@inproceedings{marques-etal-2026-block,
title = "Block Recursive Transformers for Structured Parameter Sharing",
author = "Marques, Josep and
Cho, Hyunyoung A. and
Kim, Youngeun and
Zhang, Youjia and
Jung, Aecheon and
Shim, Kyuhong and
Hong, Sungeun",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.1868/",
pages = "39879--39891",
ISBN = "979-8-89176-462-0",
abstract = "Recursive Transformers depend not only on how many parameters are shared, but also on how shared parameters are organized across depth. Existing static schemes adopt two extreme reuse topologies: layer-wise repetition, where each layer is reused independently, and global cyclic reuse, where the entire stack is repeatedly applied as a single shared function. We argue that sharing granularity is an important factor in recursive parameter sharing. This study proposes Block Recursive Transformers (BRT), which partition layers into contiguous blocks and recursively reuse each block before moving to the next. This intermediate topology enables local refinement within blocks while preserving depth-wise transitions, without adding routing modules, auxiliary states, or extra training objectives. Across decoder-only models up to 1.7B parameters, with additional encoder-decoder and encoder-only studies, BRT improves validation negative log-likelihood and downstream accuracy over static recursive sharing baselines. Ablations and representation analyses further show that intermediate block granularity better balances local refinement and depth-wise differentiation. Project website: https://aim-skku.github.io/BRT"
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<abstract>Recursive Transformers depend not only on how many parameters are shared, but also on how shared parameters are organized across depth. Existing static schemes adopt two extreme reuse topologies: layer-wise repetition, where each layer is reused independently, and global cyclic reuse, where the entire stack is repeatedly applied as a single shared function. We argue that sharing granularity is an important factor in recursive parameter sharing. This study proposes Block Recursive Transformers (BRT), which partition layers into contiguous blocks and recursively reuse each block before moving to the next. This intermediate topology enables local refinement within blocks while preserving depth-wise transitions, without adding routing modules, auxiliary states, or extra training objectives. Across decoder-only models up to 1.7B parameters, with additional encoder-decoder and encoder-only studies, BRT improves validation negative log-likelihood and downstream accuracy over static recursive sharing baselines. Ablations and representation analyses further show that intermediate block granularity better balances local refinement and depth-wise differentiation. Project website: https://aim-skku.github.io/BRT</abstract>
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%0 Conference Proceedings
%T Block Recursive Transformers for Structured Parameter Sharing
%A Marques, Josep
%A Cho, Hyunyoung A.
%A Kim, Youngeun
%A Zhang, Youjia
%A Jung, Aecheon
%A Shim, Kyuhong
%A Hong, Sungeun
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F marques-etal-2026-block
%X Recursive Transformers depend not only on how many parameters are shared, but also on how shared parameters are organized across depth. Existing static schemes adopt two extreme reuse topologies: layer-wise repetition, where each layer is reused independently, and global cyclic reuse, where the entire stack is repeatedly applied as a single shared function. We argue that sharing granularity is an important factor in recursive parameter sharing. This study proposes Block Recursive Transformers (BRT), which partition layers into contiguous blocks and recursively reuse each block before moving to the next. This intermediate topology enables local refinement within blocks while preserving depth-wise transitions, without adding routing modules, auxiliary states, or extra training objectives. Across decoder-only models up to 1.7B parameters, with additional encoder-decoder and encoder-only studies, BRT improves validation negative log-likelihood and downstream accuracy over static recursive sharing baselines. Ablations and representation analyses further show that intermediate block granularity better balances local refinement and depth-wise differentiation. Project website: https://aim-skku.github.io/BRT
%U https://aclanthology.org/2026.findings-emnlp.1868/
%P 39879-39891
Markdown (Informal)
[Block Recursive Transformers for Structured Parameter Sharing](https://aclanthology.org/2026.findings-emnlp.1868/) (Marques et al., Findings 2026)
ACL
- Josep Marques, Hyunyoung A. Cho, Youngeun Kim, Youjia Zhang, Aecheon Jung, Kyuhong Shim, and Sungeun Hong. 2026. Block Recursive Transformers for Structured Parameter Sharing. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 39879–39891, Budapest, Hungary. Association for Computational Linguistics.