RESEARCH PAPERYear 2026
Hierarchical Latent Action Model
Classification
View four quadrants- Major category
- VLA
- Quadrant
- Not applicable
- Architecture
- Not applicable
- Prediction paradigm
- Not applicable
- Subcategories
- Latent action pretrainingAction policy foundations
- Source review status
- Verified from primary sources
Category review. HiLAM learns variable-duration latent skills and then trains a language-conditioned hierarchical policy whose low-level module outputs executable actions. The deployed policy does not run a future-world model or planning loop. This is a latent-skill policy-learning method rather than a canonical general component. Reading evidence
Contribution
A contribution summary has not been added yet.
Abstract
An abstract has not been added yet.
Affiliations
Not listed in the collection.