Supportive Token Revealing for Fast Diffusion Language Model Decoding 事件

Supportive Token Revealing for Fast Diffusion Language Model Decoding arXiv:2606.04236v1 Announce Type: new Abstract: Discrete diffusion language models can generate text efficiently by updating multiple masked positions in parallel, but this parallelism introduces a quality-latency trade-off. Aggressive decoding may commit mutually dependent tokens too early, while conservative decoding requires many denoising steps. Existing methods address this tension by deciding which tokens are safe to re