Scaling AI Models: Combating Collapse with Reinforced Synthetic Data

As AI-generated data increasingly supplements or even replaces human-annotated data, concerns have arisen about the degradation in model performance when models are iteratively trained on synthetic data. Model collapse refers to this phenomenon where a model’s performance deteriorates significantly when trained on synthesized data generated using the model. This problem is significant because it hinders […]

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