Latent actions learned from diverse human videos serve as pseudo-labels for vision-language-action (VLA) pretraining, but provide effective supervision only if they remain informative about the underlying ground-truth actions. For effective supervision, latent actions should contain information about the underlying actions even though they are inaccessible. We propose Multi-ViewPoint Latent Action Moel (MVP-LAM), which learns latent actions that are highly informative about ground-truth actions from multi-view videos. MVP-LAM trains latent actions with a cross-viewpoint reconstruction objective, so that a latent action from one view must explain the future in another view, reducing reliance on viewpoint-specific cues. On Bridge V2, MVP-LAM produces more action-centric latent actions, achieving higher mutual information with ground-truth actions and improved action prediction, including under out-of-distribution evaluation. Finally, pretraining VLAs with MVP-LAM latent actions improves downstream manipulation performance on various benchmarks. The code and trained checkpoints are available at https://jmsnu.github.io.
@article{arxiv.2602.03668,
title = {MVP-LAM: Learning Action-Centric Latent Action via Cross-Viewpoint Reconstruction},
author = {Jung Min Lee and Dohyeok Lee and Seokhun Ju and Taehyun Cho and Jin Woo Koo and Li Zhao and Sangwoo Hong and Jungwoo Lee},
journal= {arXiv preprint arXiv:2602.03668},
year = {2026}
}