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Automatic attribution metrics for LLMs can flip rankings across datasets, leading to misleading evaluations that could cost researchers dearly in decision-making.
Exact-match retrieval metrics can mislead assessments of policy utility, as retrieved clauses perform nearly as well as gold-standard ones in decision-making tasks.
Direct token-level self-distillation can backfire, but Sibling-Guided Credit Distillation redefines credit assignment to enhance long-horizon tool-use without amplifying harmful behaviors.
