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This paper investigates the impact of bit allocation strategies on the performance of world model-based planning, specifically using DINO-WM on the Wall planning task. The study compares uniform, mixed, asymmetric, and layerwise quantization schemes under different planner budgets to identify critical bitwidth thresholds. Results show a sensitivity to bit allocation in the 4-bit regime, with encoder precision being particularly important for maintaining performance, suggesting the need for module-aware quantization policies.
Don't blindly quantize your world models: where you allocate bits matters more than the total bitwidth for spatial reasoning tasks in the critical 4-bit regime.
Efficient spatial reasoning requires world models that remain reliable under tight precision budgets. We study whether low-bit planning behavior is determined mostly by total bitwidth or by where bits are allocated across modules. Using DINO-WM on the Wall planning task, we run a paired-goal mixed-bit evaluation across uniform, mixed, asymmetric, and layerwise variants under two planner budgets. We observe a consistent three-regime pattern: 8-bit and 6-bit settings remain close to FP16, 3-bit settings collapse, and 4-bit settings are allocation-sensitive. In that transition region, preserving encoder precision improves planning relative to uniform quantization, and near-size asymmetric variants show the same encoder-side direction. In a later strict 22-cell replication with smaller per-cell episode count, the mixed-versus-uniform INT4 sign becomes budget-conditioned, which further highlights the sensitivity of this transition regime. These findings motivate module-aware, budget-aware quantization policies as a broader research direction for efficient spatial reasoning. Code and run artifacts are available at https://github.com/suraj-ranganath/DINO-MBQuant.