This study presents a comparative analysis of curriculum learning and traditional training methods
in AI models, focusing on their performance across diverse tasks, including image classification with
Convolutional Neural Networks (CNNs) and policy learning in Reinforcement Learning (RL) agents.
Curriculum learning organizes training data in a progressive manner, from simpler to more complex
examples, while traditional methods employ a random presentation of data. Experimental results across
datasets such as MNIST and CIFAR-10 and environments like CartPole and Atari Breakout reveal
that curriculum learning consistently outperforms traditional training methods in terms of accuracy,
convergence time, and generalization performance. Specifically, models trained with curriculum
learning achieved faster convergence and superior generalization to unseen data. These findings
highlight curriculum learning as an effective strategy for improving the efficiency and robustness of AI
models, offering potential for advancements in complex tasks across various domains such as computer
vision and reinforcement learning.
