LARK: Learnability-Grounded Trajectory Selection for Efficient Reasoning Distillation 事件

LARK: Learnability-Grounded Trajectory Selection for Efficient Reasoning Distillation arXiv:2605.30651v1 Announce Type: cross 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 learnabil