Diffusion models approach AR quality and improve inference speed

Diffusion language models are now achieving significant throughput gains and narrowing the gap with autoregressive decoders in inference speed. New Introspective Diffusion Language Models (I-DLM) address prior issues of introspective consistency and inefficient sampling loops, improving both quality and latency.