NI Sampling: Accelerating Discrete Diffusion Sampling by Token Order Optimization

ArXi:2604.18471v1 Announce Type: new Discrete diffusion language models (dLLMs) have recently emerged as a promising alternative to traditional autoregressive approaches, offering the flexibility to generate tokens in arbitrary orders and the potential of parallel decoding. However, existing heuristic sampling strategies remain inefficient: they choose only a small part of tokens to sample at each step, leaving substantial room for improvement. In this work, we study the problem of token sampling order optimization and nstrate its significant potential for acceleration.