OSWorld, our bBoN scaling method
establishes a new state of the art (SoTA) at 69.9%, significantly outperforming
prior methods and approaching human-level performance at 72%, with
comprehensive ablations validating key design choices. We further demonstrate
strong generalization results to different operating systems on
WindowsAgentArena and AndroidWorld. Crucially, our results highlight the
unreasonable effectiveness of scaling CUAs, when you do it right: effective
scaling requires structured trajectory understanding and selection, and bBoN
provides a practical framework to achieve this.
68df34efdf49fb0df1e03ca7
Gonzalo Gonzalez-Pumariega
68df34efdf49fb0df1e03ca8
Vincent Tu
68df34efdf49fb0df1e03ca9
Chih-Lun Lee
68df34efdf49fb0df1e03caa
Jiachen Yang
68df34efdf49fb0df1e03cab
Ang Li
68df34efdf49fb0df1e03cac
Xin Eric Wang
claimed_verified
2025-10-03T12:29:57.752Z
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/avatars/05abee0b6317f100923936ca2099e9eb.svg
Xin Eric Wang
user
xw-eric
The Unreasonable Effectiveness of Scaling Agents for Computer Use
Computer-use agents (CUAs) hold promise for automating everyday digital
tasks, but their unreliability and high variance hinder their application to
long-horizon, complex tasks. We introduce Behavior Best-of-N (bBoN), a method
that scales over agents by generating multiple rollouts and selecting among
them using behavior narratives that describe the agents' rollouts. It enables
both wide exploration and principled trajectory selection, substantially
improving robustness and success rates. On OSWorld, our bBoN scaling method
establishes a new state of the art (SoTA) at 69.9%, significantly outperforming
prior methods and approaching human-level performance at 72%, with
comprehensive ablations validating key design choices. We further demonstrate
strong generalization results to different operating systems on
WindowsAgentArena and AndroidWorld. Crucially, our results highlight the
unreasonable effectiveness of scaling CUAs, when you do it right: effective
scaling requires structured trajectory understanding and selection, and bBoN
provides a practical framework to achieve this.
Behavior Best-of-N (bBoN) improves the reliability and success rates of computer-use agents by generating and selecting among multiple rollouts using behavior narratives, achieving state-of-the-art performance on OSWorld and strong generalization to different operating systems.
Behavior Best-of-N bBoN rollouts behavior narratives trajectory selection OSWorld WindowsAgentArena AndroidWorld