arXiv — cs.AI preprintsInternational9 October 2026
VLA Grounder: Language-Conditioning Space Optimization for Black-Box VLA Models
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arXiv:2607.04517v2 Announce Type: replace Abstract: Vision-Language-Action (VLA) models are commonly treated as end-to-end action policies conditioned on natural-language task descriptions. In practice, however, their behavior often depends sharply on how the instruction is phrased, suggesting that language is not merely a task label but an optimizable conditioning input. We study whether frozen VLA policies can be improved by optimizing language space rather than updating action weights. Our method introduces a language-conditioning space policy that translates a human instruction into a shor
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