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index.html
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<meta charset="utf-8">
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body { font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, sans-serif; line-height: 1.6; color: #333; max-width: 800px; margin: 0 auto; padding: 20px; }
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h3 { border-bottom: 1px solid #eee; padding-bottom: 6px; margin-top: 30px; }
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table { width: 100%; border-collapse: collapse; margin: 15px 0; }
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th, td { text-align: left; padding: 8px 12px; border: 1px solid #e0e0e0; }
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</head>
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<body>
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<p>
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We build cross-platform GUI agents that can operate both desktop and mobile interfaces through a unified training framework.
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</p>
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<h3>
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<p>UI-MOPD introduces a two-stage training pipeline:</p>
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<ul>
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<li><b>Stage 1</b>: Supervised Fine-Tuning (SFT) on platform-specific teacher models</li>
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</ul>
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<p>Our student model (8B) learns from multiple 32B teacher models to achieve strong cross-platform GUI interaction capabilities.</p>
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<h3>
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<table>
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<tr><th>Model</th><th>Size</th><th>Description</th></tr>
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<tr><td><a href="https://huggingface.co/UI-MOPD/Qwen3-VL-32B-Thinking-Desktop-Teacher">Qwen3-VL-32B-Thinking-Desktop-Teacher</a></td><td>33B</td><td>Desktop platform teacher</td></tr>
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<tr><td><a href="https://huggingface.co/UI-MOPD/Qwen3-VL-8B-Thinking-UI-MOPD-Student">Qwen3-VL-8B-Thinking-UI-MOPD-Student</a></td><td>9B</td><td>Final cross-platform student</td></tr>
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</table>
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<h3>
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<table>
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<tr><th>Dataset</th><th>Description</th></tr>
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<tr><td><a href="https://huggingface.co/datasets/UI-MOPD/Uni-GUI-OpenCUA">Uni-GUI-OpenCUA</a></td><td>Post-processed desktop trajectories from OpenCUA (~832 episodes, ~14K steps)</td></tr>
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<tr><td><a href="https://huggingface.co/datasets/UI-MOPD/Uni-GUI-Desktop-1">Uni-GUI-Desktop-1</a></td><td>Large-scale desktop GUI trajectories (~2.7K episodes, ~36K steps)</td></tr>
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</table>
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<h3>
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<ul>
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<li>
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</ul>
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</body>
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<meta charset="utf-8">
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<style>
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body { font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, sans-serif; line-height: 1.6; color: #333; max-width: 800px; margin: 0 auto; padding: 20px; }
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.title { text-align: center; margin-bottom: 30px; }
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.title h1 { margin-bottom: 0; font-size: 1.8em; }
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.title h1 span { color: #FF6900; }
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.title p { margin-top: 4px; color: #555; font-size: 1.05em; }
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h3 { border-bottom: 1px solid #eee; padding-bottom: 6px; margin-top: 30px; }
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table { width: 100%; border-collapse: collapse; margin: 15px 0; }
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th, td { text-align: left; padding: 8px 12px; border: 1px solid #e0e0e0; }
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</head>
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<body>
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<div class="title">
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<h1><span>UI-MOPD</span>: Multi-platform On-Policy Distillation for Continual GUI Agent Learning</h1>
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</div>
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<p>
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We build cross-platform GUI agents that can operate both desktop and mobile interfaces through a unified training framework.
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</p>
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<h3>Research</h3>
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<p>UI-MOPD introduces a two-stage training pipeline:</p>
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<ul>
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<li><b>Stage 1</b>: Supervised Fine-Tuning (SFT) on platform-specific teacher models</li>
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</ul>
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<p>Our student model (8B) learns from multiple 32B teacher models to achieve strong cross-platform GUI interaction capabilities.</p>
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<h3>Models</h3>
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<table>
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<tr><th>Model</th><th>Size</th><th>Description</th></tr>
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<tr><td><a href="https://huggingface.co/UI-MOPD/Qwen3-VL-32B-Thinking-Desktop-Teacher">Qwen3-VL-32B-Thinking-Desktop-Teacher</a></td><td>33B</td><td>Desktop platform teacher</td></tr>
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<tr><td><a href="https://huggingface.co/UI-MOPD/Qwen3-VL-8B-Thinking-UI-MOPD-Student">Qwen3-VL-8B-Thinking-UI-MOPD-Student</a></td><td>9B</td><td>Final cross-platform student</td></tr>
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</table>
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<h3>Datasets</h3>
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<table>
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<tr><th>Dataset</th><th>Description</th></tr>
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<tr><td><a href="https://huggingface.co/datasets/UI-MOPD/Uni-GUI-OpenCUA">Uni-GUI-OpenCUA</a></td><td>Post-processed desktop trajectories from OpenCUA (~832 episodes, ~14K steps)</td></tr>
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<tr><td><a href="https://huggingface.co/datasets/UI-MOPD/Uni-GUI-Desktop-1">Uni-GUI-Desktop-1</a></td><td>Large-scale desktop GUI trajectories (~2.7K episodes, ~36K steps)</td></tr>
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</table>
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<h3>Links</h3>
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<ul>
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<li><a href="https://elispctre.github.io/UI-MOPD">Project Page</a></li>
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</ul>
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</body>
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