OpenMobile-2 matches proprietary smartphone agents
The Decoder: OpenMobile-2 proves open-source smartphone agents can rival proprietary models
Smartphone agents don't get better by processing more data. They improve by training on as many different apps as possible. The researchers behind OpenMobile-2 built an open training environment with more than 110 applications, including 35 replicas of popular apps. On real Android devices, their model scores highest among open agents, the authors say. The article OpenMobile-2 proves open-source smartphone agents can rival proprietary models appeared first on The Decoder.
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Open-source AI smartphone agents can now match the performance of proprietary models, according to a new project that trains agents in simulated environments containing more than 110 apps. The research shows that training diversity matters more than data volume: when researchers kept training data constant but increased the number of different apps the agent practiced on, success rates on unseen applications improved from 60.7 to 67.5 percent. This development is significant because AI agents that operate apps learn through practice, but the training data they need has been largely controlled by large companies, causing open-source models to lag behind. The released models score highest among open competitors on real device benchmarks and come with full training environments and instructions for reproduction.
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