Safe at One Loop, Risky at Another: Aligning Safety Across Recurrent Depths in Looped Language Models
Looped Language Models (LoopLMs) expose outputs at multiple inference depths, creating safety challenges: safety varies across depths and jailbreaks transfer between them. SafeBridge is a preference optimization method that aligns these cross-depth safety issues by combining depth-specific modulation with selective state bridging, significantly reducing attack success rates while maintaining model utility.