@inproceedings{zhu-etal-2026-policy,
title = "On-Policy Self-Distillation for Efficient Diffusion Language Models with Early-Stage Calibration",
author = "Zhu, Huaisheng and
Liu, MingYu and
Liu, Junze and
Ge, Zhen and
Wang, Tian and
Gesi, Jiri and
Wang, Dakuo and
Zhang, Weiqi and
Zhang, Houyu and
Guo, Yufan and
Li, Xian and
Yin, Bing and
Sanghavi, Sujay",
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://aclanthology.org/2026.findings-acl.1344/",
pages = "26954--26965",
ISBN = "979-8-89176-395-1",
abstract = "Diffusion Large Language Models (DLLMs) have recently achieved strong performance, e.g., masked diffusion models (MDMs) can surpass autoregressive models (ARMs) in various tasks. However, DLLMs often struggle with inaccurate early-stage predictions due to limited context, which hinders both the model{'}s inference efficiency and the output{'}s overall quality. We propose Calibrated On-Policy Self-Distillation (COPSD) for DLLMs, a simple and efficient method to calibrate early token predictions without requiring demonstration data. COPSD distills an unnormalized target distribution derived from later decoding steps into the original model, enabling more accurate early predictions during inference. Experiments on math, planning, and RLHF tasks show that COPSD improves both effectiveness and efficiency, and further enhances performance when combined with supervised fine-tuning."
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<abstract>Diffusion Large Language Models (DLLMs) have recently achieved strong performance, e.g., masked diffusion models (MDMs) can surpass autoregressive models (ARMs) in various tasks. However, DLLMs often struggle with inaccurate early-stage predictions due to limited context, which hinders both the model’s inference efficiency and the output’s overall quality. We propose Calibrated On-Policy Self-Distillation (COPSD) for DLLMs, a simple and efficient method to calibrate early token predictions without requiring demonstration data. COPSD distills an unnormalized target distribution derived from later decoding steps into the original model, enabling more accurate early predictions during inference. Experiments on math, planning, and RLHF tasks show that COPSD improves both effectiveness and efficiency, and further enhances performance when combined with supervised fine-tuning.</abstract>
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%0 Conference Proceedings
%T On-Policy Self-Distillation for Efficient Diffusion Language Models with Early-Stage Calibration
%A Zhu, Huaisheng
%A Liu, MingYu
%A Liu, Junze
%A Ge, Zhen
%A Wang, Tian
%A Gesi, Jiri
%A Wang, Dakuo
%A Zhang, Weiqi
%A Zhang, Houyu
%A Guo, Yufan
%A Li, Xian
%A Yin, Bing
%A Sanghavi, Sujay
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F zhu-etal-2026-policy
%X Diffusion Large Language Models (DLLMs) have recently achieved strong performance, e.g., masked diffusion models (MDMs) can surpass autoregressive models (ARMs) in various tasks. However, DLLMs often struggle with inaccurate early-stage predictions due to limited context, which hinders both the model’s inference efficiency and the output’s overall quality. We propose Calibrated On-Policy Self-Distillation (COPSD) for DLLMs, a simple and efficient method to calibrate early token predictions without requiring demonstration data. COPSD distills an unnormalized target distribution derived from later decoding steps into the original model, enabling more accurate early predictions during inference. Experiments on math, planning, and RLHF tasks show that COPSD improves both effectiveness and efficiency, and further enhances performance when combined with supervised fine-tuning.
%U https://aclanthology.org/2026.findings-acl.1344/
%P 26954-26965
Markdown (Informal)
[On-Policy Self-Distillation for Efficient Diffusion Language Models with Early-Stage Calibration](https://aclanthology.org/2026.findings-acl.1344/) (Zhu et al., Findings 2026)
ACL
- Huaisheng Zhu, MingYu Liu, Junze Liu, Zhen Ge, Tian Wang, Jiri Gesi, Dakuo Wang, Weiqi Zhang, Houyu Zhang, Yufan Guo, Xian Li, Bing Yin, and Sujay Sanghavi. 2026. On-Policy Self-Distillation for Efficient Diffusion Language Models with Early-Stage Calibration. In Findings of the Association for Computational Linguistics: ACL 2026, pages 26954–26965, San Diego, California, United States. Association for Computational Linguistics.