A Survey on Efficient Large Language Model Training: From Data-centric Perspectives

• URL: http://arxiv.org/abs/2510.25817v1
• Date: Wed, 29 Oct 2025 17:01:55 GMT
• Title: A Survey on Efficient Large Language Model Training: From Data-centric Perspectives
• Authors: Junyu Luo, Bohan Wu, Xiao Luo, Zhiping Xiao, Yiqiao Jin, Rong-Cheng Tu, Nan Yin, Yifan Wang, Jingyang Yuan, Wei Ju, Ming Zhang,
• Abstract summary: We present the first systematic survey of data-efficient Large Language Models post-training from a data-centric perspective.<n>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.<n>We hope our work inspires further exploration into maximizing the potential of data utilization in large-scale model training.
• Score: 42.897899343082806
• License: http://creativecommons.org/licenses/by/4.0/
• 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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