title: Efficient Agent Training for Computer Use
summary: 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.
paper_authors._id: 682d525994ed89a9b2aec35d
paper_authors.name: Yanheng He
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-22T07:17:46.371Z
paper_authors._id: 661b9ac57cfb7bcb3057a578
paper_authors.avatarUrl: /avatars/f8afaa8eaad3a1e5963a4feebec3f7ab.svg
paper_authors.fullname: Yanheng He
paper_authors.type: user
paper_authors.user: henryhe0123
paper_authors._id: 682d525994ed89a9b2aec35e
paper_authors.name: Jiahe Jin
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-22T07:17:43.438Z
paper_authors._id: 663f5e959e6f865ec6d4fb62
paper_authors.avatarUrl: /avatars/eb0a908562b2c57335ae8bb949220430.svg
paper_authors.fullname: Jiahe Jin
paper_authors.type: user
paper_authors.user: zizi-0123
paper_authors._id: 682d525994ed89a9b2aec35f
paper_authors.name: Pengfei Liu
paper_title: Efficient Agent Training for Computer Use
paper_summary: 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.
paper_ai_summary: PC Agent-E framework improves data efficiency and achieves superior performance on human-like computer use tasks through enhanced trajectory synthesis and training.
paper_ai_keywords: agent training framework human-annotated trajectories action decisions Claude 3.7 Sonnet WindowsAgentArena-V2 OSWorld generalizability