LightAgent: Mobile Agentic Foundation Models

• URL: http://arxiv.org/abs/2510.22009v1
• Date: Fri, 24 Oct 2025 20:23:12 GMT
• Title: LightAgent: Mobile Agentic Foundation Models
• Authors: Yangqin Jiang, Chao Huang,
• Abstract summary: We propose a mobile agentic foundation model solution that leverages device-cloud collaboration to tap the cost-efficiency of on-device models and the high capability of cloud models.<n>Specifically, LightAgent enhances Qwen2.5-VL-3B via two-stage SFT->GRPO training on synthetic GUI data for strong decision-making.<n>Experiments on the online AndroidLab benchmark and diverse apps show LightAgent matches or nears larger models, with a significant reduction in cloud costs.
• Score: 8.847692192802343
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the advancement of multimodal large language models (MLLMs), building GUI agent systems has become an increasingly promising direction-especially for mobile platforms, given their rich app ecosystems and intuitive touch interactions. Yet mobile GUI agents face a critical dilemma: truly on-device models (4B or smaller) lack sufficient performance, while capable models (starting from 7B) are either too large for mobile deployment or prohibitively costly (e.g., cloud-only closed-source MLLMs). To resolve this, we propose LightAgent, a mobile agentic foundation model solution that leverages device-cloud collaboration to tap the cost-efficiency of on-device models and the high capability of cloud models, while avoiding their drawbacks. Specifically, LightAgent enhances Qwen2.5-VL-3B via two-stage SFT->GRPO training on synthetic GUI data for strong decision-making, integrates an efficient long-reasoning mechanism to utilize historical interactions under tight resources, and defaults to on-device execution-only escalating challenging subtasks to the cloud via real-time complexity assessment. Experiments on the online AndroidLab benchmark and diverse apps show LightAgent matches or nears larger models, with a significant reduction in cloud costs.

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