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.

翻译：高效空间推理需要世界模型在严格的精度预算下保持可靠性。我们研究了低比特规划行为主要是由总比特宽度决定，还是由比特在模块间的分配方式决定。在Wall规划任务上使用DINO-WM，我们在两种规划器预算下，对均匀、混合、非对称和分层变体进行了配对目标混合比特评估。我们观察到一个一致的三阶段模式：8比特和6比特设置与FP16保持接近，3比特设置性能崩溃，而4比特设置对分配方式敏感。在该过渡区域，保持编码器精度相对于均匀量化能改善规划性能，且接近尺寸的非对称变体显示出相同的编码器侧优势。在后续更严格的22单元复制实验中（每个单元情节数更少），混合与均匀INT4的优劣符号变为预算条件依赖，这进一步凸显了该过渡区域的敏感性。这些发现启发了模块感知、预算感知的量化策略，作为高效空间推理领域一个更广泛的研究方向。代码与运行记录可在 https://github.com/suraj-ranganath/DINO-MBQuant 获取。