Beyond a Single Latent Space: A Dual-Latent World Model for Long-Horizon Planning

Latent world models often struggle with long-horizon planning, even when their short-term predictions are accurate. Prediction errors accumulate during recursive rollout, while distance concentration in high-dimensional latent spaces can weaken the distinction between states at different distances from a goal. We introduce the Dual-Latent World Model (Dual-WM), which separates local execution from long-range planning through two distinct state representations and dynamics models. The low-level model captures fine-grained action-conditioned transitions, while the high-level model uses learned macro-actions to plan over longer temporal spans. We further propose Long-Horizon Representation Learning with Weighted Rollout (LoRe), which supervises self-generated predictions at both levels. Motivated by an analysis of recursive error propagation, LoRe uses exponential horizon weights with separate decay rates to balance multi-step supervision at each temporal scale. During planning, the high-level model generates latent subgoals, and the low-level model refines them into actions for precise execution. We evaluate from-scratch Dual-WM on five goal-conditioned visual control tasks against the task-wise strongest baselines without actor-guided proposals. At goal offsets of 50 and 100 environment steps, mean success increases from 75.9% to 84.4% and from 61.4% to 69.5%, respectively. At offset 100, Dual-WM outperforms these baselines on all five tasks and improves mean success over LeWM by 30.8 percentage points. Ablations and supporting analyses provide evidence that the proposed design learns more informative state representations for goal evaluation and improves consistency under recursive prediction. These results highlight the value of separating temporal roles and training across multiple horizons for reliable latent planning. Our core implementation is available in an anonymous repository at https://anonymous.4open.science/r/Dual-WM-2411/.