@inproceedings{xu-etal-2026-unsupervised,
title = "Unsupervised Post-Training of Foundation Models: A Survey",
author = "Xu, Yijie and
Cai, Qianyi and
Yao, Huizai and
Wang, Yili and
Wang, Tianfu and
Yang, Cehao and
Yao, Xingbo and
Guo, Zhiyu and
Liu, Aiwei and
Hu, Xuming and
Guo, Weiyu and
Xiong, Hui",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.2044/",
pages = "43485--43504",
ISBN = "979-8-89176-462-0",
abstract = "Foundation-model post-training usually relies on human labels, preference data, stronger teachers, or executable verifiers. We study Unsupervised Post-Training (UPT): update-bearing adaptation on unlabeled inputs whose learning signal is derived from same-lineage model artifacts rather than an external oracle. We catalog 80 strict UPT methods and organize them by the object that supplies the update signal: a prediction statistic, a sample relation, a self-generated target, or an internal evaluator. Beyond inventory, we show how the choice of internal signal and task structure determines whether post-training improves the model or recursively amplifies error. An orthogonal Input Visibility {\texttimes} Update Persistence view maps deployment regimes and defines a unified framework for UPT selection and evaluation."
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<abstract>Foundation-model post-training usually relies on human labels, preference data, stronger teachers, or executable verifiers. We study Unsupervised Post-Training (UPT): update-bearing adaptation on unlabeled inputs whose learning signal is derived from same-lineage model artifacts rather than an external oracle. We catalog 80 strict UPT methods and organize them by the object that supplies the update signal: a prediction statistic, a sample relation, a self-generated target, or an internal evaluator. Beyond inventory, we show how the choice of internal signal and task structure determines whether post-training improves the model or recursively amplifies error. An orthogonal Input Visibility × Update Persistence view maps deployment regimes and defines a unified framework for UPT selection and evaluation.</abstract>
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%0 Conference Proceedings
%T Unsupervised Post-Training of Foundation Models: A Survey
%A Xu, Yijie
%A Cai, Qianyi
%A Yao, Huizai
%A Wang, Yili
%A Wang, Tianfu
%A Yang, Cehao
%A Yao, Xingbo
%A Guo, Zhiyu
%A Liu, Aiwei
%A Hu, Xuming
%A Guo, Weiyu
%A Xiong, Hui
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F xu-etal-2026-unsupervised
%X Foundation-model post-training usually relies on human labels, preference data, stronger teachers, or executable verifiers. We study Unsupervised Post-Training (UPT): update-bearing adaptation on unlabeled inputs whose learning signal is derived from same-lineage model artifacts rather than an external oracle. We catalog 80 strict UPT methods and organize them by the object that supplies the update signal: a prediction statistic, a sample relation, a self-generated target, or an internal evaluator. Beyond inventory, we show how the choice of internal signal and task structure determines whether post-training improves the model or recursively amplifies error. An orthogonal Input Visibility × Update Persistence view maps deployment regimes and defines a unified framework for UPT selection and evaluation.
%U https://aclanthology.org/2026.findings-emnlp.2044/
%P 43485-43504
Markdown (Informal)
[Unsupervised Post-Training of Foundation Models: A Survey](https://aclanthology.org/2026.findings-emnlp.2044/) (Xu et al., Findings 2026)
ACL
- Yijie Xu, Qianyi Cai, Huizai Yao, Yili Wang, Tianfu Wang, Cehao Yang, Xingbo Yao, Zhiyu Guo, Aiwei Liu, Xuming Hu, Weiyu Guo, and Hui Xiong. 2026. Unsupervised Post-Training of Foundation Models: A Survey. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 43485–43504, Budapest, Hungary. Association for Computational Linguistics.