CUA-Master: Scaling Computer-use Agents through Open-World Recursive Improvements

CUA-Master introduces a nested double-loop recursive improvement framework to scale computer-use agents. The outer flywheel continuously integrates open-world data and model weaknesses to guide data construction and optimization, while the inner flywheel mines instructional videos for seed tasks and propagates them into reusable primitives for diverse task synthesis. The system also employs a feedback-guided snapshot-rollback retry mechanism for reliable trajectory collection, achieving competitive performance on multiple benchmarks and demonstrating continuous gains across iterations.

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• agents
• benchmarks