Adaptive Long-Horizon Latent Action Model

Latent action models aim to learn controllable actions from unlabeled trajectories. However, the action-induced changes between consecutive frames are hard for models to distinguish from noise. Without action labels, reconstructing observed transitions alone does not explicitly require the model to separate action effects from noise. To overcome this problem, Adaptive Long-Horizon LAM is introduced. In the real world, few controllable actions can be completed to reach a target in one frame. Also, they will exhibit strong temporal persistence and dictate a consistent state transition throughout their lifespan, while exogenous noise is stochastic and lacks dynamical consistency over time. The key idea of ALHLAM is to force the model to learn the consistent parts of changes. This constraint can be viewed as an unsupervised consistency bottleneck. In the meantime, fixed long-horizon cannot fit into all situations. These temporal supports also guide action chunk discovery. The experiments on world models show that ALHLAM is better than fixed long-horizon.