Diffusion Language Models Show Competitive Accuracy but Trade Speed for Parallel Generation
A new paper benchmarks eight diffusion language models on eight tasks—from reasoning to coding—measuring both quality and compute cost. It finds DLMs can rival autoregressive LLMs on certain benchmarks but require careful inference design, exposing a clear trade‑off between parallel generation speed and performance.