动态调整生成顺序，让文本更连贯且高效。
WavefrontDiffusion: Dynamic Decoding Schedule for Improved Reasoning
- 从已确定位置向外扩展生成范围，自适应推进。
- 在4个推理与代码生成任务中达当前最优表现。
- 适合追求生成质量与逻辑连贯性的研究者。
扩散语言模型（DLMs）在文本生成方面展现出强大潜力，正成为自回归模型的有力竞争者。去噪策略对输出质量至关重要。主流方法包括标准扩散与块扩散：标准扩散进行全局去噪，常导致上下文未完成即提前结束；块扩散按预设顺序更新固定大小块，但僵化结构会破坏语义单元完整性。本文提出波前扩散（WavefrontDiffusion），一种动态解码方法，从已确定位置向外扩展活跃词元波前。该自适应过程遵循语义结构自然流动，同时计算开销与块方法相当。在四个推理与代码生成基准测试中，该方法实现最先进性能，且输出具有更高语义保真度，证明自适应调度在提升生成连贯性与效率方面的价值。
原文摘要 · Abstract (English)
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.
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