Developing autonomous agents that effectively interact with Graphic User
Interfaces (GUIs) remains a challenging open problem, especially for small
on-device models. In this paper, we present Ferret-UI Lite, a compact,
end-to-end GUI agent that operates across diverse platforms, including mobile,
web, and desktop. Utilizing techniques optimized for developing small models,
we build our 3B Ferret-UI Lite agent through curating a diverse GUI data
mixture from real and synthetic sources, strengthening inference-time
performance through chain-of-thought reasoning and visual tool-use, and
reinforcement learning with designed rewards. Ferret-UI Lite achieves
competitive performance with other small-scale GUI agents. In GUI grounding,
Ferret-UI Lite attains scores of $91.6\%$, $53.3\%$, and $61.2\%$ on the
ScreenSpot-V2, ScreenSpot-Pro, and OSWorld-G benchmarks, respectively. For GUI
navigation, Ferret-UI Lite achieves success rates of $28.0\%$ on AndroidWorld
and $19.8\%$ on OSWorld. We share our methods and lessons learned from
developing compact, on-device GUI agents.