@inproceedings{lai-etal-2025-survey,
    title = "A Survey of Post-Training Scaling in Large Language Models",
    author = "Lai, Hanyu  and
      Liu, Xiao  and
      Gao, Junjie  and
      Cheng, Jiale  and
      Qi, Zehan  and
      Xu, Yifan  and
      Yao, Shuntian  and
      Zhang, Dan  and
      Du, Jinhua  and
      Hou, Zhenyu  and
      Lv, Xin  and
      Huang, Minlie  and
      Dong, Yuxiao  and
      Tang, Jie",
    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://preview.aclanthology.org/ingestion-acl-25/2025.acl-long.140/",
    pages = "2771--2791",
    ISBN = "979-8-89176-251-0",
    abstract = "Large language models (LLMs) have achieved remarkable proficiency in understanding and generating human natural languages, mainly owing to the ``scaling law'' that optimizes relationships among language modeling loss, model parameters, and pre-trained tokens. However, with the exhaustion of high-quality internet corpora and increasing computational demands, the sustainability of pre-training scaling needs to be addressed. This paper presents a comprehensive survey of post-training scaling, an emergent paradigm aiming to relieve the limitations of traditional pre-training by focusing on the alignment phase, which traditionally accounts for a minor fraction of the total training computation. Our survey categorizes post-training scaling into three key methodologies: Supervised Fine-tuning (SFT), Reinforcement Learning from Feedback (RLxF), and Test-time Compute (TTC). We provide an in-depth analysis of the motivation behind post-training scaling, the scalable variants of these methodologies, and a comparative discussion against traditional approaches. By examining the latest advancements, identifying promising application scenarios, and highlighting unresolved issues, we seek a coherent understanding and map future research trajectories in the landscape of post-training scaling for LLMs."
}