Bentonite Buffer

"Buffers," in various computational contexts, serve as temporary storage spaces to improve efficiency and performance in diverse machine learning and optimization tasks. Current research focuses on optimizing buffer management strategies, including novel sampling techniques (e.g., Monte Carlo methods, class-adaptive sampling) and buffer architectures (e.g., hybrid reservoirs, elite buffers) to enhance continual learning, reinforcement learning, and other applications. These advancements aim to address challenges like catastrophic forgetting, sample inefficiency, and limited memory resources, ultimately leading to more robust and efficient algorithms across numerous fields.