Anti-Length Shift: Dynamic Outlier Truncation for Training Efficient Reasoning Models

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) 2026 Open access

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Authors: Wei Wu, Liyi Chen, Congxi Xiao, Tianfu Wang, Qimeng Wang, Chengqiang Lu, Yan Gao, Yiwu, Yao Hu, Hui Xiong
DOI: 10.18653/v1/2026.acl-long.1047
Cited by: 1

Abstract

Wei Wu, Liyi Chen, Congxi Xiao, Tianfu Wang, Qimeng Wang, Chengqiang Lu, Yan Gao, Yiwu, Yao Hu, Hui Xiong. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Fields of Study & Subjects

Natural Language Processing Techniques Topic Modeling Machine Learning in Healthcare

Publication details

Journal / source
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Publisher
Association for Computational Linguistics
Pages
22863–22891
Publication date
1 January 2026