LangGap: Diagnosing and Closing the Language Gap in Vision-Language-Action Models

arXiv:2603.00592v1 Announce Type: new
Abstract: Vision-Language-Action (VLA) models achieve over 95% success on standard benchmarks. However, through systematic experiments, we find that current state-of-the-art VLA models largely ignore language instructions. Prior work lacks: (1) systematic semantic perturbation diagnostics, (2) a benchmark that forces language understanding by design, and (3) linguistically diverse training data.
This paper constructs the LangGap benchmark, based on a four-dimensional semantic perturbation method — varying instruction semantics while keeping the tabletop layout fixed — revealing language understanding deficits in {pi}0.5. Existing benchmarks like LIBERO assign only one task per layout, underutilizing available objects and target locations; LangGap fully diversifies pick-and-place tasks under identical layouts, forcing models to truly understand language.
Experiments show that targeted data augmentation can partially close the language gap — success rate improves from 0% to 90% with single-task training, and 0% to 28% with multi-task training. However, as semantic diversity of extended tasks increases, model learning capacity proves severely insufficient; even trained tasks perform poorly. This reveals a fundamental challenge for VLA models in understanding diverse language instructions — precisely the long-term value of LangGap.