<ul><li>Data pruning techniques are essential for training advanced machine learning models on massive datasets.</li><li>Existing pruning techniques often require a full initial training pass, which can be inefficient for single training runs.</li><li>A new importance score extrapolation framework has been introduced to predict sample importance with minimal data usage.</li><li>The framework demonstrates effectiveness across different datasets and training paradigms, offering scalability for expensive score calculation methods.</li></ul>

Effective Data Pruning through Score Extrapolation

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