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

• URL: http://arxiv.org/abs/2603.00592v1
• Date: Sat, 28 Feb 2026 10:53:33 GMT
• Title: LangGap: Diagnosing and Closing the Language Gap in Vision-Language-Action Models
• Authors: Yuchen Hou, Lin Zhao,
• Abstract summary: Vision-Language-Action (VLA) models achieve over 95% success on standard benchmarks.<n>We find that current state-of-the-art VLA models largely ignore language instructions.<n>This paper constructs the LangGap benchmark, based on a four-dimensional semantic perturbation method.
• Score: 4.54067274409672
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 π0.5. 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.

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