Judge Decoding: Faster Speculative Sampling Requires Going Beyond Model Alignment
Gregor Bachmann, Sotiris Anagnostidis, Albert Pumarola, Markos Georgopoulos, Artsiom Sanakoyeu, Yuming Du, Edgar Schönfeld, Ali K. Thabet, Jonas Kohler
摘要
The performance of large language models (LLMs) is closely linked to their underlying size, leading to ever-growing networks and hence slower inference. Speculative decoding has been proposed as a technique to accelerate autoregressive generation, leveraging a fast draft model to propose candidate tokens, which are then verified in parallel based on their likelihood under the target model. While this approach guarantees to reproduce the target output, it incurs a substantial penalty: many high-quality draft tokens are rejected, even when they represent objectively valid continuations. Indeed, we show that even powerful draft models such as GPT-4o, as well as human text cannot achieve high acceptance rates under the standard verification scheme. This severely limits the speedup potential of current speculative decoding methods, as an early rejection becomes overwhelmingly likely when solely relying on alignment of draft and target. We thus ask the following question: Can we adapt verification to recognize correct, but non-aligned replies? To this end, we draw inspiration from the LLMas-a-judge framework, which demonstrated that LLMs are able to rate answers in a versatile way. We carefully design a dataset to elicit the same capability in the target model by training a compact module on top of the embeddings to produce "judgements" of the current continuation. We showcase our strategy on the Llama-3.1 family, where our 8b/405B-Judge achieves a speedup of 9× over Llama-405B, while maintaining its quality on a large range of benchmarks. These benefits remain present even in optimized inference frameworks, where our method reaches up to 141 tokens/s for 8B/70B-Judge and 129 tokens/s for 8B/405B on 2 and 8 H100s respectively. -8b/ -405b Judge Verification: User Question: Chenny is 10 years old. Alyana is 4 years younger than Chenny. How old is Anne if she is 2 years older than Alyana? To find Alyana's age, we need to subtract 4 from Chenny's age. Chenny's age: 10 years Alyana's age: 10 -4 = 6 years To find Anne's age, we need to add 2 to Alyana's age.
问问这篇 Paper
智能体会读完全文。
Lune 把这篇 Paper 索引到了每一个公式,引用它的顶会 Paper 也一样。你提问,回答直接引用原文。
引用它的顶会 Paper23
- AutoJudge: Judge Decoding Without Manual AnnotationRoman Garipov, Fedor Velikonivtsev, Ivan Ermakov, Ruslan Svirschevski 等NeurIPS 2025 · 被引用 14 次
- LongSpec: Long-Context Lossless Speculative Decoding with Efficient Drafting and VerificationPenghui Yang, Cunxiao Du, Fengzhuo Zhang, Haonan Wang 等ACL 2026 · 被引用 12 次
- Training-Free Loosely Speculative Decoding: Accepting Semantically Correct Drafts Beyond Exact MatchJinze Li, Yixing Xu, Guanchen Li, Shuo Yang 等ICLR 2026 · 被引用 12 次
- Dynamic Speculative Agent PlanningYilin Guan, Qingfeng Lan, Fei Sun, Dujian Ding 等ICLR 2026 · 被引用 10 次
- STree: Speculative Tree Decoding for Hybrid State Space ModelsYangchao Wu, Zongyue Qin, Alex Wong, Stefano SoattoNeurIPS 2025 · 被引用 8 次
它引用的顶会 Paper22
- Measuring Massive Multitask Language UnderstandingDan Hendrycks, Collin Burns, Steven Basart, Andy Zou 等ICLR 2021 · 被引用 7,905 次
- Fast Inference from Transformers via Speculative DecodingYaniv Leviathan, Matan Kalman, Yossi MatiasICML 2023 · 被引用 1,472 次
- EAGLE: Speculative Sampling Requires Rethinking Feature UncertaintyYuhui Li, Fangyun Wei, Chao Zhang, Hongyang ZhangICML 2024 · 被引用 424 次
- Confident Adaptive Language ModelingTal Schuster, Adam Fisch, Jai Gupta, Mostafa Dehghani 等NeurIPS 2022 · 被引用 394 次
- Language Models Represent Space and TimeWes Gurnee, Max TegmarkICLR 2024 · 被引用 303 次
相关 Paper
- SelfJudge: Faster Speculative Decoding via Self-Supervised Judge VerificationKanghoon Yoon, Minsub Kim, Sungjae Lee, Joonhyung Lee 等ICML 2026 · 被引用 4 次
- Speculative Decoding with CTC-based Draft Model for LLM Inference AccelerationZhuofan Wen, Shangtong Gui, Yang FengNeurIPS 2024 · 被引用 19 次
- Talon: Breaking the Synchronization Barrier in Speculative Decoding with Hybrid Model-based and Retrieve-based DraftingXiangxiang Gao, Weisheng Xie, Lixin, Xuwei Fang 等AAAI 2026
- polybasic Speculative Decoding Through a Theoretical PerspectiveRuilin Wang, Huixia Li, Yuexiao Ma, Xiawu Zheng 等ICML 2025
- Draft& Verify: Lossless Large Language Model Acceleration via Self-Speculative DecodingJun Zhang, Jue Wang, Huan Li, Lidan Shou 等ACL 2024
