@inproceedings{luo-etal-2025-survey,
title = "A Survey on Efficient Large Language Model Training: From Data-centric Perspectives",
author = "Luo, Junyu and
Wu, Bohan and
Luo, Xiao and
Xiao, Zhiping and
Jin, Yiqiao and
Tu, Rong-Cheng and
Yin, Nan and
Wang, Yifan and
Yuan, Jingyang and
Ju, Wei and
Zhang, Ming",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.acl-long.1493/",
doi = "10.18653/v1/2025.acl-long.1493",
pages = "30904--30920",
ISBN = "979-8-89176-251-0",
abstract = "Post-training of Large Language Models (LLMs) is crucial for unlocking their task generalization potential and domain-specific capabilities. However, the current LLM post-training paradigm faces significant data challenges, including the high costs of manual annotation and diminishing marginal returns on data scales. Therefore, achieving data-efficient post-training has become a key research question. In this paper, we present the first systematic survey of data-efficient LLM post-training from a data-centric perspective. We propose a taxonomy of data-efficient LLM post-training methods, covering data selection, data quality enhancement, synthetic data generation, data distillation and compression, and self-evolving data ecosystems. We summarize representative approaches in each category and outline future research directions. By examining the challenges in data-efficient LLM post-training, we highlight open problems and propose potential research avenues. We hope our work inspires further exploration into maximizing the potential of data utilization in large-scale model training. Paper List: https://github.com/luo-junyu/Awesome-Data-Efficient-LLM"
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<abstract>Post-training of Large Language Models (LLMs) is crucial for unlocking their task generalization potential and domain-specific capabilities. However, the current LLM post-training paradigm faces significant data challenges, including the high costs of manual annotation and diminishing marginal returns on data scales. Therefore, achieving data-efficient post-training has become a key research question. In this paper, we present the first systematic survey of data-efficient LLM post-training from a data-centric perspective. We propose a taxonomy of data-efficient LLM post-training methods, covering data selection, data quality enhancement, synthetic data generation, data distillation and compression, and self-evolving data ecosystems. We summarize representative approaches in each category and outline future research directions. By examining the challenges in data-efficient LLM post-training, we highlight open problems and propose potential research avenues. We hope our work inspires further exploration into maximizing the potential of data utilization in large-scale model training. Paper List: https://github.com/luo-junyu/Awesome-Data-Efficient-LLM</abstract>
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%0 Conference Proceedings
%T A Survey on Efficient Large Language Model Training: From Data-centric Perspectives
%A Luo, Junyu
%A Wu, Bohan
%A Luo, Xiao
%A Xiao, Zhiping
%A Jin, Yiqiao
%A Tu, Rong-Cheng
%A Yin, Nan
%A Wang, Yifan
%A Yuan, Jingyang
%A Ju, Wei
%A Zhang, Ming
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-251-0
%F luo-etal-2025-survey
%X Post-training of Large Language Models (LLMs) is crucial for unlocking their task generalization potential and domain-specific capabilities. However, the current LLM post-training paradigm faces significant data challenges, including the high costs of manual annotation and diminishing marginal returns on data scales. Therefore, achieving data-efficient post-training has become a key research question. In this paper, we present the first systematic survey of data-efficient LLM post-training from a data-centric perspective. We propose a taxonomy of data-efficient LLM post-training methods, covering data selection, data quality enhancement, synthetic data generation, data distillation and compression, and self-evolving data ecosystems. We summarize representative approaches in each category and outline future research directions. By examining the challenges in data-efficient LLM post-training, we highlight open problems and propose potential research avenues. We hope our work inspires further exploration into maximizing the potential of data utilization in large-scale model training. Paper List: https://github.com/luo-junyu/Awesome-Data-Efficient-LLM
%R 10.18653/v1/2025.acl-long.1493
%U https://aclanthology.org/2025.acl-long.1493/
%U https://doi.org/10.18653/v1/2025.acl-long.1493
%P 30904-30920
Markdown (Informal)
[A Survey on Efficient Large Language Model Training: From Data-centric Perspectives](https://aclanthology.org/2025.acl-long.1493/) (Luo et al., ACL 2025)
ACL
- Junyu Luo, Bohan Wu, Xiao Luo, Zhiping Xiao, Yiqiao Jin, Rong-Cheng Tu, Nan Yin, Yifan Wang, Jingyang Yuan, Wei Ju, and Ming Zhang. 2025. A Survey on Efficient Large Language Model Training: From Data-centric Perspectives. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 30904–30920, Vienna, Austria. Association for Computational Linguistics.