Uno：扩散增强LLM实现无损加速推理

AI 摘要

Hugging Face 论文页面介绍了一种名为 Uno 的新型扩散增强自回归语言模型，通过扩散蒸馏和 Ψ-Spec 采样器实现无损并行解码，无需草稿模型即可加速推理。Uno 在多个基准上超越现有扩散模型，最高可提升 3 倍速度，并已发布代码和检查点。社区讨论中，作者回应了与 Orthrus 等方法的相似性争议，强调架构差异。

正文节选

Unlocking Lossless Speedups in LLMs via Discrete Diffusion Abstract Diffusion-augmented autoregressive language models use parallel token sampling via distilled diffusion weights and a specialized sampler to accelerate inference without quality loss or draft models. Large Language Models (LLMs) owe much of their success to next-token prediction (NTP), but their autoregressive (AR) structure requires slow, sequential token generation. To overcome this bottleneck, we introduce diffusion-augmented

发布时间：—

抓取时间：2026-09-08 16:20

来源机构：Hugging Face