Confidence-based decoding in diffusion models is provably efficient and adapts automatically to data complexity, offering a theoretical foundation for why this practical strategy works well.
This paper proves that confidence-based decoding—a strategy that decides which tokens to generate next in diffusion language models based on prediction confidence—is theoretically efficient.