TOAST: Stochastic Robot Action Tokenization for Autoregressive Vision-Language-Action Models
TOAST introduces a method to diversify how continuous robot actions are represented as discrete tokens during policy training. It randomly samples different ways to tokenize the same action sequence using a language model, which helps improve learning efficiency when training data is scarce. This stochastic approach reduces overfitting and makes policies more robust.