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MAGA: Multi-Platform Self-Fusion of GUI Agents via Structured Action Distillation· MAGA: 多平台 GUI 代理的自融合

Graphical user interface (GUI) agents based on large language models are increasingly deployed across mobile, web, and desktop environments. However, existing agents are typically domain-specific, limiting the deployment and user experience. This motivates the consolidation of specialized models into a single cross-environment policy. Weight merging directly merges domain-specific experts but can corrupt executable actions under expert disagreement, while on-policy distillation (OPD) avoids conflicting teacher supervision yet still treats all response tokens equally during distillation, ignoring that action tokens are the only interface between the environment and the agent. To address this, We introduce MAGA that re-allocates training signal according to the structured action. Based on th

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通过结构化动作蒸馏实现多平台 GUI 代理的自融合

核心方法
MAGA 通过重新分配训练信号,根据结构化动作进行多平台自融合,避免了直接权重合并和常规策略蒸馏的缺陷
适合谁读
研究者 / 工程师
要解决的问题
现有 GUI 代理模型通常特定于某一领域,限制了其在不同环境中的部署和用户体验
关键实验
未提供
主要贡献
提出了结构化动作蒸馏方法,提高了多平台 GUI 代理的互操作性和鲁棒性
意义与局限
MAGA 有望改善多平台 GUI 代理的部署和用户体验,但可能需要更多实验验证其在实际应用中的表现
领域:cs.AI作者:Hang Yan、Zhangxuan GU、Beitong Zhou
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