WavefrontDiffusion: Dynamic Decoding Schedule for Improved Reasoning

TL;DR

WavefrontDiffusion introduces a dynamic decoding schedule that adaptively expands token contexts during generation, improving reasoning and semantic coherence in diffusion language models without increasing computational costs.

Contribution

It proposes WavefrontDiffusion, a novel adaptive decoding strategy that enhances reasoning and semantic coherence in diffusion language models.

Findings

Achieves state-of-the-art results on reasoning and code generation benchmarks.

Produces outputs with higher semantic fidelity.

Maintains computational cost comparable to block-based methods.

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.