AINeutralarXiv – CS AI · 9h ago6/10
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Curated Synthetic Data Doesn't Have to Collapse: A Theoretical Study of Generative Retraining with Pluralistic Preferences
Researchers demonstrate that model collapse during recursive synthetic data retraining can be prevented by curating outputs across multiple reward functions rather than a single objective. The study provides theoretical proof that diverse preference aggregation leads to stable distributions satisfying Nash bargaining solutions, offering a framework for maintaining output diversity in AI training loops.