Curriculum learning trains models on easier examples first, gradually increasing difficulty like human education. Intuition: Start with clear patterns, build up to complex cases. Avoids early confusion from hard examples. Better optimization trajectory. Difficulty metrics: Loss value (lower = easier), prediction confidence, human-defined complexity, data-driven scoring. Strategies: Predetermined: Fixed difficulty ordering based on metrics. Self-paced: Model selects examples it can currently learn. Teacher-guided: Separate model determines curriculum. Baby Steps: Multiple difficulty levels, progress when mastered. Implementation: Sort dataset by difficulty, start with easy subset, gradually expand, or weight examples by curriculum. Benefits: Faster convergence, better final performance on some tasks, more stable training. Challenges: Defining difficulty, computational overhead for scoring, may not help all tasks. When most effective: Noisy data (easy examples often clean), complex tasks with learnable substructure, limited training time. Negative results: Not always beneficial, random ordering sometimes competitive. Useful technique for specific scenarios requiring training stability.