Episode Details

Anna and Ed are co-first authors for this work. We’re presenting these results as a research update for a continuing body of work, which we hope will be interesting and useful for others working on related topics.

TL;DR

• We investigate why models become misaligned in diverse contexts when fine-tuned on narrow harmful datasets (emergent misalignment), rather than learning the specific narrow task.
• We successfully train narrowly misaligned models using KL regularization to preserve behavior in other domains. These models give bad medical advice, but do not respond in a misaligned manner to general non-medical questions.
• We use this method to train narrowly misaligned steering vectors, rank 1 LoRA adapters and rank 32 LoRA adapters, and compare these to their generally misaligned counterparts.
  • The steering vectors are particularly interpretable, we introduce Training Lens as a tool for analysing the revealed residual stream geometry.
• The general misalignment solution is consistently more [...]

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Outline:

(00:27) TL;DR

(02:03) Introduction

(04:03) Training a Narrowly Misaligned Model

(07:13) Measuring Stability and Efficiency

(10:00) Conclusion

The original text contained 7 footnotes which were omitted from this narration.

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First published:
July 14th, 2025

Source:
https://www.lesswrong.com/posts/gLDSqQm8pwNiq7qst/narrow-misalignment-is-hard-emergent-misalignment-is-easy

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Narrated by TYPE III AUDIO.

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