LEAP: Unlocking dLLM Parallelism via Lookahead Early-Convergence Token Detection

Diffusion Language Models (dLLMs) face scalability limits in parallelism due to overly conservative confidence thresholds that hinder their potential for highly parallel processing. This paper introduces LEAP, a training-free, plug-and-play method that improves dLLM parallelism by detecting early-converging tokens, thereby accelerating decoding.