Reinforcement learning's problem-solving range is broader than most people realize. Once you grasp what RL can actually do, priorities shift completely—optimizing speed and performance becomes non-negotiable. The architecture needs to serve RL's computational demands, not the other way around. It's genuinely transformative tech. If you've spent time exploring RL applications across different domains, you'd understand why this matters so much. The potential is just starting to surface.