Reinforcement learning breaks distillation-based model defenses
September 27, 2026
Research shows that current defenses against model distillation attacks fail when attackers apply reinforcement learning to the distilled model. This demonstrates that a threat model ignoring post-distillation training provides a false sense of security.
HOW THIS AFFECTS YOU
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builderYou should account for RL-driven improvements in potential adversarial or cloned models.
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policyThis suggests that current safety and IP protections for frontier models are insufficient.