What Shapes Emergent Misalignment? Insights from Training Dynamics, Model Priors, and Data

Emergent misalignment (EM) is a phenomenon in which models generalize with narrow fine-tuning, leading to broad (yet uneven) misalignment across evaluation questions. We study EM and its variability directly through the components of fine-tuning: training dynamics, model priors, and data. (1) We first explored how in-domain training loss relates to out-of-domain alignment scores across datasets and model families. Then, we tried to induce potential alternative local minima through different lear

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ethics.ai (18 June 2026), “What Shapes Emergent Misalignment? Insights from Training Dynamics, Model Priors, and Data,” evidence record 788, https://ethics.ai/record/788 (originally published by arXiv).

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