The field of neural networks and control systems is witnessing significant advancements in efficient tuning and optimization techniques. Researchers are exploring innovative methods to improve the performance of neural networks, including the use of machine learning and control-theoretic approaches. These advancements have the potential to enhance the reliability and efficiency of various systems, such as payment routing and deep reinforcement learning agents. Notably, novel parameter-efficient fine-tuning methods and dynamic architecture optimization techniques are being developed to improve the adaptability and performance of neural networks. Noteworthy papers include: Cavity Duplexer Tuning with 1d Resnet-like Neural Networks, which presents a machine learning method for tuning cavity duplexers. A Control-Theoretic Approach to Dynamic Payment Routing for Success Rate Optimization, which introduces a control-theoretic framework for dynamic payment routing. NeuroAda: Activating Each Neuron's Potential for Parameter-Efficient Fine-Tuning, which proposes a novel parameter-efficient fine-tuning method. Study of Training Dynamics for Memory-Constrained Fine-Tuning, which proposes a novel transfer learning scheme for memory-efficient training. An Integrated Approach to Neural Architecture Search for Deep Q-Networks, which introduces an agent that integrates a learned neural architecture search controller into the DRL training loop.