arXiv Computation and Language By Zhaoxiang Feng (UC San Diego), Lanxiang Hu (UC San Diego), Haoran Yuan (UIUC), Yu-Yang Qian (Nanjing University), Jihui Yang (Meta), Peng Zhao (Nanjing University), Yibo Zhu (StepFun), Tajana Rosing (UC San Diego), Hao Zhang (UC San Diego)

JetSpec++: Foresight Parallel Drafting with Self Refinements

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arXiv Computation and Language
Sep 24

When Parallel Drafter Meets Parallel Speculative Decoding

The paper introduces DPara, a parallel speculative decoding framework that builds on DSpark-style parallel drafters. DPara eliminates the need for probabilistic guesses by precomputing draft representations for every acceptance boundary and using a lightweight autoregressive head to combine verification outcomes with these representations, enabling full parallelization of the backbone forward pass. Experiments on Qwen3-8B and Qwen3-14B across multiple benchmarks demonstrate average speedups of 3.21× and 3.52× over autoregressive decoding, outperforming existing serial and parallel speculative decoding methods.

By Fuliang Liu, Xue Li, Kun Qian, Zhibin Wang, Wanchun Dou, Wenyuan Yu, Chen Tian
arXiv Computation and Language
Oct 7

DLoop: Looped Speculative Decoding

The paper introduces DLoop, a looped speculative decoding technique that adaptively performs multiple drafting stages before verification, allowing a draft model to continue generating tokens while confident. By verifying all accumulated draft tokens together and training the draft model to handle its own hidden states for unverified tokens, DLoop reduces the number of target‑model forward passes needed. Experiments across several speculative decoding methods show wall‑clock speedups of 5–41 % without sacrificing lossless decoding.

By Geonmo Gu, Byeongho Heo, HeeJae Jun, Yoohoon Kang, Sangmin Lee, Sangdoo Yun, Dongyoon Han
arXiv AI
Aug 26

ResiSpec: Enhancing Multi-Candidate Speculative Sampling via Residual Distribution Shaping

ResiSpec is a framework that improves speculative decoding for large language models by reshaping the residual distribution during verification. It addresses the problem of residual drift, where rejected candidates cause the target distribution to diverge from the draft model’s predictions, rendering later candidates ineffective. By aligning the verification process with the draft model’s high‑confidence regions, ResiSpec prevents candidate obsolescence and achieves up to 1.92× speedup over existing multi‑candidate methods.

By Zhi-Kai Chen, Jun-Jie Tao, Wei-Xiang Mao, De-Chuan Zhan, Han-Jia Ye
arXiv Machine Learning
Oct 28

One Call Ahead: Packed Self-Speculative Decoding with Diffusion Drafting

The paper introduces Packed-SS, a self‑speculative decoding schedule that combines drafting and verification into a single model call, reducing input size to linear in speculative width. By running the diffusion drafter one step ahead, the method keeps an unresolved suffix for the next iteration, avoiding the sequential wait of standard two‑call decoding. Experiments on Nemotron 3B/8B and SetDLM 1.7B demonstrate higher throughput and lower mean wall time under greedy decoding.

By Yuxiao Chen, Changxiao Cai