A Sokoban RNN's plan representation predicts 50 steps ahead and generalizes OOD

Taufeeque et al. recover a causal plan representation from a DRC ConvLSTM Sokoban agent via logistic-regression probes, with causal validation that probed directions drive behavior [taufeeque-etal-2024-planning-in-a-recurrent-neural-network-that-plays-sokoban] The plan representation predicts the agent's future actions roughly 50 steps ahead [taufeeque-etal-2024-planning-in-a-recurrent-neural-network-that-plays-sokoban] Plan length and quality increase over the network's early internal computation steps [taufeeque-etal-2024-planning-in-a-recurrent-neural-network-that-plays-sokoban] The representation generalizes robustly to out-of-distribution puzzles far larger than any seen in training, and also explains a level-start "pacing" behavior the training incentivizes [taufeeque-etal-2024-planning-in-a-recurrent-neural-network-that-plays-sokoban]