@inproceedings{luo-etal-2025-turning,
    title = "Turning Trash into Treasure: Accelerating Inference of Large Language Models with Token Recycling",
    author = "Luo, Xianzhen  and
      Wang, Yixuan  and
      Zhu, Qingfu  and
      Zhang, Zhiming  and
      Zhang, Xuanyu  and
      Yang, Qing  and
      Xu, Dongliang",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingestion-acl-25/2025.acl-long.338/",
    pages = "6816--6831",
    ISBN = "979-8-89176-251-0",
    abstract = "The rapid growth in the parameters of LLMs has made inference latency a fundamental bottleneck. Speculative decoding represents a lossless approach to accelerate inference through a guess-and-verify paradigm. Some methods rely on additional architectures to guess draft tokens, which need extra training before use. Alternatively, retrieval-based train-free techniques build libraries from pre-existing corpora or by n-gram generation. However, they face challenges like large storage requirements, time-consuming retrieval, and limited adaptability. Observing that candidate tokens generated during the decoding process are likely to reoccur in future sequences, we propose Token Recycling. This approach stores candidate tokens in an adjacency matrix and employs a breadth-first-search (BFS)-like algorithm to construct a draft tree, which is then validated through tree attention. New candidate tokens from the decoding process are then used to update the matrix. Token Recycling requires {\ensuremath{<}}2MB of additional storage and achieves approximately 2x speedup across all sizes of LLMs. It significantly outperforms existing train-free methods by 30{\%} and even a training method by 25{\%}."
}