Efficient Agent Training for Computer Use
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.
682d525994ed89a9b2aec35d
Yanheng He
claimed_verified
2025-05-22T07:17:46.371Z
661b9ac57cfb7bcb3057a578
/avatars/f8afaa8eaad3a1e5963a4feebec3f7ab.svg
Yanheng He
user
henryhe0123
682d525994ed89a9b2aec35e
Jiahe Jin
claimed_verified
2025-05-22T07:17:43.438Z
663f5e959e6f865ec6d4fb62
/avatars/eb0a908562b2c57335ae8bb949220430.svg
Jiahe Jin
user
zizi-0123
682d525994ed89a9b2aec35f
Pengfei Liu
claimed_verified
2025-06-29T10:14:30.563Z
6144a0c4ff1146bbd84d9865
https://cdn-avatars.huggingface.co/v1/production/uploads/1661715958139-6144a0c4ff1146bbd84d9865.png
Pengfei Liu
user
Pengfei
Efficient Agent Training for Computer Use
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.
PC Agent-E framework improves data efficiency and achieves superior performance on human-like computer use tasks through enhanced trajectory synthesis and training.
agent training framework human-annotated trajectories action decisions Claude 3.7 Sonnet WindowsAgentArena-V2 OSWorld generalizability