@inproceedings{yang-etal-2026-reasoning,
    title = "Which Reasoning Trajectories Teach Students to Reason Better? A Simple Metric of Informative Alignment",
    author = "Yang, Yuming  and
      Lai, Mingyoung  and
      Zhao, Wanxu  and
      Fan, Xiaoran  and
      Xi, Zhiheng  and
      Wu, Mingqi  and
      Huang, Chiyue  and
      Zhao, Jun  and
      Lv, Haijun  and
      Tong, Jian  and
      Zhou, Yunhua  and
      Zou, Yicheng  and
      Guo, Qipeng  and
      Gui, Tao  and
      Zhang, Qi  and
      Huang, Xuanjing",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-long.1950/",
    pages = "42123--42150",
    ISBN = "979-8-89176-390-6",
    abstract = "Long chain-of-thought (CoT) trajectories provide rich supervision signals for distilling reasoning from teacher to student LLMs. However, both prior work and our experiments show that trajectories from stronger teachers do not necessarily yield better students, highlighting the importance of data-student suitability in distillation. Existing methods assess suitability primarily through student likelihood, favoring trajectories that align closely with the student model{'}s current behavior but overlooking more informative ones. Addressing this, we propose Rank{--}Surprisal Ratio (RSR), a simple metric that captures both alignment and informativeness to assess the suitability of a reasoning trajectory. RSR is motivated by the observation that effective trajectories typically balance learning signal strength and behavioral alignment by combining low absolute probability with relatively high-ranked tokens under the student model.Concretely, RSR is defined as the ratio of a trajectory{'}s average token-wise rank to its average negative log-likelihood, and is straightforward to compute and interpret. Across five student models and reasoning trajectories from 11 diverse teachers, RSR strongly correlates with post-training reasoning performance (average Spearman 0.86), consistently outperforming existing metrics. We further demonstrate its practical utility in both trajectory selection and teacher selection."
}