Abstract
Diffusion Language Models (DLMs) have shown strong potential for text generation and are becoming a competitive alternative to autoregressive models. The denoising strategy plays an important role in determining the quality of their outputs. Mainstream denoising strategies include Standard Diffusion and BlockDiffusion. Standard Diffusion performs global denoising without restricting the update range, often finalizing incomplete context and causing premature end-of-sequence predictions. BlockDiffusion updates fixed-size blocks in a preset order, but its rigid structure can break apart coherent semantic units and disrupt reasoning. We present WavefrontDiffusion, a dynamic decoding approach that expands a wavefront of active tokens outward from finalized positions. This adaptive process follows the natural flow of semantic structure while keeping computational cost equal to block-based methods. Across four benchmarks in reasoning and code generation, WavefrontDiffusion achieves state-of-the-art performance while producing outputs with higher semantic fidelity, showing the value of adaptive scheduling for more coherent and efficient generation.
Abstract (translated)
扩散语言模型（DLM）在文本生成方面表现出强大的潜力，并正成为自回归模型的有力竞争者。去噪策略对于决定其输出质量起着重要作用。主流的去噪策略包括标准扩散和BlockDiffusion。标准扩散执行全局去噪，不限制更新范围，经常过早地完成未完整上下文的信息并导致序列提前结束预测。BlockDiffusion以预设顺序更新固定大小的块，但其刚性结构可能会打破连贯的语义单元，并干扰推理过程。 我们提出了WavefrontDiffusion，这是一种动态解码方法，从已经确定的位置向外扩展活跃令牌波前。这种自适应过程遵循自然的语义结构流程，同时保持与基于块的方法相等的计算成本。在四个涉及推理和代码生成的基准测试中，WavefrontDiffusion实现了最先进的性能，并产生了具有更高语义保真的输出结果，展示了自适应调度对于更连贯、更高效的生成的重要性。
URL
https://arxiv.org/abs/2511.19473
