Online Dense Reinforcement Learning with Planning-Grounding Agent for UI Navigation on Live IOS Apps
arXiv:2610.39264v1 Announce Type: new Abstract: 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 to brittle execution and an inability to recover from errors. To address these limitations, we propose a novel online reinforcement learning framework…