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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2507.16178 |
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| _version_ | 1866911069552771072 |
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| author | Yu, Yang Han, Kai Zhou, Hang Tang, Yehui Huang, Kaiqi Wang, Yunhe Tao, Dacheng |
| author_facet | Yu, Yang Han, Kai Zhou, Hang Tang, Yehui Huang, Kaiqi Wang, Yunhe Tao, Dacheng |
| contents | While large-scale training data is fundamental for developing capable large language models (LLMs), strategically selecting high-quality data has emerged as a critical approach to enhance training efficiency and reduce computational costs. Current data selection methodologies predominantly rely on static, training-agnostic criteria, failing to account for the dynamic model training and data interactions. In this paper, we propose a new Data Weighting Model (DWM) to adjust the weight of selected data within each batch to achieve a dynamic data utilization during LLM training. Specially, to better capture the dynamic data preference of the trained model, a bi-level optimization framework is implemented to update the weighting model. Our experiments demonstrate that DWM enhances the performance of models trained with randomly-selected data, and the learned weighting model can be transferred to enhance other data selection methods and models of different sizes. Moreover, we further analyze how a model's data preferences evolve throughout training, providing new insights into the data preference of the model during training. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_16178 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | LLM Data Selection and Utilization via Dynamic Bi-level Optimization Yu, Yang Han, Kai Zhou, Hang Tang, Yehui Huang, Kaiqi Wang, Yunhe Tao, Dacheng Machine Learning Artificial Intelligence While large-scale training data is fundamental for developing capable large language models (LLMs), strategically selecting high-quality data has emerged as a critical approach to enhance training efficiency and reduce computational costs. Current data selection methodologies predominantly rely on static, training-agnostic criteria, failing to account for the dynamic model training and data interactions. In this paper, we propose a new Data Weighting Model (DWM) to adjust the weight of selected data within each batch to achieve a dynamic data utilization during LLM training. Specially, to better capture the dynamic data preference of the trained model, a bi-level optimization framework is implemented to update the weighting model. Our experiments demonstrate that DWM enhances the performance of models trained with randomly-selected data, and the learned weighting model can be transferred to enhance other data selection methods and models of different sizes. Moreover, we further analyze how a model's data preferences evolve throughout training, providing new insights into the data preference of the model during training. |
| title | LLM Data Selection and Utilization via Dynamic Bi-level Optimization |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2507.16178 |