Foundation Models

FAST (Frequency-space Action Sequence Tokenization)

FAST is an action tokenization scheme, introduced by Physical Intelligence in 2025, that compresses continuous robot action chunks using a discrete cosine transform followed by byte-pair encoding. By decorrelating action sequences in frequency space before quantization, FAST yields far shorter token sequences than per-dimension binning, enabling efficient autoregressive training of vision-language-action models such as pi0-FAST on high-frequency dexterous data.

Why it matters for physical AI

How actions are represented as tokens directly bounds a VLA model's control frequency, training efficiency, and dexterity; compression schemes like FAST make autoregressive transformers practical for fine-grained real-robot control.