One of robotics’ hardest problems isn’t making robots move; it’s making what they learn in simulation work in the real world. No matter how detailed a virtual environment is, it can’t fully capture the messiness of reality: friction changes, shifting light, unpredictable humans. This gap between simulation and reality is called the sim-to-real gap. Teams are closing it with better physics, precise sensor calibration, and datasets that mix simulated and real-world data. But even then, the world stays noisy and unpredictable: reality always has the final say. That’s exactly what CodecFlow is solving. By combining standardized Operators, shared real-world feedback loops, and adaptive simulation pipelines, CodecFlow turns the sim-to-real gap into a continuous learning process, so what robots learn in simulation actually works out there, where it matters.