Training Open Models for Agentic Phone Use
Authors: name: Zhengyang Tang, name: Xin Lai, name: Pengyuan Lyu, name: Xinyuan Wang, name: Tianyi Bai, name: Chenxin Li, name: Yiduo Guo, name: Huawen Shen, name: Yuxuan Liu, name: Junyi Li, name: Zhengyao Fang, name: Yang Ding, name: Yi Zhang, name: Weinong Wang, name: Xingran Zhou, name: Liang Wu, name: Fei Tang, name: Sunqi Fan, name: Shangpin Peng, name: Zheng Ruan, name: Anran Zhang, name: Benyou Wang, name: Ji-Rong Wen, name: Rui Yan, name: Chengquan Zhang, name: Han Hu
Organization: Tencent Hunyuan

Phones are becoming an important execution surface for general-purpose agents, but training open models for reliable phone use remains difficult because the environment that matters at deployment, real devices running real apps, is slow, stateful, side-effectful, and hard to reset or verify, while scalable mock environments only approximate real behavior. We present PhoneBuddy, a training recipe and open-model line for agentic phone use that combines a real-app environment with a mock-app environment, PhoneWorld, which reconstructs runnable mock apps from real GUI usage structure. PhoneBuddy first builds a shared supervised fine-tuning stage from trajectories collected in both environments, then compares real-app RL against mixed RL across both environments. Across a 150-task human evaluation on real phones spanning apps, mini-apps, and cross-app workflows, task success rate improves from 36.67\% after supervised fine-tuning to 40.67\% after real-app RL and 45.33\% after mixed RL. On AndroidWorld, the same progression rises from 60.3\% to 77.2\% to 83.2\%. These results show that mock-app training is not a replacement for real-app RL, but a complementary source of scalable, resettable, and automatically checked interaction. The gains are strongest on app and mini-app tasks, while long-horizontal cross-app workflows remain an important open challenge.