Abstract: Discrete diffusions models have been demonstrated to be surprisingly strong language models.In this work, we show that discrete diffusion language models can be further improved by adapting methods from continuous-state diffusion models.We establish a core property of uniform state diffusion: it stems from an underlying Gaussian diffusion process.This property allows us to improve both training by utilizing a curriculum learning strategy that reduces training variance and leads to $\mathbf{2\times}$ faster convergence, as well as sampling by adapting efficient distillation methods from continuous-state diffusion models.As a result, models surpass an autoregressive model’s zero-shot perplexity on 3 out of 7 benchmarks and we manage to reduce the sampling steps by **two orders of magnitude** while preserving sample quality.