Stable Signal Principle Explains Retraining Convergence in Performative Prediction
A new theoretical framework, the stable signal principle, demonstrates that retraining predictive models converges to a stable direction when a nonzero model-independent signal exists, even if the model's influence on the data is strong. The analysis generalizes to affine retraining operators and applies to language model training with synthetic data, offering a unified explanation for stability in performative prediction loops.
Why it matters: This work provides a theoretical explanation for the convergence and stability of retraining in real-world learning systems, including language models, even under strong feedback effects.
Full story at: arXiv Statistical ML ↗