@inproceedings{mohamed-etal-2026-fast,
    title = "Fast-Decoding Diffusion Language Models via Progress-Aware Confidence Schedules",
    author = "Mohamed, Amr  and
      Zhang, Yang  and
      Vazirgiannis, Michalis  and
      Shang, Guokan",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.findings-acl.1782/",
    pages = "35793--35807",
    ISBN = "979-8-89176-395-1",
    abstract = "Diffusion large language models (dLLMs) offer a promising alternative to autoregressive models, but their practical utility is severely hampered by slow, iterative sampling. We present *SchED*, a training-free, model-agnostic early-exit algorithm that terminates diffusion decoding using a progress-aware confidence threshold. We evaluate *SchED* across multiple diffusion model families and a diverse set of benchmarks spanning multiple-choice, math, long-form QA, and translation. *SchED* delivers substantial acceleration: on instruction-tuned models, it achieves approximately $4\times$ speedups while retaining baseline performance on average. On base models, *SchED* yields consistent speedup gains with 99.1{--}100{\%} performance retention, with up to $2.34\times$ under more aggressive settings. Under a conservative quality{--}penalized speed metric, *SchED* consistently outperforms prior confidence-based early-exit methods, including on long-form generation where existing approaches tend to break down. An entropy analysis of the model{'}s token predictions reveals that instruction tuning speeds up the decay of predictive entropy. By leveraging inherent confidence stabilization as a signal for computational efficiency, *SchED* provides a robust framework for efficient dLLM inference."
}