Paper page - Training Open Models for Agentic Phone Use

Source: https://huggingface.co/papers/2606.23049 Authors:

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Abstract

PhoneBuddy combines real and mock app environments to improve training of open models for phone use, demonstrating enhanced task success rates through mixed reinforcement learning approaches.

Phones are becoming an important execution surface for general-purpose agents, but trainingopen modelsfor 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 areal-app environmentwith amock-app environment,PhoneWorld, which reconstructs runnable mock apps from real GUI usage structure. PhoneBuddy first builds a sharedsupervised fine-tuningstage 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 rateimproves from 36.67\% aftersupervised fine-tuningto 40.67\% after real-app RL and 45.33\% after mixed RL. OnAndroidWorld, 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.

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