Taking Reinforcement Learning Cross Datacenter#

Speakers: Nan Jiang

• Why: RL for frontier models is pinned to a single datacenter today — training and rollout generation must co-locate — which makes the whole system hostage to one site's capacity, hardware, and failures. Decoupling them changes where AI training traffic flows and what the cross-DC network has to tolerate.
• What: A position talk on the first steps toward cross-datacenter RL: training in one place, rollout trajectory generation elsewhere, pulling compute opportunistically from whatever cloud, region, hardware, or precision format is available — making RL capacity global and elastic rather than a reserved supercomputer (from abstract only).
• How: Architectural disaggregation of the RL loop (learner vs. rollout workers) so each component can live where capacity and price are best, tolerating cross-site latency and heterogeneous hardware/precision.
• Where applicable: Frontier-lab-scale RL today, but it directly signals future cross-DC training/rollout traffic patterns and DCI requirements. The abstract is a vision statement with no quantitative results — go for direction, not data.