Why Do Naive SFT Filters For Safety Properties Fail?

Short summary

Google DeepMind researchers investigate why supervised fine-tuning data filtering fails to remove unwanted safety behaviors like negative emotion, date confusion, and blackmail. They propose seven hypotheses including persona lock-in and subliminal learning. A post-training diffing methodology isolates which SFT components cause trait transfer across models.

• •SFT data filtering frequently fails to remove undesired safety properties, even with targeted filtering
• •Seven hypotheses explain the failure, including pretraining persona lock-in and subliminal learning from teacher models
• •Post-training diffing pipeline methodology enables systematic identification of where safety issues originate in training

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