Audio note: this article contains 53 uses of latex notation, so the narration may be difficult to follow. There's a link to the original text in the episode description.

Ariana Azarbal*, Matthew A. Clarke*, Jorio Cocola*, Cailley Factor*, and Alex Cloud.

*Equal Contribution. This work was produced as part of the SPAR Spring 2025 cohort.

TL;DR: We benchmark seven methods to prevent emergent misalignment and other forms of misgeneralization using limited alignment data We demonstrate a consistent tradeoff between capabilities and alignment, highlighting the need for better methods to mitigate this tradeoff. Merely including alignment data in training data mixes is insufficient to prevent misalignment, yet a simple KL Divergence penalty on alignment data outperforms more sophisticated methods.

Narrow post-training can have far-reaching consequences on model behavior. Some are desirable, whereas others may be harmful. We explore methods enabling selective generalization.