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This work forms data rewriting as a policy-learning problem and trains a lightweight LoRA rewriting policy with reinforcement learning that optimizes question-answering-style distributional alignment and semantic diversity under a hard task-consistency gate, producing verified supervision for downstream SFT.
These findings establish intrinsic risk auditing as an open challenge for agent safety, where intrinsic failures remain latent, propagate across long-horizon execution, and eventually lead to high-consequence outcomes.
This work focuses on a continuation-triggered jailbreak phenomenon, whereby simply relocating a continuation-triggered instruction suffix can substantially increase jailbreak success rates and proposes Head Competition Steering (HCS), a mechanistically grounded inference-time strategy that explicitly leverages the comp...
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