Robot Learning

Cross-Entropy Loss

Cross-entropy loss is a loss function measuring the dissimilarity between a predicted probability distribution and target labels, equal to the negative log-likelihood of the correct class under the model. It is the standard objective for classification and for next-token prediction in language models, and appears in robot learning when policies output discretized action bins, as in RT-1 and RT-2, or when VLAs are trained autoregressively on action tokens.

Why it matters for physical AI

Tokenized-action VLAs are trained almost entirely with cross-entropy, so its properties, including how action discretization interacts with likelihood training, directly shape the precision of foundation-model robot control.