Unlocking Lossless Speedups in LLMs via Discrete Diffusion

Tue, 08 Sep 2026 00:00:00 +0900

Paper

Uno: AR and Diffusion in One Model — Lossless Parallel Generation for LLM Acceleration

TL;DR — LLMs are slow because next-token prediction (NTP) can emit only one token at a time by its very sequential structure. Uno plants AR weights that own quality and lightweight LoRA diffusion weights that own speed into a single architecture; the diffusion path drafts token blocks in parallel, and the AR path verifies them via rejection sampling. With neither a separate draft model nor a lossy AR→diffusion conversion, it achieves lossless acceleration that exactly preserves the AR distribution. In practice it delivers up to 3× speedup over the base AR model and still up to 2× at the largest batch sizes, while beating open d-LLMs (26B DiffusionGemma) and proprietary models (Mercury 2) on agentic, coding, and long-form reasoning benchmarks (source: Abstract).

2609.04010v1 on Jaehun's Blog

Unlocking Lossless Speedups in LLMs via Discrete Diffusion

Uno: AR and Diffusion in One Model — Lossless Parallel Generation for LLM Acceleration