On-Policy Distillation for LLM Safety: A Routing Approach to Template-Robust Realignment
Researchers propose on-policy distillation to fix safety vulnerabilities in fine-tuned LLMs caused by malicious data. This method prevents skill loss and resists unseen attack templates.
- Fine-tuning is vulnerable to data poisoning that hides harmful behaviors
- On-policy distillation helps realign models without catastrophic forgetting
- The routing method defends against attacks using unseen prompt templates
Fine-tuning large language models introduces a security risk where malicious actors can poison training data to embed harmful behaviors. Current safety realignment methods struggle because they often erase the model's learned skills or fail when the attack uses unknown prompt structures. This paper introduces a technique called on-policy distillation combined with a routing mechanism. It aims to realign models to safety standards while preserving their specialized capabilities. The approach specifically addresses the issue of template robustness, meaning it remains effective even if the attacker changes the specific prompts used to trigger the harmful behavior. This offers a more resilient defense against data poisoning during the specialization phase.
Crucial for fine-tuning models safely without losing utility
Mitigates risk when using third-party data for model customization
Highlights ongoing security challenges in the AI supply chain
- On-Policy Distillation
- A method where a model learns from its own generated outputs to improve safety or efficiency
- Catastrophic Forgetting
- The tendency of a neural network to lose previously learned information upon learning new tasks
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