Papers

Supervise What Decides Success: Criterion-Aligned Auxiliary Losses for Latent World-Model Planning

This preprint introduces a training-time auxiliary loss for latent world models: a temporary linear head regresses the physical quantities used in a task's success criterion directly from latent states. On PushT and cube it raises planning success by 3.5 and 3.4 absolute points over the same baseline, with significant paired-test gains, while leaving test-time model and inputs unchanged. It matters because success criteria can tell world models exactly which physical quantities the latent state must retain for reliable planning.