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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 learn efficiently while preserving the generalization of the full training distribution. At the core of LARK is a learnability factor $\\rho$, which characterizes the rate at which the student's training loss decreases. To estimate this rate efficiently and maintain generalization, we introduce a learnability proxy and a $\\chi^2$-regularized selection policy that balances learnability and distributional coverage, both with strong theoretical guarantees on their estimation error. 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To estimate this rate efficiently and maintain generalization, we introduce a learnability proxy and a $\\chi^2$-regularized selection policy that balances learnability and distributional coverage, both with strong theoretical guarantees on their estimation error. 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