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2026-03-01 10:43:19
raw text
DigiRL DigiRL: Training In-The-Wild Device-Control Agents with Autonomous Reinforcement Learning Hao Bai 1,2* , Yifei Zhou 1* , Mert Cemri 1 , Jiayi Pan 1 , Alane Suhr 1 , Sergey Levine 1 , Aviral Kumar 3 1 UC Berkeley, 2 UIUC, 3 Google DeepMind *Equal contribution, alphabetic order; work done at UC Berkeley 📝 Paper 💻 Code 📂 Data Demo Speed: Fast Slow Success rate and corresponding trajectories. A green final screen indicates a successful trajectory; a red final screen indicates a failed trajectory. Abstract Training corpuses for vision language models typically lack sufficient amounts of decision-centric data. This renders off-the-shelf VLMs sub-optimal for decision-making tasks such as in-the-wild device control through graphical user interfaces ...
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