Recursive generative retraining with heterogeneous rewards converges to a stable distribution satisfying a weighted Nash bargaining solution, preserving diversity under stated conditions.
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cs.LG 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
A dynamical systems analysis of constant-step SGD explains memorization in generative models by combining two-time-scale dynamics with a collapse model.
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Curated Synthetic Data Doesn't Have to Collapse: A Theoretical Study of Generative Retraining with Pluralistic Preferences
Recursive generative retraining with heterogeneous rewards converges to a stable distribution satisfying a weighted Nash bargaining solution, preserving diversity under stated conditions.
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Adynamical systems view of training generativemodels and the memorization phenomenon
A dynamical systems analysis of constant-step SGD explains memorization in generative models by combining two-time-scale dynamics with a collapse model.