Uno shows a simple way to speed up existing LLMs without changing their output distribution: keep the original model in charge, and use diffusion only to draft multiple tokens in parallel.

Keep the autoregressive model for quality and add diffusion for parallel drafting

On Qwen3-8B, Uno delivered 2.5X higher per-request throughput and 1.6X higher system throughput at the largest tested batch size.

Normal autoregressive LLMs generate 1 token at a time, making long responses, agents, and RL rollouts slow.

Uno adds lightweight diffusion adapters that propose several future tokens at once, while the original model verifies them.

That avoids a separate draft model and preserves the base model’s sampling behavior.

It also sped up end-to-end RL training by up to 40% in the reported runs.