Author

Yang Xiao

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#artificial intelligence Preprint Sep 2026

M2Tok: Multi-head Multi-codebook Discrete Action Tokenization for Vision-Language-Action Models

Experimental results demonstrate the proposed ${M}^2$Tok, a Multi-head Multi-codebook Action Tokenizer designed to minimize reconstruction error and enhance policy performance, achieves superior reconstruction fidelity and significantly boosts the success rate of VLA models.

Chun-Pu Xu, Zhi-Xuan Liang, Yu-Hao Zhang et al. · 0 citations

MoEActok: A MoE-based Action Tokenizer for Vision-Language-Action Models

Across both simulated and real-world settings, MoEActok-based VLA substantially outperforms existing VLA models in both simulated and real-world settings.

Chun-Pu Xu, Zhi-Xuan Liang, Tianshuo Yang et al. · 1 citation