A new architecture from Nemotron-Labs uses diffusion to generate text, bypassing the slow token-by-token process of autoregressive models. This approach allows for parallelized sampling and faster inference speeds. By treating text as a continuous signal, the researchers aim to eliminate the sequential bottleneck. Practitioners can now explore non-linear text generation for complex tasks.