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 short VLA-grounded command using object appearance, spatial relations, and target-grounding cues. The language-conditioning space policy is optimized with reinforcement learning from sparse task-completion rewards, while the downstream VLA remains fully frozen. Experiments on RL4VLA and VL-Think show that language-conditioning space optimization improves success on instruction-sensitive, symbolic, and multi-object manipulation tasks, demonstrating that language can serve as an optimizable variable for a robot foundation models.

Embodied Vision–Language–Action (VLA) models are typically obtained by fine-tuning powerful pretrained VLMs on robotics data. Yet we do not know whether these agents still "know the basics" about the world after adaptation, nor how much commonsense and factual knowledge is preserved versus catastrophically forgotten. When a VLA fails on a knowledge-sensitive task, it is unclear whether the failure reflects degraded world knowledge or merely poor generalization of low-level control. We introduce Act2Answer, a lightweight protocol that adapts VLM knowledge benchmarks to VLA evaluation by requiring agents to act to answer. We map each benchmark question to a discrete embodied answer-selection decision in a controlled tabletop environment, where the agent answers via a single object-placement action into the region corresponding to its selected option, producing an action-grounded success rate with reduced control confounds. We introduce layerwise intent probing–linear classifiers on per-layer representations predicting the agent’s option selection–to localize answer-relevant information across model depth. Evaluating 9 state-of-the-art VLA models, we rank them across basic commonsense and world-knowledge categories and find that answer-relevant signals peak in middle VLM-backbone layers but attenuate in upper backbone layers and the action head.