CDLM: Consistency Diffusion Language Models For Faster Sampling

• URL: http://arxiv.org/abs/2511.19269v1
• Date: Mon, 24 Nov 2025 16:21:25 GMT
• Title: CDLM: Consistency Diffusion Language Models For Faster Sampling
• Authors: Minseo Kim, Chenfeng Xu, Coleman Hooper, Harman Singh, Ben Athiwaratkun, Ce Zhang, Kurt Keutzer, Amir Gholami,
• Abstract summary: Diffusion Language Models (DLMs) offer a promising parallel generation paradigm but suffer from slow inference.<n>We introduce CDLM, a training-based acceleration method that simultaneously tackles both bottlenecks.<n>Experiments show CDLM achieves 3.6x-14.5x lower latency while maintaining competitive accuracy on math and coding tasks.
• Score: 54.886467592798
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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