Bring Back Language to Actions: Restoring VLM capabilities in VLA Models via Parameter Merging

Vision-Language-Action models (VLAs) are obtained by fine-tuning pre-trained Vision-Language Models (VLMs) on robot demonstration data, enabling action prediction from visual observations and language instructions. However, this fine-tuning induces catastrophic forgetting, substantially degrading the semantic and visual grounding capabilities of the original VLM. We address this by merging the parameters of a VLM and its VLA derivative without retraining either model. Concretely, we extract the task vector — the difference between VLA and VLM weights — and decompose it via Singular Value Decomposition. For each component, we learn a scalar mixing coefficient controlling how much of the VLA adaptation is retained, optimised to jointly preserve action prediction and vision-language capabilities. Experiments show that our merged model performs well on both axes simultaneously, consistently outperforming established merging baselines.