NVIDIA has released Nemotron-Labs-TwoTower, a diffusion language model built on a pretrained autoregressive backbone. It ships as open weights under the NVIDIA Nemotron Open Model License. The release targets a throughput bottleneck in text generation.
Autoregressive (AR) models decode one token at a time. That serial process caps generation throughput. Discrete diffusion language models take another route. They generate tokens in parallel and refine them iteratively.
Most diffusion language models use one network for two jobs. It represents clean tokens and denoises corrupted ones at every step. TwoTower separates these jobs into two towers. It keeps 98.7% of the AR baseline’s aggregate benchmark quality. It also reports 2.42× higher wall-clock generation throughput.
TL;DR
TwoTower splits diffusion into a frozen AR context tower and a trained denoiser tower.
