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Behind Harmful Compliance: Behavioral and Mechanistic Divergence Across LLM Jailbreaks
Open-weight language models can be rendered unsafe through several parameter-level interventions, yet models with matched harmful compliance can exhibit fundamentally different failure modes. We compare harmful supervised fine-tuning (SFT), harmful reinforcement learning with verifiable rewards (RLV
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