@inproceedings{hu-etal-2026-strongest,
title = "When the Strongest Teacher Is Not the Best Teacher: Student-Centric Answer Selection",
author = "Hu, Zhengyu and
Xiao, Zheyuan and
Song, Linxin and
Jiang, Fengqing and
Li, Yuetai and
Xiong, Zhihan and
Lin, Junhao and
Liu, Yue and
Su, Yao and
Hu, Lijie and
Ding, Kaize and
Xiao, Teng and
Poovendran, Radha",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.1369/",
pages = "31221--31256",
ISBN = "979-8-89176-459-0",
abstract = "LLM training increasingly relies on teacher-generated supervision, from synthetic responses to reasoning traces and tool-use demonstrations. Current practice often chooses the highest-performing teacher to generate student training data, implicitly treating teacher test performance as a proxy for teaching quality. We show that this assumption can fail: even when multiple teachers provide correct answers to the same question, the answer from the strongest teacher is not necessarily the best supervision for a given student. To address this gap, we propose Student-Centric Answer Sampling (SCAS), a framework that selects from verified teacher-generated answers according to their estimated student-centric learning cost. Motivated by a token-wise gradient decomposition, we derive an efficient forward-only proxy for this cost and use it to guide answer selection during training. Experiments across 30 teacher models, 6 student base models, and 8 tasks show that SCAS consistently improves student performance, suggesting that effective distillation should prioritize supervision matched to the current student rather than teacher strength alone."
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<abstract>LLM training increasingly relies on teacher-generated supervision, from synthetic responses to reasoning traces and tool-use demonstrations. Current practice often chooses the highest-performing teacher to generate student training data, implicitly treating teacher test performance as a proxy for teaching quality. We show that this assumption can fail: even when multiple teachers provide correct answers to the same question, the answer from the strongest teacher is not necessarily the best supervision for a given student. To address this gap, we propose Student-Centric Answer Sampling (SCAS), a framework that selects from verified teacher-generated answers according to their estimated student-centric learning cost. Motivated by a token-wise gradient decomposition, we derive an efficient forward-only proxy for this cost and use it to guide answer selection during training. Experiments across 30 teacher models, 6 student base models, and 8 tasks show that SCAS consistently improves student performance, suggesting that effective distillation should prioritize supervision matched to the current student rather than teacher strength alone.</abstract>
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%0 Conference Proceedings
%T When the Strongest Teacher Is Not the Best Teacher: Student-Centric Answer Selection
%A Hu, Zhengyu
%A Xiao, Zheyuan
%A Song, Linxin
%A Jiang, Fengqing
%A Li, Yuetai
%A Xiong, Zhihan
%A Lin, Junhao
%A Liu, Yue
%A Su, Yao
%A Hu, Lijie
%A Ding, Kaize
%A Xiao, Teng
%A Poovendran, Radha
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F hu-etal-2026-strongest
%X LLM training increasingly relies on teacher-generated supervision, from synthetic responses to reasoning traces and tool-use demonstrations. Current practice often chooses the highest-performing teacher to generate student training data, implicitly treating teacher test performance as a proxy for teaching quality. We show that this assumption can fail: even when multiple teachers provide correct answers to the same question, the answer from the strongest teacher is not necessarily the best supervision for a given student. To address this gap, we propose Student-Centric Answer Sampling (SCAS), a framework that selects from verified teacher-generated answers according to their estimated student-centric learning cost. Motivated by a token-wise gradient decomposition, we derive an efficient forward-only proxy for this cost and use it to guide answer selection during training. Experiments across 30 teacher models, 6 student base models, and 8 tasks show that SCAS consistently improves student performance, suggesting that effective distillation should prioritize supervision matched to the current student rather than teacher strength alone.
%U https://aclanthology.org/2026.emnlp-main.1369/
%P 31221-31256
Markdown (Informal)
[When the Strongest Teacher Is Not the Best Teacher: Student-Centric Answer Selection](https://aclanthology.org/2026.emnlp-main.1369/) (Hu et al., EMNLP 2026)
ACL
- Zhengyu Hu, Zheyuan Xiao, Linxin Song, Fengqing Jiang, Yuetai Li, Zhihan Xiong, Junhao Lin, Yue Liu, Yao Su, Lijie Hu, Kaize Ding, Teng Xiao, and Radha Poovendran. 2026. When the Strongest Teacher Is Not the Best Teacher: Student-Centric Answer Selection. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 31221–31256, Budapest, Hungary. Association for Computational Linguistics.