DigiRL: Training In-The-Wild Device-Control Agents with Autonomous Reinforcement Learning Hao Bai 1,2 Yifei Zhou

While training with static demonstrations has shown some promise, we show that such methods fall short for controlling real GUIs due to their failure to deal with real world stochasticity and non-stationarity not captured in static observational data.