Diffusion Language Models (DLMs) present a promising non-sequential paradigm for text generation, distinct from standard autoregressive (AR) approaches. However, current decoding strategies often adopt a reactive stance, underutilizing the global bidirectional context to dictate global trajectories. To address this, we propose Plan-Verify-Fill (PVF), a training-free paradigm that grounds planning via quantitative validation. PVF actively constructs a hierarchical skeleton by prioritizing high-leverage semantic anchors and employs a verification protocol to operationalize pragmatic structural stopping where further deliberation yields diminishing returns. Extensive evaluations on LLaDA-8B-Instruct and Dream-7B-Instruct demonstrate that PVF reduces the Number of Function Evaluations (NFE) by up to 65% compared to confidence-based parallel decoding across benchmark datasets, unlocking superior efficiency without compromising accuracy.
@article{arxiv.2601.12247,
title = {Plan, Verify and Fill: A Structured Parallel Decoding Approach for Diffusion Language Models},
author = {Miao Li and Hanyang Jiang and Sikai Cheng and Hengyu Fu and Yuhang Cai and Baihe Huang and Tinghan Ye and Xuanzhou Chen and Pascal Van Hentenryck},
journal= {arXiv preprint arXiv:2601.12247},
year = {2026}
}