Diffusion Language Model Decoding

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DPRM: A Plug-in Doob h transform-induced Token-Ordering Module for Diffusion Language Models
Apr 27, 2026Dake Bu, Wei Huang, Andi Han +4Doob H-Transform Process Reward Models
City University of Hong Kong · CFAR and IHPC, A*STAR · RIKEN AIP +4
When to Commit? Towards Variable-Size Self-Contained Blocks for Discrete Diffusion Language Models
Apr 27, 2026Danny Wang, Ruihong Qiu, Zi HuangBlockwise Diffusion Language Models Diffusion Language Model Inference
The University of Queensland
Where to Place the Query? Unveiling and Mitigating Positional Bias in In-Context Learning for Diffusion LLMs via Decoding Dynamics
Apr 26, 2026Zhengheng Li, Panrui Li, Xuyang Liu +1In-Context Learning Diffusion Language Model Decoding
Southeast University
$R^2$ -dLLM: Accelerating Diffusion Large Language Models via Spatio-Temporal Redundancy Reduction
Apr 21, 2026Zhenbang Du, Kejing Xia, Xinrui Zhong +6Diffusion Language Model Inference Diffusion Model Fine-Tuning
Georgia Institute of Technology · NVIDIA
Remask, Don't Replace: Token-to-Mask Refinement in Diffusion Large Language Models
Apr 20, 2026Lin YaoLLM Self-Correction Multi-Token Prediction
School of Computer Science, Shanghai Jiao Tong University, Shanghai, 200240, China · Zhongguancun Academy, Beijing, 100097, China
NI Sampling: Accelerating Discrete Diffusion Sampling by Token Order Optimization
Apr 20, 2026Enshu Liu, Xuefei Ning, Yu Wang +1Few-Step Diffusion Sampling Diffusion Language Model Decoding
Department of EE, Tsinghua University · Microsoft Research
Stability-Weighted Decoding for Diffusion Language Models
Apr 18, 2026Yue Wu, Jian HuangDiffusion Language Model Decoding Diffusion Language Models
Department of Data Science and Artificial Intelligence, The Hong Kong Polytechnic University, Hong Kong, China.
DepCap: Adaptive Block-Wise Parallel Decoding for Efficient Diffusion LM Inference
Apr 17, 2026Xiang Xia, Wuyang Zhang, Jiazheng Liu +2Diffusion Language Model Inference Parallel Decoding
University of Science and Technology of China · Institute of Artificial Intelligence, Hefei Comprehensive National Science Center
BARD: Bridging AutoRegressive and Diffusion Vision-Language Models Via Highly Efficient Progressive Block Merging and Stage-Wise Distillation
Apr 15, 2026Baoyou Chen, Hanchen Xia, Peng Tu +5Vision-Language Models VLM Adaptation
Shanghai Academy of AI for Science · Fudan University · Shanghai Innovation Institute +1
Few-Step Diffusion Language Models via Trajectory Self-Distillation
Feb 12, 2026Tunyu Zhang, Xinxi Zhang, Ligong Han +10Diffusion Model Distillation Few-Step Diffusion Sampling
Rutgers University · Red Hat AI Innovation · MIT-IBM Watson AI Lab
Just on Time: Token-Level Early Stopping for Diffusion Language Models
Feb 11, 2026Zakhar Kohut, Severyn Shykula, Mykola Vysotskyi +7Diffusion Language Model Inference Diffusion Language Model Decoding
SoftServe Inc.
Streaming-dLLM: Accelerating Diffusion LLMs via Suffix Pruning and Dynamic Decoding
Jan 25, 2026Zhongyu Xiao, Zhiwei Hao, Jianyuan Guo +4Adaptive Inference Blockwise Diffusion Language Models
School of information and Electronics, Beijing Institute of Technology, Beijing · Department of Computer Science, City University of Hong Kong, Hong Kong · School of Computer Science, Wuhan University, Wuhan +2
Enabling Approximate Joint Sampling in Diffusion LMs
Sep 25, 2025Parikshit Bansal, Sujay SanghaviMasked Diffusion Models Diffusion Language Model Decoding
UT Austin
Dimple: Discrete Diffusion Multimodal Large Language Model with Parallel Decoding
May 22, 2025Runpeng Yu, Xinyin Ma, Xinchao WangMultimodal Large Language Models LLM Inference Acceleration
National University of Singapore

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DPRM: A Plug-in Doob h transform-induced Token-Ordering Module for Diffusion Language Models

Dake Bu, Wei Huang, Andi Han, Hau-San Wong+3

City University of Hong Kong · CFAR and IHPC, A*STAR · RIKEN AIP+4

Doob H-Transform Process Reward Models Diffusion Language Model Decoding Diffusion Language Models Sequence Modeling

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Diffusion language models generate without a fixed left-to-right order, leaving token ordering as a central algorithmic choice. Existing systems mainly use random masking or confidence-driven ordering, which respectively suffer from train--test mismatch and myopic exploration. We introduce DPRM (Doob -transform Process Reward Model), a plug-in token-ordering module that keeps the host architecture, denoising objective and supervision unchanged, and modifies only the ordering policy. DPRM starts from confidence-driven ordering and gradually shifts to process-reward-guided ordering through online estimates. We characterize the exact DPRM policy as a reward-tilted Gibbs reveal law, prove convergence of its stagewise Soft-BoN approximation, show that the online bucketized controller tracks the exact DPRM score at empirical-Bernstein rates, and establish a sample-complexity advantage under tractable optimization assumptions. Across nine hosts covering language reasoning, test-time scaling, protein, single-cell, molecular, DNA, text-to-image generation, and VQA, DPRM order variants improve several language, DNA, and multimodal settings while also identifying boundary cases where confidence-only ordering or task-specific utilities are preferable. Code is available at: https://github.com/DakeBU/DPRM-DLLM

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