Parallel Speculative Decoding

Latest papers

37 papers7 papers in the last four weeks, down 22% on the four weeks before. 0.0% of all new papers.

PSD: Pushing the Pareto Frontier of Diffusion LLMs via Parallel Speculative Decoding
May 15, 2026Shengyin Sun, Yiming Li, Renxi Liu +7Parallel Speculative Decoding Masked Diffusion Models
Department of Computer Science City University of Hong Kong · Huawei Technologies · City University of Hong Kong
SPECTRE: Hybrid Ordinary-Parallel Speculative Serving for Resource-Efficient LLM Inference
May 4, 2026Jincheng Xie, Yawen Ling, Qi Xiao +4Parallel Speculative Decoding LLM Serving
1Tsinghua University · 2AI Infra Team at JDT · 3JD iCity, JD Technology, JD Intelligent Cities Research
Accelerating Speculative Decoding with Block Diffusion Draft Trees
Apr 14, 2026Liran Ringel, Yaniv RomanoSpeculative Decoding Parallel Speculative Decoding
Department of Computer Science, Technion – Israel Institute of Technology. · Department of Electrical and Computer Engineering, Technion – Israel Institute of Technology.
DIVERSED: Relaxed Speculative Decoding via Dynamic Ensemble Verification
Apr 8, 2026Ziyi Wang, Siva Rajesh Kasa, Ankith M S +6Speculative Decoding LLM Inference Acceleration
Purdue University · Amazon Inc. · University of Illinois Urbana-Champaign +1
Speculative Speculative Decoding
Mar 3, 2026Tanishq Kumar, Tri Dao, Avner MaySpeculative Decoding LLM Inference Acceleration
Department of Computer Science, Stanford University · Department of Computer Science, Princeton University · Together AI
MineDraft: A Framework for Batch Parallel Speculative Decoding
Feb 24, 2026Zhenwei Tang, Arun Verma, Zijian Zhou +4Speculative Decoding LLM Inference Acceleration
College of Computing and Data Science, Nanyang Technological University, Republic of Singapore · Singapore-MIT Alliance for Research and Technology Centre · Department of Computer Science, National University of Singapore, Republic of Singapore +1
Towards Optimal Multi-draft Speculative Decoding
Feb 26, 2025Zhengmian Hu, Tong Zheng, Vignesh Viswanathan +5Speculative Decoding LLM Inference Acceleration
Department of Computer Science, University of Maryland, College Park, MD, USA · Adobe Research, San Jose, CA, USA · Manning College of Information & Computer Sciences, University of Massachusetts Amherst, MA, USA +1

May 15, 2026J/K move · Enter open · S save

PSD: Pushing the Pareto Frontier of Diffusion LLMs via Parallel Speculative Decoding

Shengyin Sun, Yiming Li, Renxi Liu, Xinqi Li+6

Department of Computer Science City University of Hong Kong · Huawei Technologies · City University of Hong Kong

Parallel Speculative Decoding Masked Diffusion Models Speculative Decoding for Diffusion Language Models Speculative Decoding Diffusion Language Model Inference Diffusion Language Model Decoding+3

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Diffusion large language models (dLLMs) generate text by iteratively denoising masked token sequences. Although dLLMs can predict all masked positions in parallel within each step, the large number of denoising iterations still makes inference expensive. This cost can be reduced spatially by unmasking multiple tokens per step, or temporally by collapsing multiple denoising steps into one verification call. We propose Parallel Speculative Decoding (PSD), a training-free framework that jointly improves inference along both axes. Using the confidence scores from a single forward pass, PSD selects positions to unmask via a configurable, adaptive unmasking policy and constructs multi-depth speculative drafts without extra model calls. A final batched verification pass then applies hierarchical acceptance, keeping the deepest draft that remains consistent with the updated predictions. Experiments on three dLLMs across reasoning and code generation tasks show that PSD achieves favorable trade-offs between inference efficiency and generation quality, reaching up to

5.5\times

tokens per forward pass with accuracy comparable to greedy decoding.