Robot Learning

Batch Size

Batch size is the number of training examples processed together in one forward-backward pass before a gradient update, balancing gradient estimate variance against memory use and hardware utilization. Large-scale policy training aggregates batches across many accelerators, while on-policy reinforcement learning in simulation gathers batches from thousands of parallel environments. Learning rate and batch size interact strongly, with linear or square-root scaling rules commonly applied.

Why it matters for physical AI

Massively parallel simulation made huge on-policy batches cheap, a key enabler of learned locomotion, while vision-language-action pretraining inherits the large-batch optimization recipes of language model training.