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Unsupervisedly uncover and neutralize hidden model misalignments transferred during distillation, even without prior knowledge of the problematic trait.
Language model explanations unlock structured credit assignment, dramatically improving RL sample efficiency in long-horizon generative tasks.
A dual-threshold targeting policy can cut recommendation impressions by 7% without sacrificing user engagement, showcasing the power of causal modeling in recommendation systems.
