“Optimiser Choice Can Amplify or Suppress Emergent Misalignment” by Jason R Brown, Patrick Leask, Lev McKinney

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 [...]

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Outline:

(00:44) Introduction

(01:34) What we found

(04:41) Future directions

(07:39) Acknowledgements

The original text contained 4 footnotes which were omitted from this narration.

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First published:
July 9th, 2026

Source:
https://www.lesswrong.com/posts/Wq6CaAbiixoCEzbat/optimiser-choice-can-amplify-or-suppress-emergent-1

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Narrated by TYPE III AUDIO.

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