The paper introduces APEX, a learned controller that improves speculative decoding for large language models by dynamically selecting the best speculation strategy and adjusting draft depth during generation. APEX uses request‑level routing among EAGLE‑3, n‑gram, and draft‑model speculation, and block‑level depth adaptation based on causal decoding signals and verifier feedback. Integrated into vLLM and tested with Qwen3‑8B, APEX achieves up to 5.24× speedup over autoregressive decoding while reducing wasted tokens by 41% compared to fixed‑depth n‑gram speculation.
By Manvi Jha, Zach Zhang, Zhichao Xu, Linbo Liu, Sai Muralidhar Jayanthi, Vinayak Arannil
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:2604. 02047v2 Announce Type: replace-cross Abstract: Speculative decoding accelerates large language model inference by drafting multiple candidate tokens and verifying them in a single forward pass.
By Tao Jin, Phuong Minh Nguyen, Naoya Inoue
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
The paper introduces the Expected Decoding Rounds (EDR) objective, a theoretical framework that models speculative decoding as a Markov reward process and directly optimizes the number of decoding rounds. EDR weights local rejection costs by state occupancies, eliminating auxiliary hyperparameters and enabling unbiased stochastic optimization via temporal‑difference gradients. Applying EDR to fine‑tune state‑of‑the‑art draft models (DSpark and DFly) improves mean accepted length and outperforms prior training objectives across nine benchmarks in math reasoning, code generation, and chat.
By Yunxiao Zhao, Changxiao Cai
Draft-OPD introduces an on‑policy distillation method for speculative draft models, addressing the mismatch between supervised fine‑tuning and inference by letting the target model supervise the drafter on draft‑induced states. The approach uses target‑assisted rollouts for stable continuations and replays drafting from error positions exposed during verification, enabling the drafter to learn from both accepted and rejected proposals. Experiments demonstrate that Draft‑OPD achieves more than five‑fold lossless acceleration across diverse tasks, outperforming prior draft models such as EAGLE‑3 and DFlash by 23 % and 13 % respectively.
By Haodi Lei, Yafu Li, Haoran Zhang, Shunkai Zhang, Qianjia Cheng, Xiaoye Qu, Ganqu Cui, Bowen Zhou, Ning Ding, Yun Luo, Yu Cheng
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
NgramRL is a new method for accelerating reinforcement‑learning post‑training of large language models by using a per‑prompt n‑gram drafter built from recent rollout history, eliminating the need for auxiliary models or persistent GPU state. It incorporates an adaptive fan‑out prefetcher that predicts verification outcomes to hide CPU cache lookup and scheduling delays, and a causal budget allocator that focuses prefetches on high‑probability outcomes. Experiments on two model families and three reasoning/structured‑generation workloads show that NgramRL achieves 3.60–7.77 token savings per target‑model forward in the warm‑cache regime, up to 1.99× faster than SuffixDecoding and 7.0× faster than prompt‑lookup decoding, with the prefetcher improving throughput by 1.38× on Spider.
By Nilesh Prasad Pandey, Jangseon Park, Jason Kong, Onat Gungor, Tajana Rosing
arXiv:2605.14978v3 Announce Type: replace
Abstract: Speculative decoding accelerates LLM inference by having a lightweight draft model propose speculative windows of candidate tokens for parallel ver...
By Jie Jiang, Xing Sun, Ruotian Chen, Jianan Su, Kaixin Shen
The paper introduces Tree Exit Verification (TEV), an exact, level‑parallel speculative decoding method that uses a single exit‑node decision and a bonus‑token decision to verify draft continuations. By deriving a canonical exit law, the authors show that TEV achieves the same acceptance as optimal verifiers while reducing verifier‑stage latency by 15%. They also propose Exit‑Guided Draft‑Tree Training (ExitTrain), which improves draft trees and increases average output‑block length by 13%, leading to a 14% end‑to‑end speedup over DDTree across dialogue, code, and mathematical reasoning tasks.
By Shijing Hu, Xuancheng Ren, Zhihui Lu, Pan Zhou
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:2609.36590v1 Announce Type: cross
Abstract: Self-speculative decoding accelerates large language model (LLM) inference by drafting tokens from the target model itself, but faces a sharp tradeof...
By Hankun Lin, Patrick Pynadath, Ruqi Zhang