Online Dense Reinforcement Learning with Planning-Grounding Agent for UI Navigation on Live IOS Apps

Researchers describe an online dense reinforcement learning approach that trains a planning-grounding agent for UI navigation on live iOS apps, targeting the high inference costs that make frontier vision-language models impractical for everyday use. The work argues that smaller models trained via supervised fine-tuning or offline reinforcement learning tend to memorize static trajectories rather than generalize.

While frontier vision-language models demonstrate strong capabilities in user interface UI navigation, their high inference costs prohibit everyday use. Conversely, smaller models trained via supervised fine-tuning SFT or offline reinforcement learning often memorize static trajectories, leading