Research5h ago
The Misnomer of Distillation Attacks
The term distillation attacks inaccurately describes current model training trends. Nathan Lambert argues that using synthetic data from larger models to train smaller ones is a standard practice, not a malicious exploit. This semantic confusion obscures the actual technical challenges of data quality. Researchers must distinguish between intentional model distillation and adversarial attempts to steal proprietary weights.