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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.
We propose LIFT, a learnability-informed fine-tuning algorithm for diffusion language models that aligns training with token difficulty and time step, achieving substantial gains on reasoning benchmarks.