Rohan Paul

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.