Stability-Weighted Decoding for Diffusion Language Models

Diffusion large language models (dLLMs) enable parallel text generation by iteratively denoising a fully masked sequence. However, existing decoding strategies rely on single-step static confidence metrics, ignoring temporal history and often prematurely unmasking unstable tokens. Under idealized ground-truth context revelation, we establish that the expected KL divergence between true conditional distributions equals the information gained about a token and lower-bounds its dependence on the previously masked context. Inspired by this result, we propose Stability-Weighted Decoding (SWD), a training-free, plug-and-play strategy that incorporates temporal stability into token scoring, using model-based temporal KL as an empirical sensitivity signal rather than a dependency guarantee. Experiments on code generation and mathematical reasoning benchmarks demonstrate that SWD improves generation accuracy across representative scoring metrics and exhibits exceptional robustness under aggressive acceleration ratios.