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arXiv · 2608.24982

Unsupervised Post-Training of Foundation Models: A Survey

Yijie Xu·Qianyi Cai·Huizai Yao·Yili Wang·Tianfu Wang·Cehao Yang·Xingbo Yao·Zhiyu Guo·Aiwei Liu·Xuming Hu·Weiyu Guo·Hui Xiong
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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 $\times$ Update Persistence view maps deployment regimes and defines a unified framework for UPT selection and evaluation.

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Yijie Xu, Qianyi Cai, Huizai Yao, Yili Wang, Tianfu Wang, Cehao Yang, Xingbo Yao, Zhiyu Guo, Aiwei Liu, Xuming Hu, Weiyu Guo, Hui Xiong. 2026-08-25. Unsupervised Post-Training of Foundation Models: A Survey. https://arxiv.org/abs/2608.24982

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