Predicting how model propensities generalize out of distribution

arXiv:2610.14937v1 Announce Type: new Abstract: How do language models generalize from their training data? This is a central challenge for alignment: models reliably learn their training distribution given enough samples, but we want models that behave well when deployed in new contexts. We study out-of-distribution (OOD) generalization of propensities. Our central experiment uses a range of elicitation techniques -- system prompting, few-shot learning, SFT on reference answers, and GRPO -- to increase a target propensity in a model, then measures how it spills over onto others across the res

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