MHWA: Multi-timescale Hierarchical World-Action Model

Autoregressive world models have emerged as a powerful paradigm for sample-efficient decision-making, yet their open-loop rollouts often exhibit increasing drift that leads to error accumulation even within a relatively small planning horizon. Hierarchical temporal abstraction is a natural candidate to mitigate such drift by reducing the effective prediction horizon; however, existing hierarchical designs can introduce added structural and conceptual complexity, sometimes making them difficult to control and limiting gains over flat baselines. To this end, we introduce MHWA (Multi-timescale Hierarchical World Action model), which employs a strided high-level context channel to reduce global-context update frequency, effectively curbing the propagation of compounding drift. Crucially, via context-aware routing, the gating network dynamically reconfigures a Low-Level Mixture-of-Experts by using both global context and current observations, thereby enabling flexible high--low coordination, helping alleviate interference commonly observed in static hierarchies. Empirically, MHWA (77M parameters) achieves an IQM human normalized score (HNS) of 0.867$\pm$0.047 across three training seeds on a 14-game Atari subset using 10\% subsampled offline data, matching the existing state-of-the-art 150M-parameter JOWA baseline while using nearly half the parameters and significantly reducing computational costs.