@inproceedings{wei-etal-2026-efficient,
title = "Efficient Token-Level Policy Optimization for Masked Diffusion Models via Velocity Alignment",
author = "Wei, Chenxing and
Kang, Jiazhen and
Wang, Hong and
Zhang, Jianqing and
Jiang, Hao and
Xuxiaolong and
Sun, Ningyuan and
He, Ying and
Yu, Fei and
Shu, Yao and
Jiang, Bo",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.1910/",
pages = "43516--43536",
ISBN = "979-8-89176-459-0",
abstract = "Reinforcement Learning with Verifiable Rewards (RLVR) successfully aligns autoregressive models in rigorous domains like mathematical reasoning and code generation. However, applying RLVR to Diffusion Large Language Models (dLLMs) is hindered by intractable sequence-level likelihoods, forcing existing methods to rely on unstable, high-variance approximations. We propose Sequence Likelihood-Free Policy Optimization (LFPO), an efficient framework that circumvents this bottleneck by formulating policy alignment as discrete velocity alignment. Inspired by continuous flow matching, LFPO eschews trajectory-level estimation and directly optimizes denoising logits via contrastive token-level updates, providing a stable, low-variance surrogate. Furthermore, predicting terminal solutions from intermediate states implicitly straightens the generation trajectory, enabling high-quality sampling with significantly fewer denoising iterations. Extensive experiments on reasoning and coding benchmarks demonstrate that LFPO matches or exceeds the accuracy of likelihood-based baselines while substantially accelerating inference."
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%0 Conference Proceedings
%T Efficient Token-Level Policy Optimization for Masked Diffusion Models via Velocity Alignment
%A Wei, Chenxing
%A Kang, Jiazhen
%A Wang, Hong
%A Zhang, Jianqing
%A Jiang, Hao
%A Sun, Ningyuan
%A He, Ying
%A Yu, Fei
%A Shu, Yao
%A Jiang, Bo
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%A Xuxiaolong
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F wei-etal-2026-efficient
%X Reinforcement Learning with Verifiable Rewards (RLVR) successfully aligns autoregressive models in rigorous domains like mathematical reasoning and code generation. However, applying RLVR to Diffusion Large Language Models (dLLMs) is hindered by intractable sequence-level likelihoods, forcing existing methods to rely on unstable, high-variance approximations. We propose Sequence Likelihood-Free Policy Optimization (LFPO), an efficient framework that circumvents this bottleneck by formulating policy alignment as discrete velocity alignment. Inspired by continuous flow matching, LFPO eschews trajectory-level estimation and directly optimizes denoising logits via contrastive token-level updates, providing a stable, low-variance surrogate. Furthermore, predicting terminal solutions from intermediate states implicitly straightens the generation trajectory, enabling high-quality sampling with significantly fewer denoising iterations. Extensive experiments on reasoning and coding benchmarks demonstrate that LFPO matches or exceeds the accuracy of likelihood-based baselines while substantially accelerating inference.
%U https://aclanthology.org/2026.emnlp-main.1910/
%P 43516-43536
Markdown (Informal)
[Efficient Token-Level Policy Optimization for Masked Diffusion Models via Velocity Alignment](https://aclanthology.org/2026.emnlp-main.1910/) (Wei et al., EMNLP 2026)
ACL
- Chenxing Wei, Jiazhen Kang, Hong Wang, Jianqing Zhang, Hao Jiang, Xuxiaolong, Ningyuan Sun, Ying He, Fei Yu, Yao Shu, and Bo Jiang. 2026. Efficient Token-Level Policy Optimization for Masked Diffusion Models via Velocity Alignment. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 43516–43536, Budapest, Hungary. Association for Computational Linguistics.