The Path Matters: Learning a Token-Commitment Policy for Diffusion Language Models 事件

The Path Matters: Learning a Token-Commitment Policy for Diffusion Language Models arXiv:2605.24697v1 Announce Type: new Abstract: Diffusion large language models promise faster generation by refining many token positions in parallel, but this parallelism introduces a hidden control problem: which proposed tokens should be transferred into the partially decoded sequence at each step? We refer to this decision as token commitment. Existing frozen-generator decoders largely rely on hand-designed