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arXiv Machine Learning By Fred Zhangzhi Peng, Kaiwen Zheng, Anru R. Zhang

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

Read the original on arXiv Machine Learning →

PlaidQ is a 0.7B continuous diffusion language model designed for code generation. By distilling its iterative refinement trajectory into only a few denoising steps—or even a single step—PlaidQ achieves competitive performance with discrete diffusion models while dramatically reducing inference time. The study demonstrates that continuous diffusion can be effectively compressed, enabling efficient and accurate code generation with minimal computational overhead.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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