Efficient Rubik's Cube Solving via Learned Representations: No Hand-Crafted Heuristics Needed

Classical AI separates perception (spatial representation learning) from planning (temporal reasoning via search). This work explores representations capturing both spatial and temporal structure. Standard temporal contrastive learning often fails due to spurious features. The authors introduce Contrastive Representations for Temporal Reasoning (CRTR), using negative sampling to remove these features and improve temporal reasoning. CRTR excels on complex temporal tasks like Sokoban and Rubik's Cube, solving the latter faster than BestFS (albeit with longer solutions). Remarkably, this is the first demonstration of efficiently solving arbitrary Rubik's Cube states using only learned representations, eliminating the need for hand-crafted search heuristics.