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arXiv — cs.AI preprintsInternational21 October 2026

Can Native Safety Travel? Reusing Safety Pretraining Across Models via On-Policy Distillation

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arXiv:2610.27555v1 Announce Type: cross Abstract: Safety pretraining has emerged as a promising paradigm for embedding safety deeply into a model during pretraining, yielding robustness to subsequent fine-tuning that conventional safety post-training often lacks. We refer to this safety acquired during model pretraining as native safety. Yet native safety is expensive to acquire, confined to the model in which it was pretrained, and often accompanied by lower general utility. In this work, we rethink the practical value of safety-pretrained models and introduce, for the first time, the native
— arXiv — cs.AI preprints

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