SplitJEPA proposes separating stable and changing factors in AI's learned representations

A researcher behind SplitJEPA says it builds on LeJEPA by training on observation pairs that share stable factors but differ in others. They say their proof depends on sufficient variation in the changing factors across those pairs. In the PushCube robot-arm simulation, they report behavior-cloning success under an unseen 10-degree camera rotation of 71.5% with SplitJEPA, versus 12.8% with raw pixels and 48.0% with frozen R3M.