@inproceedings{xie-etal-2025-word,
    title = "Word Salad Chopper: Reasoning Models Waste A Ton Of Decoding Budget On Useless Repetitions, Self-Knowingly",
    author = "Xie, Wenya  and
      Zhong, Shaochen  and
      Le, Hoang Anh Duy  and
      Xu, Zhaozhuo  and
      Xie, Jianwen  and
      Liu, Zirui",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-luhme/2025.emnlp-main.1705/",
    doi = "10.18653/v1/2025.emnlp-main.1705",
    pages = "33576--33586",
    ISBN = "979-8-89176-332-6",
    abstract = "Large Reasoning Models (LRMs) are often bottlenecked by the high cost of output tokens. We show that a significant portion of these tokens are useless self-repetitions {---} what we call ``word salad'' {---} that exhaust the decoding budget without adding value. Interestingly, we observe that LRMs are self-aware when trapped in these loops: the hidden states of `{`} tokens trailing each reasoning chunk exhibit patterns that allow us to detect word salad behavior on-the-fly via a single linear classifier. Once detected, a simple chop appended by a straightforward regeneration prompt yields substantial length savings with minimal quality loss. Our work offers WordSaladChopper (WSC) {---} a lightweight, turnkey component for LRM that is minimally invasive to its reasoning trajectory. Given its low overhead, strong savings, and the lack of semantic value of word salad tokens, we believe it is not too far-fetched to argue that WSC {---} or a similar component {---} is a must-have for all LRM applications with user experience in mind."
}