Consistency Diffusion Language Models Achieve 14x Faster Inference Through KV Caching and Step Reduction

Summary

Consistency Diffusion Language Models (CDLM) represent a breakthrough in language model architecture that addresses key limitations of standard diffusion models. While traditional diffusion language models offer parallel generation capabilities by iteratively refining masked sequences, they suffer from practical deployment issues including inability to use KV caching and requiring too many refinement steps. CDLM introduces a post-training recipe that enables exact block-wise KV caching and trajectory-consistent step reduction, achieving up to 14.5x latency improvements without sacrificing quality. This makes diffusion-based language models more practical for real-world applications by significantly reducing inference time while maintaining the benefits of parallel generation and bidirectional context utilization.