Introducing static network sparsity through one-shot random pruning can enhance the scaling potential of deep reinforcement learning (DRL) models. This approach provides higher parameter efficiency and better optimization resilience compared to traditional dense networks, demonstrating benefits in both visual and streaming RL scenarios.