Just on Time: Token-Level Early Stopping for Diffusion Language Models
JoT is a training-free method for early stopping diffusion language models by deciding when to finalize individual tokens. It monitors prediction confidence at each position and uses spatially-adaptive thresholds, lowering them near already resolved context. This allows different parts of the model to finish in parallel, significantly speeding up generation without sacrificing quality.