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arXiv · 2601.20339

Improving Diffusion Language Model Decoding through Joint Search in Generation Order and Token Space

Yangyi Shen·Tianjian Feng·Jiaqi Han·Wen Wang·Tianlang Chen·Chunhua Shen·Jure Leskovec·Stefano Ermon
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Abstract

Diffusion Language Models (DLMs) offer order-agnostic generation that can explore many possible decoding trajectories. However, current decoding methods commit to a single trajectory, limiting exploration in trajectory space. We introduce Order-Token Search to explore this space through jointly searching over generation order and token values. Its core is a likelihood estimator that scores denoising actions, enabling stable pruning and efficient exploration of diverse trajectories. Across mathematical reasoning and coding benchmarks, Order-Token Search consistently outperforms baselines on GSM8K, MATH500, Countdown, and HumanEval (3.1%, 3.8%, 7.9%, and 6.8% absolute over backbone), matching or surpassing diffu-GRPO post-trained d1-LLaDA. Our work establishes joint search as a key component for advancing decoding in DLMs.

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Yangyi Shen, Tianjian Feng, Jiaqi Han, Wen Wang, Tianlang Chen, Chunhua Shen, Jure Leskovec, Stefano Ermon. 2026-01-28. Improving Diffusion Language Model Decoding through Joint Search in Generation Order and Token Space. https://arxiv.org/abs/2601.20339

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