On June 5, the ByteDance technical team published a post on its official WeChat account stating that the ByteBrain team led by ByteDance, in collaboration with UC Merced and UC Berkeley, proposed VMR²L and developed a VMR system based on deep reinforcement learning. While maintaining near-optimal performance, the inference time was compressed to 1.1 seconds, successfully achieving the unity of system performance and industrial deployability. This work has been published at the top system conference EuroSys25. The two co-first authors of this article are interns of the ByteBrain team of ByteDance. Their research focuses on the long-neglected but critical virtual machine rescheduling (VMR) problem.