UltraCUA: A Foundation Model for Computer Use Agents with Hybrid Action
Authors: name: Yuhao Yang, name: Zhen Yang, name: Zi-Yi Dou, name: Anh Nguyen, name: Keen You, name: Omar Attia, name: Andrew Szot, name: Michael Feng, name: Ram Ramrakhya, name: Alexander Toshev, name: Chao Huang, name: Yinfei Yang, name: Zhe Gan
Organization: Apple

Multimodal agents for computer use rely exclusively on primitive actions
(click, type, scroll) that require accurate visual grounding and lengthy
execution chains, leading to cascading failures and performance bottlenecks.
While other agents leverage rich programmatic interfaces (APIs, MCP servers,
tools), computer-use agents (CUAs) remain isolated from these capabilities. We
present UltraCUA, a foundation model that bridges this gap through hybrid
action -- seamlessly integrating GUI primitives with high-level programmatic
tool calls. To achieve this, our approach comprises four key components: (1) an
automated pipeline that scales programmatic tools from software documentation,
open-source repositories, and code generation; (2) a synthetic data engine
producing over 17,000 verifiable tasks spanning real-world computer-use
scenarios; (3) a large-scale high-quality hybrid action trajectory collection
with both low-level GUI actions and high-level programmatic tool calls; and (4)
a two-stage training pipeline combining supervised fine-tuning with online
reinforcement learning, enabling strategic alternation between low-level and
high-level actions. Experiments with our 7B and 32B models demonstrate
substantial improvements over state-of-the-art agents. On OSWorld, UltraCUA
models achieve an average 22% relative improvement over base models, while
being 11% faster in terms of steps. Out-of-domain evaluation on
WindowsAgentArena shows our model reaches 21.7% success rate, outperforming
baselines trained on Windows data. The hybrid action mechanism proves critical,
reducing error propagation while maintaining execution efficiency.