Researchers propose a new action tokenization method, M^2Tok, to improve the performance of Vision-Language-Action (VLA) models. The approach decomposes latent action features into multiple heads and assigns independent codebooks for quantization, leading to better reconstruction fidelity and higher success rates in VLA tasks.