Retraining Seeks Stable Signals
Predictive models deployed at scale influence future data, a phenomenon called performativity. And there is always one way to cope: Train the model on new data, deploy it again, and repeat. This process, called retraining or repeated risk minimization, creates a feedback loop between model and data that real-world learning systems can't avoid. Results on performative prediction shed light on this dynamic: If the model's influence on the data is small, retraining reaches a fixed point. What remains open is why fixed points should naturally exist, and what governs retraining when the model's influence is strong. In this work we develop a new perspective on retraining -- the stable signal pr...
| Authors | Moritz Hardt |
|---|---|
| Primary category | stat.ML |
| Published | 17 July 2026 |
| Last updated at source | 17 July 2026 |
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