Shidan Javaheri

Distillation Defenses Easily Break After Reinforcement Learning

Distillation attacks copy the reasoning capabilities of closed-source large language models, allowing bad actors to replicate state-of-the-art performance at low cost. Attackers systematically collect a large volume of frontier model reasoning traces and then train (i.e.,"distill") their own models on these traces. Exi...