Apple study compares diffusion vs autoregressive language models

Apple ML Research finds diffusion language models (DLMs) achieve higher arithmetic intensity via parallel token generation, but fail to scale with longer contexts unlike autoregressive models (ARMs). Block-wise decoding decouples arithmetic intensity from sequence length to improve DLM scaling; ARMs retain superior throughput in batched inference.