Adaptive Multi-Step Lookahead Decoding for Diffusion Language Models
This paper introduces AdaLook, an adaptive lookahead framework for masked diffusion language models. By dynamically determining rollout depth based on candidate-score variance and enabling branch expansion, AdaLook achieves a better accuracy-efficiency trade-off than existing one-step lookahead methods.
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[Submitted on 17 Jul 2026]
Title:Adaptive Multi-Step Lookahead Decoding for Diffusion Language Models
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Abstract:Masked diffusion language models (DLMs) enable parallel text generation by iteratively refining masked tokens, offering a promising alternative to autoregressive decoding. Recent lookahead-based decoding methods improve the accuracy--efficiency trade-off by exploring future decoding states before committing token updates. However, existing approaches mainly rely on shallow one-step lookahead, which optimizes immediate information gain but can be suboptimal for longer-horizon decoding trajectories. Meanwhile, we find that a naive extension for deeper lookahead is also ineffective, as fixed-depth rollout introduces additional computation and cannot adapt to heterogeneous intermediate decoding states. Thus, in this work, we propose AdaLook, an adaptive lookahead framework for DLM decoding. AdaLook dynamically determines whether to continue rollout based on candidate-score variance and further enables branch expansion when intermediate rollout states require additional exploration. This design avoids unnecessary deep rollout while allowing the decoder to re-trigger lookahead from informative intermediate states. Experiments on various benchmarks and models demonstrate that AdaLook achieves a better accuracy--decoding steps trade-off than existing one-step lookahead decoding methods.
Subjects:
Computation and Language (cs.CL); Machine Learning (cs.LG)
Cite as: arXiv:2607.15655 [cs.CL]
(or arXiv:2607.15655v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2607.15655
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Yingqian Cui [view email] [v1] Fri, 17 Jul 2026 06:04:04 UTC (1,318 KB)
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