Efficient Agent Training for Computer Use
Authors: name: Yanheng He, name: Jiahe Jin, name: Pengfei Liu

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 improved data quality by synthesizing
diverse action decisions with Claude 3.7 Sonnet. Trained on these enriched
trajectories, our PC Agent-E model achieved a remarkable 141% relative
improvement, surpassing the strong Claude 3.7 Sonnet with extended thinking on
WindowsAgentArena-V2, an improved benchmark we also released. Furthermore, PC
Agent-E demonstrates strong generalizability to different operating systems on
OSWorld. Our findings suggest that strong computer use capabilities can be
stimulated from a small amount of high-quality trajectory data.