Unsupervised Post-Training of Foundation Models: A Survey
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Computer Science > Computation and Language
Title:Unsupervised Post-Training of Foundation Models: A Survey
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.
| Comments: | Accepted to Findings of EMNLP 2026. 20 pages, 3 figures, 8 tables |
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG); Multimedia (cs.MM) |
| Cite as: | arXiv:2608.24982 [cs.CL] |
| (or arXiv:2608.24982v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2608.24982
arXiv-issued DOI via DataCite (pending registration)
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