SCALECUA: Scaling Computer Use Agents with Verifiable Task Synthesis and Efficient Online RL

• URL: http://arxiv.org/abs/2607.11185v1
• Date: Mon, 13 Jul 2026 07:32:35 GMT
• Title: SCALECUA: Scaling Computer Use Agents with Verifiable Task Synthesis and Efficient Online RL
• Abstract summary: Computer use agents (CUAs) are emerging as a powerful interface for automating complex digital through visual perception and GUI execution.<n>Online reinforcement learning with verifiable rewards (RLVR) has emerged as a key direction for scaling their capabilities.<n>We introduce ScaleCUA, a unified framework that scales online RL for CUAs via verifiable task synthesis and efficient training.
• Score: 38.05711358650287
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Computer use agents (CUAs) are emerging as a powerful interface for automating complex digital workflows through visual perception and GUI execution. Online reinforcement learning with verifiable rewards (RLVR) has emerged as a key direction for scaling their capabilities. However, this paradigm is bottlenecked by verifiable data scarcity and online RL inefficiency. To break these barriers, we introduce ScaleCUA, a unified framework that scales online RL for CUAs via verifiable task synthesis and efficient training. At the data level, we design VeriGen, an end-to-end framework for generating verifiable RL tasks through iterative docker interactions and a multi-agent feedback loop. Scaled to 100+ concurrent agent workers via a shared docker interaction probe, this pipeline produces 24K+ verifiable tasks and nearly 3K high-quality RL tasks. To maximize sample efficiency, we propose Frontier Sampling, which tracks per-task capability and allocates rollouts to the current learning frontier. On the training side, we further design Visual Context Segmentation, a sliding window over recent visual context that balances rollout and training-engine pressure, yielding a 2.83x training speedup over step-wise decomposition. Together, ScaleCUA achieves 68.7% on OSWorld and 54.0% on ScienceBoard, establishing new state-of-the-art performance among open-source computer use agents. Code, models, and datasets are available at https://github.com/THUDM/SCALE-CUA.

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