FreshVLA: Frequency-Regularized Time-Scale Hierarchical Latent Actions for Vision-Language-Action Models

Learning reusable latent actions from action-free videos offers an effective route to improving Vision-Language-Action (VLA) policies. However, reconstruction-oriented latent actions do not necessarily benefit policy learning: as they may encode task-irrelevant static appearance with scene identity, or entangle slowly varying task progress with fine-grained execution dynamics in limited discrete code. We propose FreshVLA, a VLA framework built on a frequency-guided, dynamics-sensitive learning paradigm for acquiring temporally hierarchical continuous latent actions. FreshVLA uses a high-pass prior to guide visual transition encoding toward dynamical regions, thereby reducing reliance on static appearance cues. It further organizes latent actions into coarse-grained task progress and fine-grained execution dynamics through cross-timescale sampling and frequency-domain regularization, enhancing their capacity to represent long-horizon behaviors. During policy training, we freeze the pretrained latent action encoder and transfer its representations to robot control through joint flow-matching of latent and robot actions. FreshVLA achieves 98.2% average success on LIBERO and 98.0% on LIBERO-Long, exceeding GR00T-N1.6 on the latter by 3.6%. It also reaches an average sequence length of 4.5 on CALVIN and strong overall success rate of 80.4% in the zero-shot LIBERO-PLUS evaluation. Furthermore, representation analysis reveals more consistent robot–object interaction attention in FreshVLA and a 51.3% greater relative error reduction in long-horizon action regression than discrete latent actions. Real-world experiments demonstrate that FreshVLA improves the average success rate over ${\pi }_{0.5}$ by 7.5%.