@inproceedings{gu-etal-2026-advantageous,
title = "Advantageous Parameter Expansion Training Makes Better Large Language Models",
author = "Gu, Naibin and
Chen, Yilong and
Zhang, Zhenyu and
Fu, Peng and
Lin, Zheng and
Wang, Shuohuan and
Sun, Yu and
Wu, Hua and
Wang, Weiping and
Wang, Haifeng",
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.386/",
pages = "8852--8874",
ISBN = "979-8-89176-459-0",
abstract = "Although scaling up the number of trainable parameters can effectively improve the training performance of large language models, it also leads to increased computational overhead. When delving into the parameter difference, we find that a subset of parameters, termed advantageous parameters, plays a crucial role in determining model performance. Further analysis reveals that stronger models tend to possess more such parameters. In this paper, we propose **A**dvantageous **P**arameter **EX**pansion Training (APEX), a method that progressively expands advantageous parameters into the space of disadvantageous ones, thereby increasing their proportion and enhancing training effectiveness, while keeping the total parameter count unchanged. Extensive experiments on both instruction tuning and continued pre-training across five base models demonstrate that, in instruction tuning, APEX outperforms full-parameter tuning while using only 52{\%} of the trainable parameters. In continued pre-training, APEX achieves the same perplexity level as conventional training with only approximately 30{\%} of the training data, and yields significant improvements on downstream tasks."
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<abstract>Although scaling up the number of trainable parameters can effectively improve the training performance of large language models, it also leads to increased computational overhead. When delving into the parameter difference, we find that a subset of parameters, termed advantageous parameters, plays a crucial role in determining model performance. Further analysis reveals that stronger models tend to possess more such parameters. In this paper, we propose **A**dvantageous **P**arameter **EX**pansion Training (APEX), a method that progressively expands advantageous parameters into the space of disadvantageous ones, thereby increasing their proportion and enhancing training effectiveness, while keeping the total parameter count unchanged. Extensive experiments on both instruction tuning and continued pre-training across five base models demonstrate that, in instruction tuning, APEX outperforms full-parameter tuning while using only 52% of the trainable parameters. In continued pre-training, APEX achieves the same perplexity level as conventional training with only approximately 30% of the training data, and yields significant improvements on downstream tasks.</abstract>
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%0 Conference Proceedings
%T Advantageous Parameter Expansion Training Makes Better Large Language Models
%A Gu, Naibin
%A Chen, Yilong
%A Zhang, Zhenyu
%A Fu, Peng
%A Lin, Zheng
%A Wang, Shuohuan
%A Sun, Yu
%A Wu, Hua
%A Wang, Weiping
%A Wang, Haifeng
%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 gu-etal-2026-advantageous
%X Although scaling up the number of trainable parameters can effectively improve the training performance of large language models, it also leads to increased computational overhead. When delving into the parameter difference, we find that a subset of parameters, termed advantageous parameters, plays a crucial role in determining model performance. Further analysis reveals that stronger models tend to possess more such parameters. In this paper, we propose **A**dvantageous **P**arameter **EX**pansion Training (APEX), a method that progressively expands advantageous parameters into the space of disadvantageous ones, thereby increasing their proportion and enhancing training effectiveness, while keeping the total parameter count unchanged. Extensive experiments on both instruction tuning and continued pre-training across five base models demonstrate that, in instruction tuning, APEX outperforms full-parameter tuning while using only 52% of the trainable parameters. In continued pre-training, APEX achieves the same perplexity level as conventional training with only approximately 30% of the training data, and yields significant improvements on downstream tasks.
%U https://aclanthology.org/2026.emnlp-main.386/
%P 8852-8874
Markdown (Informal)
[Advantageous Parameter Expansion Training Makes Better Large Language Models](https://aclanthology.org/2026.emnlp-main.386/) (Gu et al., EMNLP 2026)
ACL
- Naibin Gu, Yilong Chen, Zhenyu Zhang, Peng Fu, Zheng Lin, Shuohuan Wang, Yu Sun, Hua Wu, Weiping Wang, and Haifeng Wang. 2026. Advantageous Parameter Expansion Training Makes Better Large Language Models. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 8852–8874, Budapest, Hungary. Association for Computational Linguistics.