Learnability-Grounded Trajectory Selection for Efficient Reasoning Distillation
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LARK: Learnability-Grounded Trajectory Selection for Efficient Reasoning Distillation
Announce Type: new Abstract: We study trajectory selection for reasoning distillation, where teacher-generated reasoning trajectories are selectively used as supervision for a student model. Existing methods rely on heuristics such as trajectory quality or model confidence, but they often overlook whether a trajectory is learnable by the student. In this paper, we present LARK, a learnability-grounded method for reasoning trajectory selection.