Abstract
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.
Abstract (translated)
扩散语言模型（DLMs）为文本生成提供了一种有前景的非序列范式，与标准自回归（AR）方法不同。然而，当前的解码策略通常采取一种反应式的立场，在利用全局双向上下文来指引全局轨迹方面存在不足。为了应对这一挑战，我们提出了一种名为“计划-验证-填充”（Plan-Verify-Fill, PVF）的新范式，该范式通过定量验证来指导规划，并且无需额外的训练。PVF主动构建了一个分层骨架，优先选择高影响力的语义锚点，并采用一个验证协议以实用性的结构停止标准为依据操作，这种策略在进一步思考不再带来显著收益时可以有效地结束解码过程。 在LLaDA-8B-Instruct和Dream-7B-Instruct数据集上的广泛评估表明，PVF相比于基于置信度的并行解码方法，在基准数据集中最多可将功能评估次数（NFE）减少65%，同时不牺牲准确性，从而实现了效率的显著提升。
URL
https://arxiv.org/abs/2601.12247
