Why Self-Training Helps and Hurts: Denoising vs. Signal Forgetting
Iterative self-training (self-distillation) repeatedly refits a model on pseudo-labels generated by its own predictions. We study this procedure in overparameterized linear regression: an initial estimator is trained on noisy labels, and each subsequent iterate is trained on fresh covariates with noiseless pseudo-labels from the previous model. In the high-dimensional regime, we derive deterministic-equivalent recursions for the prediction risk and effective noise across iterations, and prove that the empirical quantities concentrate sharply around these limits. The recursion separates two competing forces: a systematic component that grows with iteration due to progressive signal forgett...
| Authors | Mingqi Wu, Archer Y. Yang, Qiang Sun |
|---|---|
| Primary category | stat.ML |
| Published | 15 February 2026 |
| Last updated at source | 15 February 2026 |
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