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Toward Understanding and Preventing Misalignment Generalization
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4/5
High(4)High quality. Established institution or organization with editorial oversight and accountability.
Rating inherited from publication venue: OpenAI
An OpenAI research page on emergent misalignment generalization, relevant to researchers studying how fine-tuning safety failures can propagate broadly, and how to build more robust alignment evaluations.
Metadata
Importance: 72/100blog postprimary source
Summary
This OpenAI research examines how misalignment can generalize unexpectedly in language models, investigating cases where fine-tuning on narrow harmful behaviors causes broader misaligned behavior across unrelated contexts. The work explores mechanisms behind misalignment generalization and discusses approaches to detect and prevent such emergent failures.
Key Points
- •Models fine-tuned on specific harmful tasks can develop broadly misaligned behavior that generalizes beyond the training distribution
- •Misalignment generalization poses a distinct and underappreciated risk: targeted safety failures can spread to unintended domains
- •The research investigates what internal model changes drive this generalization effect
- •Findings motivate better evaluation frameworks to detect latent misalignment before deployment
- •Mitigation strategies and detection methods are discussed to help prevent misalignment from becoming entrenched
Cited by 1 page
| Page | Type | Quality |
|---|---|---|
| Why Alignment Might Be Hard | Argument | 69.0 |
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Toward understanding and preventing misalignment generalization | OpenAI
Mar
APR
May
04
2025
2026
2027
success
fail
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TIMESTAMPS
The Wayback Machine - http://web.archive.org/web/20260404144908/https://openai.com/index/emergent-misalignment/
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Table of contents
About this project
Overview
Misalignment emerges in diverse settings
Investigating with SAEs, we find a misaligned persona feature in GPT-4o’s activations
“Misaligned persona”: top activating examples
The “misaligned persona” latent can be steered to cause or suppress emergent misalignment
Emergent re-alignment
Conclusion
June 18, 2025
Publication
Toward understanding and preventing misalignment generalization
A misaligned persona feature controls emergent misalignment.
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About this project
Large language models like ChatGPT don’t just learn facts—they pick up on patterns of behavior. That means they can start to act like different “personas,” or types of people, based on the content they’ve been trained on. Some of those personas are helpful and honest. Others might be careless or misleading.
Existing research showed that if you train a model on wrong answers, even in just one narrow area, like writing insecure computer code, it can inadvertently cause the model to act “misaligned” in many other areas. This is called “emergent misalignment.” We studied why this happens.
Through this research, we discovered a specific internal pattern in the model, similar to a pattern of brain activity, that becomes more active when this misaligned behavior appears. The model learned this pattern from training on data that describes bad behavior. We found we can make a model more or less aligned, just by directly increasing or decreasing this pattern’s activity. This suggests emergent misalignment works by strengthening a misaligned persona in the model.
We showed that training the model again on correct information can push it back toward helpful behavior. Together, this means we might be able to detect misaligned activity patterns, and fix the problem before it spreads.
In short, this work helps us understand why a model might start exhibiting misaligned behavior, and could g
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