learnability

This paper presents C-Guard, a constitution-grid instrument for data-efficient RL alignment of safety guards, using C-LIM learnability scores to prune, densify, amend, or expand training data. It reduces over-refusal from 22.4% to 12.8% and improves data efficiency.

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.