AI RESEARCH
LARK: Learnability-Grounded Trajectory Selection for Efficient Reasoning Distillation
arXiv CS.AI
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ArXi:2605.30651v1 Announce Type: cross 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. LARK selects trajectories that the student can distribution.