Why robotics RL training pipelines fail at scale

About This Tutorial

Scaling reinforcement learning for robotics looks straightforward on paper. You have a simulator, a policy network, a reward function, and compute. Add of each, and you should get better policies faster. In practice, most teams hit a wall somewhere between "works in a single environment" and "trains reliably across a fleet of parallel workers." The failures are rarely dramatic. They accumulate quietly until your sim-to-real transfer is broken, your reward signal is lying to you, or your infrastructure is burning CPU cycles on stale observations.