用外部验证器防止生成数据训练导致模型退化，实现短期提升与长期稳定。
Escaping Model Collapse via Synthetic Data Verification: Near-term Improvements and Long-term Convergence
- 引入外部验证器筛选合成数据，避免模型自我迭代恶化。
- 实验验证线性回归、VAE和小模型微调均实现初期性能提升。
- 适合关注生成模型稳定性与数据质量的开发者与研究者。
合成数据被广泛用于训练前沿生成模型，但近期研究指出，将生成模型反复在自身生成的合成数据上训练可能导致性能持续下降，即模型崩溃现象。本文研究如何修改合成数据重训练流程以避免模型崩溃，甚至逆转这一趋势。核心发现是：通过外部合成数据验证器（人类或更优模型）注入信息，可有效防止模型崩溃。理论分析基于基础线性回归设定，表明验证器引导的重训练能带来近似性能提升，但长期将使参数收敛至验证器的“知识中心”。理论预测：除非验证器完全可靠，否则早期增益会停滞甚至反转。多项实验涵盖线性回归、基于MNIST训练的变分自编码器（VAEs）以及在XSUM任务上微调SmolLM2-135M模型，均验证了上述理论洞察。
原文摘要 · Abstract (English)
Synthetic data has been increasingly used to train frontier generative models. However, recent studies raise key concerns that iteratively retraining a generative model on its self-generated synthetic data may keep deteriorating model performance, a phenomenon often coined model collapse. In this paper, we investigate ways to modify the synthetic retraining process to avoid model collapse, and even possibly help reverse the trend from collapse to improvement. Our key finding is that by injecting information through an external synthetic data verifier, whether a human or a better model, synthetic retraining will not cause model collapse. Specifically, we situate our theoretical analysis in the fundamental linear regression setting, showing that verifier-guided retraining can yield near-term improvements, but ultimately drives the parameter estimate to the verifier's "knowledge center" in the long run. Our theory further predicts that, unless the verifier is perfectly reliable, these early gains will plateau and may even reverse. Indeed, our experiments across linear regression, Variational Autoencoders (VAEs) trained on MNIST, and fining-tuning SmolLM2-135M on the XSUM task confirm these theoretical insights.
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