Performance Based Difficulty Calibration

via “difficulty-calibrated-problem-stratification”

13K competitive programming problems from AlphaCode research.

Unique: Uses empirical runtime metrics (median and 95th percentile from real submissions) to calibrate difficulty rather than subjective classification or problem setter ratings. This grounds difficulty in measurable performance data and enables reproducible difficulty-based dataset splits.

vs others: More objective than subjective difficulty labels (e.g., 'hard' vs 'medium') and more granular than binary easy/hard splits, enabling fine-grained curriculum learning studies that other datasets don't support.