@inproceedings{yu-etal-2025-revisiting,
title = "Revisiting Intermediate-Layer Matching in Knowledge Distillation: Layer-Selection Strategy Doesn{'}t Matter (Much)",
author = "Yu, Zony and
Wen, Yuqiao and
Mou, Lili",
editor = "Inui, Kentaro and
Sakti, Sakriani and
Wang, Haofen and
Wong, Derek F. and
Bhattacharyya, Pushpak and
Banerjee, Biplab and
Ekbal, Asif and
Chakraborty, Tanmoy and
Singh, Dhirendra Pratap",
booktitle = "Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics",
month = dec,
year = "2025",
address = "Mumbai, India",
publisher = "The Asian Federation of Natural Language Processing and The Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-ijcnlp.105/",
pages = "1686--1694",
ISBN = "979-8-89176-303-6",
abstract = "Knowledge distillation (KD) is a popular method of transferring knowledge from a large ``teacher'' model to a small ``student'' model. Previous work has explored various layer-selection strategies (e.g., forward matching and in-order random matching) for intermediate-layer matching in KD, where a student layer is forced to resemble a certain teacher layer. In this work, we revisit such layer-selection strategies and observe an intriguing phenomenon that layer-selection strategy does not matter (much) in intermediate-layer matching{---}even seemingly nonsensical matching strategies such as *reverse matching* still result in surprisingly good student performance. We provide an interpretation for this phenomenon by examining the angles between teacher layers viewed from the student{'}s perspective. Our work sheds light on KD practice, as layer-selection strategies may not be the main focus of KD system design and vanilla forward matching works well in most setups."
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<abstract>Knowledge distillation (KD) is a popular method of transferring knowledge from a large “teacher” model to a small “student” model. Previous work has explored various layer-selection strategies (e.g., forward matching and in-order random matching) for intermediate-layer matching in KD, where a student layer is forced to resemble a certain teacher layer. In this work, we revisit such layer-selection strategies and observe an intriguing phenomenon that layer-selection strategy does not matter (much) in intermediate-layer matching—even seemingly nonsensical matching strategies such as *reverse matching* still result in surprisingly good student performance. We provide an interpretation for this phenomenon by examining the angles between teacher layers viewed from the student’s perspective. Our work sheds light on KD practice, as layer-selection strategies may not be the main focus of KD system design and vanilla forward matching works well in most setups.</abstract>
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%0 Conference Proceedings
%T Revisiting Intermediate-Layer Matching in Knowledge Distillation: Layer-Selection Strategy Doesn’t Matter (Much)
%A Yu, Zony
%A Wen, Yuqiao
%A Mou, Lili
%Y Inui, Kentaro
%Y Sakti, Sakriani
%Y Wang, Haofen
%Y Wong, Derek F.
%Y Bhattacharyya, Pushpak
%Y Banerjee, Biplab
%Y Ekbal, Asif
%Y Chakraborty, Tanmoy
%Y Singh, Dhirendra Pratap
%S Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics
%D 2025
%8 December
%I The Asian Federation of Natural Language Processing and The Association for Computational Linguistics
%C Mumbai, India
%@ 979-8-89176-303-6
%F yu-etal-2025-revisiting
%X Knowledge distillation (KD) is a popular method of transferring knowledge from a large “teacher” model to a small “student” model. Previous work has explored various layer-selection strategies (e.g., forward matching and in-order random matching) for intermediate-layer matching in KD, where a student layer is forced to resemble a certain teacher layer. In this work, we revisit such layer-selection strategies and observe an intriguing phenomenon that layer-selection strategy does not matter (much) in intermediate-layer matching—even seemingly nonsensical matching strategies such as *reverse matching* still result in surprisingly good student performance. We provide an interpretation for this phenomenon by examining the angles between teacher layers viewed from the student’s perspective. Our work sheds light on KD practice, as layer-selection strategies may not be the main focus of KD system design and vanilla forward matching works well in most setups.
%U https://aclanthology.org/2025.findings-ijcnlp.105/
%P 1686-1694
Markdown (Informal)
[Revisiting Intermediate-Layer Matching in Knowledge Distillation: Layer-Selection Strategy Doesn’t Matter (Much)](https://aclanthology.org/2025.findings-ijcnlp.105/) (Yu et al., Findings 2025)
ACL