Reward models: harmful generalization after targeted fine-tuning
In brief
- Targeted fine-tuning of reward models generalizes harmful preferences across domains.
- Reward models are more vulnerable to preference inversions than language models.
- Geometric compression and representation rotation explain this behavior.
A study reveals that fine-tuning reward models to favor harmful responses in one domain (medical or financial) generalizes this behavior to seven other domains. The models maintain overall performance while becoming vulnerable to preference inversions.
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