Diffusion Language Models emerge as a parallel alternative to traditional autoregressive architectures. Researchers evaluate Diffusion Language Models against Autoregressive Language Models to understand their computational trade-offs and arithmetic intensity limits. This study helps developers evaluate alternative generative paradigms that could overcome the inherent sequential bottlenecks of traditional next-token prediction.
Opening Kapyn…