Hot take: the community keeps asking whether diffusion LLMs can beat AR. That's the wrong question. Diffusion is a sampling method, and it can sit on top of an AR model to make it faster instead of replacing it. Speculative decoding already speeds up AR, but it has two long-standing problems. Training a drafter takes a long time, and you have to build a complete pretraining corpus for it. Even then, token acceptance drops sharply on out-of-domain data. Uno @ssahoo_ offers an elegant way out: it decouples sampling training from capability training. There's no separate drafter. The model itself, plus one LoRA, drafts several tokens in a single forward pass, and the LoRA learns to match the model's own next-token distribution, so there's no drafter corpus to build and no labels to collect. We applied this to the Looped Transformer Nanbeige4.2-3B: 1 hour of LoRA training gives a lossless 2× speedup on agentic task. We haven't optimized anything yet, and we believe the acceleration can go much further. The takeaway: diffusion doesn't need to beat AR. Any model that can score its next token can get a fast diffusion mode in an hour. Page: fengsxy.github.io/LoopUno Lora Adapters: huggingface.co/liddlefish/Lo…