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Efficient Agent Training for Computer Use

arXiv – CS AI|Yanheng He, Jiahe Jin, Pengfei Liu|March 4, 2026 at 05:00 AM|1 views

🤖AI Summary

Researchers introduced PC Agent-E, an efficient AI agent training framework that achieves human-like computer use with minimal human demonstration data. Starting with just 312 human-annotated trajectories and augmenting them with Claude 3.7 Sonnet synthesis, the model achieved 141% relative improvement and outperformed Claude 3.7 Sonnet by 10% on WindowsAgentArena-V2 benchmark.

Key Takeaways

→PC Agent-E framework dramatically reduces the need for large-scale human demonstration data in training computer use agents.
→The model achieved 141% relative improvement using only 312 human-annotated trajectories augmented with AI synthesis.
→PC Agent-E outperformed Claude 3.7 Sonnet by 10% on the WindowsAgentArena-V2 benchmark.
→The approach combines human computer use skills with automated AI data synthesis for superior results.
→Code, data, and models are publicly available, potentially accelerating AI agent development across the industry.

#ai-agents #machine-learning #computer-automation #training-efficiency #benchmarks #open-source #claude #data-synthesis #artificial-intelligence

Read Original →via arXiv – CS AI

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