τ_0-VLA: a Hierarchical Robot Foundation Model with World-Model-Guided Test-Time Computation
Summary
A hierarchical vision-language-action model improves long-horizon robot manipulation by using world-model-guided test-time search to scale computation for high-level subtask decisions.
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Paper page - τ_0-VLA: a Hierarchical Robot Foundation Model with World-Model-Guided Test-Time Computation
Source: https://huggingface.co/papers/2608.16885 Authors:
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Abstract
A hierarchical vision-language-action model improves long-horizon robot manipulation by using world-model-guided test-time search to scale computation for high-level subtask decisions.
Long-horizon robot manipulation requires a robot to both execute individual skills reliably and sequence them coherently over extended tasks. Most hierarchicalvision-language-action(VLA) models make each such decision with a single forward pass, leaving no mechanism to allocate additional computation to difficult or consequential choices. We introduce τ_0-VLA, a hierarchical robot foundation model that formulates high-levelsubtask generationas a compute-scalable inference problem throughworld-model-guided test-time computation. At each inference step, the high-level policy usesexecution memoryto generate a subtask and, when needed, searches over alternatives before committing to its output. Alow-level policythen executes the generated subtask across multiple robot embodiments. The policy is trained on 40,115 hours of heterogeneous real-world data withmultimodal co-training. Across in-domain and distribution-shifted settings, allocating additional test-time computation substantially improves next-subtask prediction accuracy, and these gains translate into higher closed-loop success on long-horizon robot manipulation tasks.
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