Efficient Agent Training for Computer Use

Understand

Scaling up high-quality trajectory data has long been a critical bottleneck for developing human-like computer use agents.

• We introduce PC Agent-E, an efficient agent training framework that significantly reduces reliance on large-scale human demonstrations.
• Starting with just 312 human-annotated computer use trajectories, we further augment them by synthesizing diverse alternative action decisions with Claude 3.7 Sonnet.
• Trained on these enriched trajectories, our PC Agent-E model achieved a remarkable 141 relative improvement, and even surpassed the Claude 3.7 Sonnet by 10% in relative terms on WindowsAgentArena-V2, an improved benchmark we also released.