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arXiv Machine Learning By Tianqi Chen, Shujian Zhang, Mingyuan Zhou

DLM-One: Diffusion Language Models for One-Step Sequence Generation

Read the original on arXiv Machine Learning →

The paper introduces DLM-One, a score‑distillation framework that enables one‑step sequence generation with continuous diffusion language models (DLMs). By aligning a student model’s outputs with a pretrained teacher DLM’s score function in the forward‑diffused noisy space, DLM-One removes the need for iterative refinement. Experiments across various DLM architectures show up to ~2000× speedup in sampling steps and ~500× in wall‑clock time while retaining competitive performance, and the authors propose an adversarially‑regularized two‑stage training scheme to mitigate student degeneration.

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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