LANCE: Learning What to See in Vision-Language-Action Policies via Language-Anchored Action Queries

Vision-language-action (VLA) policies have advanced robotic manipulation, yet they still struggle with reliable instruction following. These policies tend to exploit vision-action correlations instead of consistently using language to identify task-relevant visual evidence for action prediction. To address this issue, we propose **LANCE** (**Language-Anchored Action Queries**), an action-query framework that explicitly couples scene-conditioned action representations to task semantics derived from language instructions. LANCE employs two sets of learnable action queries: one reads only the language tokens to construct a *language anchor*, while the other reads both language and visual tokens to form a scene-conditioned action representation. To explicitly couple the scene-conditioned representation with the language anchor, *Reciprocal Alignment* aligns their latent distributions. Crucially, alignment alone does not ensure that the language anchor captures the action distinctions between instructions. We therefore devise *PriorAction*, an auxiliary action prediction loss conditioned on the language anchor. The two objectives jointly steer visual processing to prioritize task-relevant evidence for instruction-consistent action prediction. Experiments across diverse unseen settings show that LANCE improves instruction-consistent target selection. Moreover, under zero-shot composition of learned skills, LANCE achieves higher semantic coverage and subtask completion rates.