OpenMobile: Building Open Mobile Agents with Task and Trajectory Synthesis

2026-04-16 · Source: Computation and Language · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems, Computer Vision & Pattern Recognition · Depth: Expert, quick

Summary

OpenMobile is an open-source framework designed to synthesize high-quality task instructions and agent trajectories for mobile agents, addressing the closed-data and opaque synthesis methods of current leading models. It features a scalable task synthesis pipeline that builds a global environment memory from exploration to generate diverse, grounded instructions. Additionally, OpenMobile employs a policy-switching strategy for trajectory rollout, alternating between learner and expert models to capture crucial error-recovery data often absent in standard imitation learning. Agents trained using OpenMobile's data achieve competitive results on dynamic mobile agent benchmarks; for instance, fine-tuned Qwen2.5-VL and Qwen3-VL models reached 51.7% and 64.7% success rates on AndroidWorld, significantly outperforming existing open-data methods. The framework also includes transparent analyses to confirm performance gains are due to broad functionality coverage, not benchmark overfitting.

Key takeaway

For research scientists developing mobile agents, OpenMobile offers a critical open-source solution to the prevailing data scarcity and opacity. You should explore integrating its task and trajectory synthesis pipelines to generate diverse, high-quality training data, potentially improving agent performance on benchmarks like AndroidWorld and fostering more transparent development practices.

Key insights

OpenMobile provides an open-source framework for synthesizing mobile agent training data, improving performance and transparency.

Principles

• Global environment memory enhances instruction diversity.
• Policy-switching captures error-recovery data.
• Open data fosters broader research.

Method

OpenMobile synthesizes task instructions via a global environment memory and generates trajectories using a policy-switching strategy that alternates between learner and expert models to gather error-recovery data.

In practice

• Train mobile agents with synthesized data.
• Utilize Qwen2.5-VL or Qwen3-VL for mobile tasks.
• Analyze data overlap to prevent overfitting.

Topics

• OpenMobile Framework
• Mobile Agents
• Task Synthesis Pipeline
• Trajectory Synthesis
• Vision-Language Models

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, Robotics Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Computation and Language.