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LARK proposes a learnability-grounded method for selecting reasoning trajectories in LLM distillation, employing a learnability factor and χ²-regularized selection policy that balances efficiency and generalization, consistently outperforming baselines across models and tasks.
Weasel is a trajectory selection method for offline training of web agents that improves out-of-domain generalization by balancing importance and diversity. It achieves up to 12.5x training speedups and improved performance across several benchmarks.