Diffusion Language Models (DLMs) offer a promising parallel generation paradigm but suffer from slow inference due to numerous refinement steps and the inability to use standard KV caching. We introduce CDLM (Consistency Diffusion Language Models), a training-based acceleration method that simultaneously tackles both bottlenecks. CDLM integrates consistency modeling to drastically reduce the number of required sampling steps by enabling multi-token finalization. Furthermore, we enforce a block-wise causal attention mask during fine-tuning, making the model fully compatible with KV caching. Experiments show CDLM achieves 3.6x-14.5x lower latency while maintaining competitive accuracy on math and coding tasks. The full training and evaluation code is available at https://github.com/SqueezeAILab/CDLM.
@article{arxiv.2511.19269,
title = {CDLM: Consistency Diffusion Language Models For Faster Sampling},
author = {Minseo Kim and Chenfeng Xu and Coleman Hooper and Harman Singh and Ben Athiwaratkun and Ce Zhang and Kurt Keutzer and Amir Gholami},
journal= {arXiv preprint arXiv:2511.19269},
year = {2026}
}