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Research from institutions including the University of Oxford has mathematically proven that model collapse—a form of degradation—is irreversible after multiple generations of training iterations.
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9/20/2026
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UnverifiedExperiments show that after only a few generations of self-training, the variance of model outputs drops sharply and the text tends toward homogeneity72% similarUnverifiedResearch published in 2023, such as "The Curse of Recursion," shows that when models are repeatedly trained on their own generated data, the data distribution gradually degrades, diversity continuously decreases, and model capabilities systematically decline (model collapse).69% similarUnverified文生图模型的理解本质上是大规模统计关联，面对训练集中稀少出现的长尾概念时生成质量会显著下滑68% similarUnverified随着模型规模从百万参数扩展至千亿参数，不理解底层数学的工程师越来越难以诊断训练不稳定、梯度爆炸或模式崩塌等问题66% similarVerifiedOverfitting refers to the phenomenon where a model performs excellently on training data but its performance drops significantly on unseen test data.66% similar
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