@inproceedings{wang-etal-2026-tikmix,
title = "{T}i{KM}i{X}: Efficient Semi-Dynamic Data Mixture via Data Influence for {LLM} Pre-training",
author = "Wang, Yifan and
Binbinliu and
Liu, Fengze and
Guo, Yuanfan and
Deng, Jiyao and
Wu, Xuecheng and
Zhou, Weidong and
Zhou, Xiaohuan and
Wang, Taifeng",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-long.261/",
pages = "5777--5793",
ISBN = "979-8-89176-390-6",
abstract = "The data mixture used in the pre-training of a language model is a cornerstone of its final performance. Static data mixing strategies in Large Language Model (LLM) pre-training are often suboptimal as they fail to adapt to the model{'}s evolving learning states. Conversely, fully online dynamic updates, while adaptive, incur prohibitive computational costs. To bridge this gap, we propose TiKMiX, an efficient semi-dynamic data mixing framework. Our approach is grounded in a key observation of influence ranking invariance: the relative importance of data domains exhibits strong temporal stability over long training intervals. Leveraging this insight, we propose Group Influence, an efficient approach for quantifying domain impact, and formulate data mixing as a periodic, low-overhead influence maximization problem. Compared with REGMIX, the proposed method reduces computational overhead by 80{\%} and achieves an average performance gain of 2{\%} across nine downstream benchmarks, thereby effectively mitigating data under-digestion."
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<abstract>The data mixture used in the pre-training of a language model is a cornerstone of its final performance. Static data mixing strategies in Large Language Model (LLM) pre-training are often suboptimal as they fail to adapt to the model’s evolving learning states. Conversely, fully online dynamic updates, while adaptive, incur prohibitive computational costs. To bridge this gap, we propose TiKMiX, an efficient semi-dynamic data mixing framework. Our approach is grounded in a key observation of influence ranking invariance: the relative importance of data domains exhibits strong temporal stability over long training intervals. Leveraging this insight, we propose Group Influence, an efficient approach for quantifying domain impact, and formulate data mixing as a periodic, low-overhead influence maximization problem. Compared with REGMIX, the proposed method reduces computational overhead by 80% and achieves an average performance gain of 2% across nine downstream benchmarks, thereby effectively mitigating data under-digestion.</abstract>
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%0 Conference Proceedings
%T TiKMiX: Efficient Semi-Dynamic Data Mixture via Data Influence for LLM Pre-training
%A Wang, Yifan
%A Liu, Fengze
%A Guo, Yuanfan
%A Deng, Jiyao
%A Wu, Xuecheng
%A Zhou, Weidong
%A Zhou, Xiaohuan
%A Wang, Taifeng
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%A Binbinliu
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F wang-etal-2026-tikmix
%X The data mixture used in the pre-training of a language model is a cornerstone of its final performance. Static data mixing strategies in Large Language Model (LLM) pre-training are often suboptimal as they fail to adapt to the model’s evolving learning states. Conversely, fully online dynamic updates, while adaptive, incur prohibitive computational costs. To bridge this gap, we propose TiKMiX, an efficient semi-dynamic data mixing framework. Our approach is grounded in a key observation of influence ranking invariance: the relative importance of data domains exhibits strong temporal stability over long training intervals. Leveraging this insight, we propose Group Influence, an efficient approach for quantifying domain impact, and formulate data mixing as a periodic, low-overhead influence maximization problem. Compared with REGMIX, the proposed method reduces computational overhead by 80% and achieves an average performance gain of 2% across nine downstream benchmarks, thereby effectively mitigating data under-digestion.
%U https://aclanthology.org/2026.acl-long.261/
%P 5777-5793
Markdown (Informal)
[TiKMiX: Efficient Semi-Dynamic Data Mixture via Data Influence for LLM Pre-training](https://aclanthology.org/2026.acl-long.261/) (Wang et al., ACL 2026)
ACL
- Yifan Wang, Binbinliu, Fengze Liu, Yuanfan Guo, Jiyao Deng, Xuecheng Wu, Weidong Zhou, Xiaohuan Zhou, and Taifeng Wang. 2026. TiKMiX: Efficient Semi-Dynamic Data Mixture via Data Influence for LLM Pre-training. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 5777–5793, San Diego, California, United States. Association for Computational Linguistics.