LESS Is More: Mutual-Stability Sampling for Diffusion Language Models
Diffusion large language models (dLLMs) offer a promising alternative to autoregressive decoding by iteratively refining masked sequences, enabling parallel token updates and bidirectional conditioning. Their practical efficiency, however, is limited by sampling procedures that execute a fixed number of reverse denoising steps selected before decoding, spending computation on already-stable positions and sometimes committing unstable ones too early. We present \textsc{LESS}, a training-free, model-agnostic adaptive sampler that treats token commitment as an online stopping problem. \textsc{LESS} implements mutual-stability sampling through a joint stability rule that makes a masked positi...
| Authors | Amr Mohamed, Guokan Shang, Michalis Vazirgiannis |
|---|---|
| Primary category | cs.CL |
| Published | 15 June 2026 |
| Last updated at source | 15 June 2026 |
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