Supervise What Decides Success: Criterion-Aligned Auxiliary Losses for Latent World-Model Planning
Latent World Models plan by minimizing distance to a goal image in latent space. This work introduces an auxiliary loss that trains a linear head on the model's outputs to regress success-criterion quantities, such as object positions. By adding this error to the training loss, planning performance on tasks like PushT and cube improves significantly.