Revisiting Parameter Redundancy in Vision-Language-Action Models: Insights from VLM-to-VLA Adaptation

Vision-Language-Action (VLA) models have made signifi-cant strides in embodied intelligence by integrating the powerful repre-sentations of pre-trained Vision-Language Models (VLMs). However, themassive parameter scale of VLAs imposes a heavy computational burden,and these models exhibit extreme sensitivity to parameter pruning. Cur-rent paradigms often treat the resulting performance degradation as in-evitable, relying on fine-tuning or low-rank corrections to recover efficacy.We challenge this convention by questioning whether the removed param-eters are truly redundant if VLA pruning necessitates performance recov-ery to be effective, or if this paradigm masks the indiscriminate pruningof critical parameters. We revisit parameter redundancy through the lensof VLM-to-VLA adaptation, first quantifying the spatial distribution ofparameter divergence during adaptation to reveal structured patternsacross different modules. Subsequently, we introduce controlled pruningas a diagnostic probe: by comparing the direct impact of removing dif-ferent parameter subsets on VLA performance without any fine-tuning,we establish a causal link between adaptation-induced divergence signalsand functional contributions. Based on the discovered modular hetero-geneities, we design a multi-module joint pruning scheme. Evaluationson the LIBERO benchmark demonstrate that our approach reduces theparameters of OpenVLA and π0.5 by 12%–30% while maintaining approx-imately 90% of the original performance without any post-pruning recov-ery. In contrast, existing parameter pruning criteria result in total perfor-mance collapse when evaluated under the same recovery-free constraints.Our study reveals the parameter evolution mechanism in VLA adapta-tion and provides a new path for deploying efficient, robust robotic poli-cies in resource-constrained environments. Code is available at https://github.com/Niannnnnn/VLAParameterRedundancy_VLM2VLA.