AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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This work introduces SeeQ (Subtask-elicited Q-functions), which instead learns Q-values for the currently active subtask, which substantially improves best-of-N policy steering on real-world manipulation tasks.
The resulting model SEAR deliberately engages in harmful reasoning as exploration while reliably flipping back to a safe answer, demonstrating that this behavior helps mitigate over-refusal and defend against attacks that directly manipulate the reasoning to be harmful.
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