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arXiv Machine Learning By Yuchen Wu (University of Washington), Jerry Wei (Anthropic)

Scaling Laws for Safety Probes

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The paper introduces two scaling laws that predict the performance of safety probes—tools used to monitor language models for harmful behavior—based on model size and the number of labeled examples. By creating six large safety datasets and training probes across multiple model families, the authors show that probe error decreases as a power law with model size and further improves with more examples, with the improvement rate increasing for larger models. These laws enable researchers to forecast probe accuracy and decide whether to invest in more data or a larger model to achieve a desired error threshold.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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