@inproceedings{hu-etal-2026-fine,
title = "Fine-Grained Data Ordering Improves Fine-Tuning for Large Language Models",
author = "Hu, Xiaomeng and
Tang, Yixuan and
Li, Haoze and
Chen, Hao and
Zhang, Qi and
Shen, Zhanming and
Zhang, Yiming and
Wang, Haobo and
Zhao, Junbo",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.1021/",
pages = "20406--20418",
ISBN = "979-8-89176-395-1",
abstract = "With the rapid progress of large language models (LLMs), aligning a general-purpose model with downstream tasks through fine-tuning has become a central research focus. Selecting only high-quality examples for training has been shown to be one of the most effective ways to improve fine-tuning performance. However, prior work concentrates almost exclusively on data preprocessing: filtering and cleaning data before training begins. While the order and composition of training data during training have received little fine-grained attention. To fill this gap, our work proposed Fine-Grained Order Fine-Tuning, a fine-grained scheduling method of data order in epochs. Drawing on curriculum-learning principles, FOT defines data difficulty based on the relevance between the data and the model, and then performs dynamic scheduling of the training order in each epoch according to the difficulty. On both large-scale continued pre-training and small-scale supervised fine-tuning experiments, FOT has achieved an average 2.4{\%} improvement over baselines. Our study offers a new perspective on data governance in the fine-tuning phase."
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<abstract>With the rapid progress of large language models (LLMs), aligning a general-purpose model with downstream tasks through fine-tuning has become a central research focus. Selecting only high-quality examples for training has been shown to be one of the most effective ways to improve fine-tuning performance. However, prior work concentrates almost exclusively on data preprocessing: filtering and cleaning data before training begins. While the order and composition of training data during training have received little fine-grained attention. To fill this gap, our work proposed Fine-Grained Order Fine-Tuning, a fine-grained scheduling method of data order in epochs. Drawing on curriculum-learning principles, FOT defines data difficulty based on the relevance between the data and the model, and then performs dynamic scheduling of the training order in each epoch according to the difficulty. On both large-scale continued pre-training and small-scale supervised fine-tuning experiments, FOT has achieved an average 2.4% improvement over baselines. Our study offers a new perspective on data governance in the fine-tuning phase.</abstract>
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%0 Conference Proceedings
%T Fine-Grained Data Ordering Improves Fine-Tuning for Large Language Models
%A Hu, Xiaomeng
%A Tang, Yixuan
%A Li, Haoze
%A Chen, Hao
%A Zhang, Qi
%A Shen, Zhanming
%A Zhang, Yiming
%A Wang, Haobo
%A Zhao, Junbo
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F hu-etal-2026-fine
%X With the rapid progress of large language models (LLMs), aligning a general-purpose model with downstream tasks through fine-tuning has become a central research focus. Selecting only high-quality examples for training has been shown to be one of the most effective ways to improve fine-tuning performance. However, prior work concentrates almost exclusively on data preprocessing: filtering and cleaning data before training begins. While the order and composition of training data during training have received little fine-grained attention. To fill this gap, our work proposed Fine-Grained Order Fine-Tuning, a fine-grained scheduling method of data order in epochs. Drawing on curriculum-learning principles, FOT defines data difficulty based on the relevance between the data and the model, and then performs dynamic scheduling of the training order in each epoch according to the difficulty. On both large-scale continued pre-training and small-scale supervised fine-tuning experiments, FOT has achieved an average 2.4% improvement over baselines. Our study offers a new perspective on data governance in the fine-tuning phase.
%U https://aclanthology.org/2026.findings-acl.1021/
%P 20406-20418
Markdown (Informal)
[Fine-Grained Data Ordering Improves Fine-Tuning for Large Language Models](https://aclanthology.org/2026.findings-acl.1021/) (Hu et al., Findings 2026)
ACL
- Xiaomeng Hu, Yixuan Tang, Haoze Li, Hao Chen, Qi Zhang, Zhanming Shen, Yiming Zhang, Haobo Wang, and Junbo Zhao. 2026. Fine-Grained Data Ordering Improves Fine-Tuning for Large Language Models. In Findings of the Association for Computational Linguistics: ACL 2026, pages 20406–20418, San Diego, California, United States. Association for Computational Linguistics.