@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://aclanthology.org/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."
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<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.</abstract>
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%0 Conference Proceedings
%T Which Reasoning Trajectories Teach Students to Reason Better? A Simple Metric of Informative Alignment
%A Yang, Yuming
%A Lai, Mingyoung
%A Zhao, Wanxu
%A Fan, Xiaoran
%A Xi, Zhiheng
%A Wu, Mingqi
%A Huang, Chiyue
%A Zhao, Jun
%A Lv, Haijun
%A Tong, Jian
%A Zhou, Yunhua
%A Zou, Yicheng
%A Guo, Qipeng
%A Gui, Tao
%A Zhang, Qi
%A Huang, Xuanjing
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F yang-etal-2026-reasoning
%X 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.
%U https://aclanthology.org/2026.acl-long.1950/
%P 42123-42150
Markdown (Informal)
[Which Reasoning Trajectories Teach Students to Reason Better? A Simple Metric of Informative Alignment](https://aclanthology.org/2026.acl-long.1950/) (Yang et al., ACL 2026)
ACL
- Yuming Yang, Mingyoung Lai, Wanxu Zhao, Xiaoran Fan, Zhiheng Xi, Mingqi Wu, Chiyue Huang, Jun Zhao, Haijun Lv, Jian Tong, Yunhua Zhou, Yicheng Zou, Qipeng Guo, Tao Gui, Qi Zhang, and Xuanjing Huang. 2026. Which Reasoning Trajectories Teach Students to Reason Better? A Simple Metric of Informative Alignment. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 42123–42150, San Diego, California, United States. Association for Computational Linguistics.