VLA Models Inherit the Meaning of Words, Not the Use of Their Reasoning

Vision-language-action (VLA) models are built on language models so that robots can use their reasoning. Acting on a reasoned instruction requires deriving the goal, setting it as the goal of the action and executing the movement; chain-of-thought policies, hierarchies and language-preserving training seek to connect these steps, but task success alone cannot show which one fails. We identify the variable through which language sets the goal: a low-dimensional goal code, inherited from the parent language model, that the action reads. In OpenVLA and π₀.₅, writing the code redirects the robot and deleting it removes the goal, while a matched random subspace does neither, so the code tells whether a computation reaches the action. By this test, VLA models can reason but cannot act on it. Text-restored OpenVLA policies answer a rule correctly when asked in text, yet told the rule as their instruction they leave the bowl in place (0 and 1 of 30 episodes): the robot's input does not engage the computation. That text answer, written into the goal code of the same policy while it hears a plain instruction, drives it to the rule's destination in 24 and 26 of 30. Across ten models, word meaning reaches the code but a destination selected by an in-context rule does not; a stated goal moves the action as far as training made the action depend on it; and conclusions delivered to the code drive both families (22 to 28 of 30 episodes). VLA models inherit the meaning of words, not the use of their reasoning.