learnability

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.