Recently, the emergence of Deep Learning on graphs has led to significant advancements in solving complex optimization problems. Among these, graph sparsification has attracted considerable attention, as it aims to reduce the size and complexity of graphs while preserving their essential structural and informational properties. This is particularly crucial in real-world applications, where graphs continue to grow in scale, making efficient processing increasingly challenging. In this paper, we explore the potential of Reinforcement Learning as a powerful approach for graph sparsification, leveraging its ability to learn adaptive strategies that balance the trade-off between reducing graph size and preserving key information.