✨From Next-Token to Next-Block: A Principled Adaptation Path for Diffusion LLMs
📝 Summary:
This paper introduces a principled method to adapt autoregressive LLMs into block-wise diffusion models, enabling efficient parallel generation. This adaptation retains pretrained knowledge, achieving state-of-the-art performance for 7B diffusion LLMs, and avoids expensive training from scratch.
🔹 Publication Date: Published on Dec 7
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.06776
• PDF: https://arxiv.org/pdf/2512.06776
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For more data science resources:
✓ https://t.me/DataScienceT
#LLM #DiffusionModels #AI #ParallelGeneration #MachineLearning
📝 Summary:
This paper introduces a principled method to adapt autoregressive LLMs into block-wise diffusion models, enabling efficient parallel generation. This adaptation retains pretrained knowledge, achieving state-of-the-art performance for 7B diffusion LLMs, and avoids expensive training from scratch.
🔹 Publication Date: Published on Dec 7
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.06776
• PDF: https://arxiv.org/pdf/2512.06776
==================================
For more data science resources:
✓ https://t.me/DataScienceT
#LLM #DiffusionModels #AI #ParallelGeneration #MachineLearning