Matteo Cercola, Katie Z Luo, Milan Ganai, John Alora, Simone Formentin, Marco Pavone · 2026 · arXiv
DOI: 10.48550/arXiv.2610.28166 · open-access PDF
Vision-Language-Action (VLA) ポリシーは、行動する前に常に理性を強化するが、既存の理性モデルは、この中間的な決定を単一の表現に圧縮する。複数の計画が有効である場合、このコミットメントは、他の方法で選択または修正される可能性のある代替を隠します。我々は、理性の状態を、使用する対象、把握、またはルートなどの計画に対する低次元の条件分布として表現する、Generative Plan Latent (GPL) を導入します。
Machine translation (Meta M2M100); the English original follows.
Vision-Language-Action (VLA) policies increasingly reason before acting, but existing reasoning models compress this intermediate decision into a single representation. When several plans are valid, this commitment hides alternatives that could otherwise be selected or revised. We introduce the Generative Plan Latent (GPL), which represents the reasoning state as a low-dimensional conditional distribution over plans, such as which object, grasp, or route to use. This makes the reasoning state addressable: without retraining the VLA, plans can be sampled (coverage), selected on request (steerin
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