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Distilled Continuous Diffusion Language Models Can Write Code in Few Steps---or One

Peng, Fred Zhangzhi et al.
arXiv (All) · Papers
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machine-learning
Language generation is almost universally treated as a sequential process: autoregressive models emit one token at a time, while diffusion language models replace token-level seriality with a long trajectory of iterative refinement. In this work, we introduce PlaidQ, a 0.7B continuous diffusion lang
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