Model Spec Midtraining: Improving How Alignment Training Generalizes
Some frontier AI developers aim to align language models to a Model Spec or Constitution that describes the intended model behavior. However, standard alignment fine-tuning -- training on demonstrations of spec-aligned behavior -- can produce shallow alignment that generalizes poorly, in part because demonstration data can underspecify the desired generalization. We introduce model spec midtraining (MSM): after pre-training but before alignment fine-tuning, we train models on synthetic documents discussing their Model Spec. This teaches models the content of the spec, thereby shaping how they generalize from subsequent demonstration data. For example, a model fine-tuned only to express ce...
| Authors | Chloe Li, Nevan Wichers, Sara Price et al. |
|---|---|
| Primary category | cs.AI |
| Published | 3 May 2026 |
| Last updated at source | 22 May 2026 |
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