Open Internet by MindsNet

Optimizing Embodied AI Training and RL Infrastructure

Current embodied AI training and reinforcement learning (RL) infrastructure faces significant bottlenecks, including memory bandwidth, launch overhead, small-batch inefficiency, and fragmented runtime stacks. These limitations hinder the efficiency and scalability of robotics training and RL workloads. Most optimization efforts have focused on inference, leaving a vast unexplored optimization space at the kernel and runtime layers.

Computing & Technology, Computer Science, Machine Learning