Ed and Anna are co-first authors on this work.

Emergent Misalignment (EM) showed that fine-tuning LLMs on insecure code caused them to become broadly misaligned. We show this is a robust and safety-relevant result, and open-source improved model organisms to accelerate future work.
Using 3 new datasets, we train small EM models which are misaligned 40% of the time, and coherent 99% of the time, compared to 6% and 69% prior.
We demonstrate EM in a 0.5B parameter model, and across Qwen, Llama and Gemma model families.
We show EM occurs in full finetuning, but also that it is possible with a single rank-1 LoRA adapter.
We open source all code, datasets, and finetuned models on GitHub and HuggingFace. Full details are in our paper, and we also present interpretability results in a parallel post.

Emergent Misalignment found that fine-tuning models on narrowly misaligned data, such as insecure code [...]

---

Outline:

(00:16) TL;DR

(01:19) Introduction

(03:25) Coherent Emergent Misalignment

(07:02) EM with 0.5B Parameters

(08:11) EM with a Full Supervised Finetune

(09:13) EM with a Single Rank 1 LoRA Adapter

(10:01) Future Work

(11:05) Contributions

(11:33) Acknowledgments

The original text contained 6 footnotes which were omitted from this narration.

---

First published:
June 16th, 2025

Source:
https://www.lesswrong.com/posts/yHmJrDSJpFaNTZ9Tr/model-organisms-for-emergent-misalignment

---

---

Images from the article:

Apple Podcasts and Spotify do not show images in the episode description. Try Pocket Casts, or another podcast app.

“Model Organisms for Emergent Misalignment” by Anna Soligo, Edward Turner, Mia Taylor, Senthooran Rajamanoharan, Neel Nanda

LessWrong (30+ Karma)