Optimiser Choice Can Amplify or Suppress Emergent Misalignment

This is a linkpost for https://arxiv.org/abs/2606.31591 . Work done with Patrick Leask and Lev McKinney during the Astra Fellowship. TL;DR: Optimiser choice strongly influences emergent misalignment, while model size and family seem to barely matter. Optimisers that concentrate the LoRA update into fewer directions degrade alignment more, but regularising towards a flatter spectrum can mitigate this and improve alignment. There are some follow-up directions I (Jason) would be happy to advise or mentor on. Introduction Emergent misalignment (EM)—where fine-tuning on a narrow misaligned task like writing insecure code produces broadly misaligned behaviour—is known to be sensitive to training choices: misalignment rates vary several-fold across models trained on the same data, modest learning-rate and LoRA-scaling changes can more than double them , and much of the effect seems to come from training past task convergence . However, this sensitivity hadn't been systematically characterised: existing work varies the training data, length of training, or the model, while holding the other important features of the training process fixed. We instead cast a much wider net, and found that the optimiser is by far the most important factor we tested—more important than the model, and often even more important than the data. [1] What we found Model size and family matter much less than people seem to believe. Across 12 models from three families (Gemma, Llama, Qwen; 270M–235B), everythi…