@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://aclanthology.org/2025.acl-long.140/",
doi = "10.18653/v1/2025.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."
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<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.</abstract>
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%0 Conference Proceedings
%T A Survey of Post-Training Scaling in Large Language Models
%A Lai, Hanyu
%A Liu, Xiao
%A Gao, Junjie
%A Cheng, Jiale
%A Qi, Zehan
%A Xu, Yifan
%A Yao, Shuntian
%A Zhang, Dan
%A Du, Jinhua
%A Hou, Zhenyu
%A Lv, Xin
%A Huang, Minlie
%A Dong, Yuxiao
%A Tang, Jie
%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 lai-etal-2025-survey
%X 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.
%R 10.18653/v1/2025.acl-long.140
%U https://aclanthology.org/2025.acl-long.140/
%U https://doi.org/10.18653/v1/2025.acl-long.140
%P 2771-2791
Markdown (Informal)
[A Survey of Post-Training Scaling in Large Language Models](https://aclanthology.org/2025.acl-long.140/) (Lai et al., ACL 2025)
ACL
- Hanyu Lai, Xiao Liu, Junjie Gao, Jiale Cheng, Zehan Qi, Yifan Xu, Shuntian Yao, Dan Zhang, Jinhua Du, Zhenyu Hou, Xin Lv, Minlie Huang, Yuxiao Dong, and Jie Tang. 2025. A Survey of Post-Training Scaling in Large Language Models. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 2771–2791, Vienna, Austria. Association for Computational Linguistics.