--- title: "Who cites Online Speculative Decoding — 62 citing papers" description: "62 papers of the Elamur Research library cite «Online Speculative Decoding», found in their bibliographies; the most cited first." canonical: "https://research.elamur.ai/free/en/papers/liu-2023-online-speculative-decoding/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/liu-2023-online-speculative-decoding/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [Online Speculative Decoding](https://md.research.elamur.ai/free/en/papers/liu-2023-online-speculative-decoding/) / cited by # Who cites «Online Speculative Decoding» 62 papers of our library cite it (of the 149,439 whose bibliographies we read) · 15 with a page here, the most cited first · [← back to «Online Speculative Decoding»](https://md.research.elamur.ai/free/en/papers/liu-2023-online-speculative-decoding/) 1. [**On-Policy Distillation of Language Models: Learning from Self-Generated Mistakes arXiv:2306.13649**Rishabh Agarwal, Nino Vieillard, Yongchao Zhou, et al.*2023 · 299 citations*](https://md.research.elamur.ai/free/en/papers/agarwal-2023-policy-distillation-language-models-learning/) 2. [**EAGLE: Speculative Sampling Requires Rethinking Feature Uncertainty arXiv:2401.15077**Yuhui Li, Fangyun Wei, Chao Zhang, et al.*2024 · 208 citations*](https://md.research.elamur.ai/free/en/papers/li-2024-eagle-speculative-sampling-requires-rethinking/) 3. **[Unlocking Efficiency in Large Language Model Inference: A Comprehensive Survey of Speculative Decoding](https://md.research.elamur.ai/free/en/papers/xia-2024-unlocking-efficiency-large-language-model/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Heming Xia, Zhe Yang, Qingxiu Dong, et al.*2024 · 146 citations* 4. **[EAGLE-2: Faster Inference of Language Models with Dynamic Draft Trees](https://md.research.elamur.ai/free/en/papers/li-2024-eagle-2-faster-inference-language/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Yuhui Li, Fangyun Wei, Chao Zhang, et al.*2024 · 133 citations* 5. [**A Survey on Efficient Inference for Large Language Models arXiv:2404.14294**Zixuan Zhou, Xuefei Ning, Ke Hong, et al.*2024 · 113 citations*](https://md.research.elamur.ai/free/en/papers/zhou-2024-survey-efficient-inference-large-language/) 6. **[Towards Efficient Generative Large Language Model Serving: A Survey from Algorithms to Systems](https://md.research.elamur.ai/free/en/papers/miao-2023-towards-efficient-generative-large-language/) [ACM Computing Surveys](https://md.research.elamur.ai/free/en/venues/acm-computing-surveys/)**Xupeng Miao, Gabriele Oliaro, Zhihao Zhang, et al.*2023 · 110 citations* 7. [**LLM Inference Unveiled: Survey and Roofline Model Insights arXiv 2024**Zhihang Yuan, Yuzhang Shang, Yang Zhou, et al.*2024 · 91 citations*](https://md.research.elamur.ai/free/en/papers/yuan-2024-llm-inference-unveiled-survey-roofline/) 8. **[Rephrasing the Web: A Recipe for Compute and Data-Efficient Language Modeling](https://md.research.elamur.ai/free/en/papers/maini-2024-rephrasing-web-recipe-compute-data/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Pratyush Maini, Skyler Seto, Richard Bai, et al.*2024 · 90 citations* 9. [**Speculative Knowledge Distillation: Bridging the Teacher-Student Gap Through Interleaved Sampling arXiv:2410.11325**Wenda Xu, Rujun Han, Zifeng Wang, et al.*2024 · 41 citations*](https://md.research.elamur.ai/free/en/papers/xu-2024-speculative-knowledge-distillation-bridging-teacher/) 10. [**MambaByte: Token-free Selective State Space Model arXiv:2401.13660**Junxiong Wang, Tushaar Gangavarapu, Jing Nathan Yan, et al.*2024 · 35 citations*](https://md.research.elamur.ai/free/en/papers/wang-2024-mambabyte-token-free-selective-state/) 11. **[Turning Trash into Treasure: Accelerating Inference of Large Language Models with Token Recycling](https://md.research.elamur.ai/free/en/papers/luo-2025-turning-trash-into-treasure-accelerating/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Xianzhen Luo, Yixuan Wang, Qingfu Zhu, et al.*2025 · 22 citations* 12. **[SEED: Accelerating Reasoning Tree Construction via Scheduled Speculative Decoding](https://md.research.elamur.ai/free/en/papers/wang-2025-seed-accelerating-reasoning-tree-construction/) [International Conference on Computational Linguistics (COLING)](https://md.research.elamur.ai/free/en/venues/coling/)**Zhenglin Wang, Jialong Wu, Yilong Lai, et al.*2025 · 8 citations* 13. **[LongSpec: Long-Context Lossless Speculative Decoding with Efficient Drafting and Verification](https://md.research.elamur.ai/free/en/papers/yang-2026-longspec-long-context-lossless-speculative/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Penghui Yang, Cunxiao Du, Fengzhuo Zhang, et al.*2026 · 5 citations* 14. **[MARS: Unleashing the Power of Speculative Decoding via Margin-Aware Verification](https://md.research.elamur.ai/free/en/papers/song-2026-mars-unleashing-power-speculative-decoding/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Jingwei Song, Xinyu Wang, Hanbin Wang, et al.*2026* 15. **[Measuring Distribution Shift in User Prompts and Its Effects on LLM Performance](https://md.research.elamur.ai/free/en/papers/seegmiller-2026-measuring-distribution-shift-user-prompts/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Parker Seegmiller, Sarah Masud Preum*2026* 47 more papers of our library cite it; they have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/papers/liu-2023-online-speculative-decoding/) — Online Speculative Decoding