The paper introduces AdaptiveSpec, a training‑free speculative decoding method that simultaneously adapts the per‑step verification rule and the draft‑tree shape using signals generated during decoding. It replaces the fixed token‑match rule with a margin‑based threshold and adjusts tree depth, width, and node count based on draft confidence and recent acceptance history, allowing the total draft count to vary. Experiments on SGLang show up to 56% throughput gains over EAGLE‑3 while maintaining 93% of lossless task accuracy on GSM8K, MATH‑500, and HumanEval across three models.
By Oszk\'ar Urb\'an, Young D. Kwon, Stylianos I. Venieris, Cecilia Mascolo
Speculative decoding losslessly accelerates autoregressive language models by verifying multiple draft tokens in parallel. Diffusion-based drafters further reduce proposal latency by predicting an entire token block in parallel, but their position-wise distributions are marginal rather than conditioned on tokens selected along each draft path.
arXiv:2608. 08721v1 Announce Type: cross Abstract: Speculative decoding accelerates large language model inference by drafting multiple tokens for parallel verification, with efficiency critically determined by the speculative length selected at each decoding round.
By Zexun Lin, Yuan Feng, Junlin Lv, Kevin S. Zhou, Xike Xie
arXiv:2609.39972v1 Announce Type: new
Abstract: Speculative decoding accelerates language model inference by verifying multiple draft tokens in a single target-model pass. Recent parallel drafters ha...
By Chumeng Liang, Linxuan Wang, Xinyu Peng, Huabin Liu, Yuxin Chen, Ge Liu, Guang Lin, Qifan Song, Jianguo Li
arXiv:2609.24698v1 Announce Type: new
Abstract: Repeated execution of the target model during autoregressive decoding is a major source of LLM inference latency. Unlike linear speculation, which foll...
By Changxu Liu, Zhaogeng Li
arXiv:2608.20375v1 Announce Type: new
Abstract: Tree-based speculative decoding raises the mean accepted tokens of standard speculative decoding by verifying multiple draft paths, and existing tree b...
By Xuming Ye, Zeming Ma, Runjie Yu, Yuan Liu, Tianle Li, Shuhan Bai, Jian Zhou, Fei Wu
arXiv:2606. 00487v1 Announce Type: new Abstract: Using a diffusion model for parallel drafting is a promising approach for speculative decoding.
By Zhuoyu Wang, Junnan Huang, Xinyu Chen
arXiv:2609.14717v1 Announce Type: cross
Abstract: Speculative decoding accelerates LLM inference by verifying multiple drafted tokens in parallel, allowing a single target forward pass to accept seve...
By Jahyun Koo, Sunghyeon Woo, Jaeeun Kil, Jeongtae Lee, Sungjae Lee, Kyomin Jung, Minsub Kim
TreeGraft introduces a multi-drafter framework that combines drafters of varying costs to build a shared draft tree for tree-based speculative decoding. The stronger drafter rescues and rescoring candidates from the weaker drafter, while a lightweight scheduler decides when to invoke the stronger drafter to manage cost. Experiments on 10 model pairs and 6 benchmarks show TreeGraft improves over the best single-drafter strategy by an average of 15.1% and up to 26.6%.
By Jiaming Fan, Daming Cao, Canchen Huang, Jiale Fu, Jin Zhang, Junjie Gao, Kai Yang, Xiangzhong Luo, Xu Yang
Verification-Aware Training (VAT) is a plug‑in framework that improves speculative decoding for large language models by simulating verification during training and using the resulting accept/reject patterns as supervision. VAT adds a lightweight binary verification head to predict whether each draft token will survive sequential verification, and replaces the fixed per‑position weighting with a verification‑adaptive schedule that keeps full weight up to the first rejection point. When applied to EAGLE‑3 and DFlash on Qwen3‑4B, Qwen3‑8B, and LLaMA‑3.1‑8B, VAT increases average acceptance length by up to 11.4% and wall‑clock speedup by up to 8.7%, yielding consistent gains across math, code, and chat benchmarks.
By Geonmo Gu, Byeongho Heo, HeeJae Jun, Yoohoon Kang, Sangmin Lee, Sangdoo Yun, Dongyoon Han
arXiv:2609.22098v1 Announce Type: new
Abstract: Speculative decoding accelerates language-model inference by letting a cheap drafter propose tokens that the target model verifies in parallel. Recent...
By Huapeng Zhou, Huayu Wang, Xinyu Wang
arXiv:2609.38510v1 Announce Type: new
Abstract: Speculative decoding accelerates autoregressive LLMs by having a lightweight drafter propose tokens that the target model verifies in parallel. Diffusi...
By Longxuan Yu, Bingsen Chen, Peng Shi, Dongkyu Lee, Yi Xiang, Hideo Kobayashi, Sheng Zhang, Shuaichen Chang, Xing Niu, Zhuoyan Xu, Greg Ver Steeg, Jiarong Jiang