Limited Linguistic Diversity in VLA Training Distorts Language Representations

Vision-language-action (VLA) models inherit rich language representations from pretraining, yet remain brittle to variation in natural-language commands. We study how this brittleness is reflected in the representations themselves. We first show that the Open X-Embodiment Magic Soup mixture relies on a strikingly small and repetitive vocabulary that does not cover the natural variation in robot commands: even simple paraphrases of benchmark instructions often contain words absent from it. We then trace the language embeddings of OpenVLA and ${\pi }_0$-FAST through vision-language and robot training. Robot training changes the embeddings considerably more than vision-language training does, and in both models these changes carry a direct imprint of the data. For OpenVLA, whose training data is public and whose embeddings are untied, only tokens that appear in the training commands change; among them, tokens appearing in more varied commands change less, while tokens seen in only a few commands can still be substantially reshaped. For ${\pi }_0$-FAST, whose training data is not public, the most heavily trained tokens stand out clearly enough from the rest of the vocabulary that we can partially recover its training vocabulary. Rather than adapting the language space as a whole, robot training thus specializes it to the narrow set of commands observed during training. This specialization also shows in how robust VLA models are to small input corruption: a single-character typo in an object name, which preserves the meaning of a command but changes its tokens, alters VLA representations substantially more than those of the corresponding language and vision-language models, suggesting that VLA representations depend more on the exact tokens than on the intended meaning. Together, these results link the narrowness of robot language data to changes in the VLA representations.