Bidirectional Curriculum Learning: Decelerating and Re-accelerating Learning for Robust Convergence

Abstract

Curriculum learning has proven success in guiding ML models toward better convergence, based on the gradual technique individuals take to address problems of increasing their complexity. Still, its unidirectional nature advancing only from easier to more difficult tasks often limits adaptability and may cause early convergence or insufficient generalization should the learning route become too rigid. We present a novel method called Bidirectional Curriculum Learning (BCL), which generates a more dynamic & flexible teaching environment. Rather than developing gradually from easy to more complex tasks, BCL alternates between periods of decelerated learning revisiting easier examples and accelerated learning addressing increasingly challenging problems. This reciprocal motion is meant to reflect how individuals typically advance by repeating fundamental ideas, therefore reinforcing their learning. We propose that the oscillation between cognitive load levels increases a model's generalizing capabilities across different data distributions as well as robustness & convergence stability. Relative to traditional curriculum learning & other baseline techniques, BCL showed speedier convergence, reduced overfitting & better performance in noisy or imbalanced environments in our experiments over numerous vision & also language assessments. We observed that a regularizing technique that of repeating smaller tasks helps models avoid overconfidence and unsatisfactory local minima. Our findings show that rather than following a straight line development in difficulty, learning requirements may be improved by well designed regressions strengthening fundamental understanding. This has significant consequences for developing robust & also effective training strategies particularly in sectors marked by dynamic data complexity or limited annotations. BCL helps curriculum designers to more faithfully portray the iterative and reflective qualities of human learning