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<head>
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<meta charset="utf-8" />
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<meta name="viewport" content="width=device-width,initial-scale=1" />
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<title>NORA-1.5: A Vision-Language-Action Model Post-Trained with World Model- and Action-Based Preference Rewards</title>
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<title>NORA-1.5: A Vision-Language-Action Model Trained using World Model- and Action-Based Preference Rewards</title>
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<!-- Tailwind + Chart.js -->
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<script src="https://cdn.tailwindcss.com"></script>
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<img src="https://declare-lab.github.io/assets/images/nora1.5-new.png" alt="NORA Logo" class="w-40 h-40 object-contain" />
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</div>
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<h1 class="text-6xl font-bold mb-6 bg-clip-text text-transparent bg-gradient-to-r from-blue-400 to-purple-400">NORA-1.5</h1>
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<p class="text-2xl text-slate-300 mb-4">A Vision-Language-Action Model Post-Trained with <br> World Model- and Action-Based Preference Rewards</p>
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<p class="text-2xl text-slate-300 mb-4">A Vision-Language-Action Model Trained using <br> World Model- and Action-Based Preference Rewards</p>
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<p class="text-lg text-slate-400 mb-12 max-w-3xl mx-auto">Shows strong generalizability even with limited fine-tuning data</p>
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<div class="flex flex-wrap justify-center gap-4 mb-16">
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<div class="max-w-4xl mx-auto bg-slate-900/50 p-8 rounded-xl border border-slate-700">
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<h2 class="text-2xl font-bold mb-4">Citation</h2>
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<pre class="bg-slate-950 p-6 rounded-lg overflow-x-auto text-sm text-slate-300">@article{hung2025nora15,
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title={NORA-1.5: A Vision-Language-Action Model Post-Trained with World Model- and Action-Based Preference Rewards},
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title={NORA-1.5: A Vision-Language-Action Model Trained using World Model- and Action-Based Preference Rewards},
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author={Hung, Chia-Yu and Majumder, Navonil and Deng, Haoyuan, Liu Renhang, Yankang Ang, Amir Zadeh, Chuan Li, Dorien Herremans, Ziwei Wang, and Soujanya Poria},
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journal={arXiv preprint},
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year={2025}

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