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arXiv AI By Aidar Myrzakhan, Zhiqiang Shen

Learning When to Commit: Schedule Distillation for Fast Diffusion Language Models

Read the original on arXiv AI →

The paper introduces dSpark, a schedule distillation framework that learns when to commit tokens in diffusion large language models to accelerate generation. By constructing unmasking schedules that prioritize revealing tokens which make other positions easier to predict, dSpark reduces uncertainty earlier in decoding. Experiments on mathematical reasoning and code generation tasks show significant speedups—10.12× on LLaDA‑8B‑Instruct and 5.12× on Dream‑7B‑Instruct—while preserving superior task performance.

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