[2505.13909] Efficient Agent Training for Computer Use

Computer Science > Artificial Intelligence arXiv:2505.13909 (cs) [Submitted on 20 May 2025 (v1), last revised 3 Mar 2026 (this version, v2)] Title:Efficient Agent Training for Computer Use Authors:Yanheng He, Jiahe Jin, Pengfei Liu View a PDF of the paper titled Efficient Agent Training for Computer Use, by Yanheng He and 2 other authors View PDF HTML (experimental) Abstract: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. By integrating robust human computer use skills with automated AI data synthesis capabilities, our method not only brought substantial improvements over training on human trajectories alone, but also significantly surpassed direct distillation from Claude 3.7 Sonnet. Code, data and models are available at this https URL Comments: Subjects: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG) Cite as: arXiv:2505...