@inproceedings{liu-etal-2026-pv,
title = "{PV}-Decoding: Progressive Verification Decoding with Decode Gating for Diffusion Language Models",
author = "Liu, Renxi and
Li, Yiming and
Sun, Shengyin and
Chen, Chen and
Wang, Xian and
Yang, Shuai and
xi, Chen and
Gao, Wenbo and
Li, Xiaoguang and
Zhen, Hui-Ling and
Yu, Xianzhi and
Lin, Weizhe and
Li, Xiaosong and
Wang, Yaoyuan",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.2228/",
pages = "47710--47720",
ISBN = "979-8-89176-462-0",
abstract = "Discrete diffusion language models (dLLMs) offer an attractive alternative to autoregressive generation by updating multiple tokens in parallel, but their inference efficiency is still limited by the cost of safely committing tentative predictions. In particular, speculative decoding for dLLMs is challenging because speculative tokens within the same diffusion block may depend on one another, making aggressive acceptance prone to error propagation and strict verification computationally expensive. We present PV-Decoding, a confidence-driven speculative decoding framework with progressive verification for block diffusion inference. PV-Decoding partitions masked positions into directly decoded tokens, speculative tokens, and unresolved tokens using two confidence thresholds, and then verifies speculative tokens with progressively stronger policies, including single-batch, dual-batch, and multi-batch verification. This design exposes a practical accuracy{--}efficiency trade-off: weaker verification minimizes overhead, whereas stronger verification improves acceptance precision and robustness. Experiments on three diffusion language models across GSM8K, MBPP, and HumanEval show that PV-Decoding reduces sequential model forward passes (MFPs) by 8{\%}{--}37{\%} relative to confidence-based decoding, while keeping the largest observed accuracy drop to 1.3{\%}. On Dream-7B-Instruct/GSM8K, PV-Decoding (1-batch) and PV-Decoding (2-batch) improve throughput by 7.0{\%}{--}13.6{\%} under the same hardware budget, whereas PV-Decoding (multi-batch) is slower under the current runtime. These results suggest that confidence-guided speculation with structured verification is a practical path toward efficient and reliable dLLM inference."
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<abstract>Discrete diffusion language models (dLLMs) offer an attractive alternative to autoregressive generation by updating multiple tokens in parallel, but their inference efficiency is still limited by the cost of safely committing tentative predictions. In particular, speculative decoding for dLLMs is challenging because speculative tokens within the same diffusion block may depend on one another, making aggressive acceptance prone to error propagation and strict verification computationally expensive. We present PV-Decoding, a confidence-driven speculative decoding framework with progressive verification for block diffusion inference. PV-Decoding partitions masked positions into directly decoded tokens, speculative tokens, and unresolved tokens using two confidence thresholds, and then verifies speculative tokens with progressively stronger policies, including single-batch, dual-batch, and multi-batch verification. This design exposes a practical accuracy–efficiency trade-off: weaker verification minimizes overhead, whereas stronger verification improves acceptance precision and robustness. Experiments on three diffusion language models across GSM8K, MBPP, and HumanEval show that PV-Decoding reduces sequential model forward passes (MFPs) by 8%–37% relative to confidence-based decoding, while keeping the largest observed accuracy drop to 1.3%. On Dream-7B-Instruct/GSM8K, PV-Decoding (1-batch) and PV-Decoding (2-batch) improve throughput by 7.0%–13.6% under the same hardware budget, whereas PV-Decoding (multi-batch) is slower under the current runtime. These results suggest that confidence-guided speculation with structured verification is a practical path toward efficient and reliable dLLM inference.</abstract>
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%0 Conference Proceedings
%T PV-Decoding: Progressive Verification Decoding with Decode Gating for Diffusion Language Models
%A Liu, Renxi
%A Li, Yiming
%A Sun, Shengyin
%A Chen, Chen
%A Wang, Xian
%A Yang, Shuai
%A xi, Chen
%A Gao, Wenbo
%A Li, Xiaoguang
%A Zhen, Hui-Ling
%A Yu, Xianzhi
%A Lin, Weizhe
%A Li, Xiaosong
%A Wang, Yaoyuan
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F liu-etal-2026-pv
%X Discrete diffusion language models (dLLMs) offer an attractive alternative to autoregressive generation by updating multiple tokens in parallel, but their inference efficiency is still limited by the cost of safely committing tentative predictions. In particular, speculative decoding for dLLMs is challenging because speculative tokens within the same diffusion block may depend on one another, making aggressive acceptance prone to error propagation and strict verification computationally expensive. We present PV-Decoding, a confidence-driven speculative decoding framework with progressive verification for block diffusion inference. PV-Decoding partitions masked positions into directly decoded tokens, speculative tokens, and unresolved tokens using two confidence thresholds, and then verifies speculative tokens with progressively stronger policies, including single-batch, dual-batch, and multi-batch verification. This design exposes a practical accuracy–efficiency trade-off: weaker verification minimizes overhead, whereas stronger verification improves acceptance precision and robustness. Experiments on three diffusion language models across GSM8K, MBPP, and HumanEval show that PV-Decoding reduces sequential model forward passes (MFPs) by 8%–37% relative to confidence-based decoding, while keeping the largest observed accuracy drop to 1.3%. On Dream-7B-Instruct/GSM8K, PV-Decoding (1-batch) and PV-Decoding (2-batch) improve throughput by 7.0%–13.6% under the same hardware budget, whereas PV-Decoding (multi-batch) is slower under the current runtime. These results suggest that confidence-guided speculation with structured verification is a practical path toward efficient and reliable dLLM inference.
%U https://aclanthology.org/2026.findings-emnlp.2228/
%P 47710-47720
Markdown (Informal)
[PV-Decoding: Progressive Verification Decoding with Decode Gating for Diffusion Language Models](https://aclanthology.org/2026.findings-emnlp.2228/) (Liu et al., Findings 2026)
ACL
- Renxi Liu, Yiming Li, Shengyin Sun, Chen Chen, Xian Wang, Shuai Yang, Chen xi, Wenbo Gao, Xiaoguang Li, Hui-Ling Zhen, Xianzhi Yu, Weizhe Lin, Xiaosong Li, and Yaoyuan Wang. 2026. PV-Decoding: Progressive Verification Decoding with Decode Gating for Diffusion Language Models. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 47710–47720, Budapest, Hungary. Association for Computational Linguistics.