The field of machine learning is witnessing a significant shift towards adaptive learning and optimization techniques, with a focus on addressing concept drift, improving convergence rates, and exploring unconventional optimization dynamics. Researchers are developing innovative methods to adapt to changing data distributions, including lightweight neural networks and online learning algorithms that can efficiently update models without requiring extensive retraining or drift detection. Additionally, there is a growing interest in exploiting chaos and non-stationarity in the training process to improve learning rates and accuracy. Notably, new frameworks and algorithms are being proposed to dynamically adjust learning rates, leveraging concepts such as gradient alignment and mixability to achieve improved convergence rates and regret bounds. Noteworthy papers include: Lite-RVFL, which introduces a novel lightweight neural network for adapting to concept drift without drift detection and retraining. Online Learning-guided Learning Rate Adaptation via Gradient Alignment proposes a principled framework for dynamically adjusting the learning rate by tracking gradient alignment and using a local curvature estimate.