The current development of DFAs, stuck in a "no man's land" between promising yet poorly transferable ML DFAs and old-school human-designed ones, underscores the urgent need to embed transferability into ML DFAs. Here, we introduce real-space energy learning as the key tool to address this issue. In real-space energy learning, we distinguish between commonly used global energy loss (GES) and local energy loss (LES) methods, demonstrating the superior data efficiency of LES.