Researchers have introduced $\tau_0$-VLA, a hierarchical vision-language-action (VLA) foundation model designed to address the complexities of long-horizon robot manipulation. Unlike traditional VLA models that rely on a single forward pass for decision-making, $\tau_0$-VLA treats high-level subtask generation as a compute-scalable inference problem. This is achieved through a world-model-guided test-time computation mechanism, where the high-level policy utilizes execution memory to generate subtasks and performs a search over potential alternatives before finalizing an output. Once a subtask is determined, a low-level policy executes the command across various robot embodiments. The model was trained using multimodal co-training on a massive dataset comprising 40,115 hours of heterogeneous real-world data. Experimental results demonstrate that allocating additional test-time computation significantly boosts the accuracy of next-subtask predictions. These improvements in high-level reasoning directly correlate with higher closed-loop success rates for complex, long-horizon manipulation tasks, proving effective in both in-domain and distribution-shifted environments.