Abstract
Vision-Language-Action (VLA) models have advanced robotic capabilities but remain challenging to deploy on resource-limited hardware. Pruning has enabled efficient compression of large language models (LLMs), yet it is largely understudied in robotics. Surprisingly, we observe that pruning VLA models leads to drastic degradation and increased safety violations. We introduce GLUESTICK, a post-pruning recovery method that restores much of the original model's functionality while retaining sparsity benefits. Our method performs a one-time interpolation between the dense and pruned models in weight-space to compute a corrective term. This correction is used during inference by each pruned layer to recover lost capabilities with minimal overhead. GLUESTICK requires no additional training, is agnostic to the pruning algorithm, and introduces a single hyperparameter that controls the tradeoff between efficiency and accuracy. Across diverse VLA architectures and tasks in manipulation and navigation, GLUESTICK achieves competitive memory efficiency while substantially recovering success rates and reducing safety violations. Additional material can be found at: this https URL.
Abstract (translated)
翻译如下： Vision-Language-Action (VLA) 模型虽然在增强机器人能力方面取得了进展，但在资源有限的硬件上部署仍然具有挑战性。修剪技术已经成功地使大型语言模型（LLMs）实现了高效的压缩，然而它在机器人领域研究较少。令人惊讶的是，我们观察到对VLA模型进行修剪会导致性能大幅下降，并增加安全违规的风险。为此，我们提出了一种名为GLUESTICK的后修剪恢复方法，该方法可以在保留稀疏性优势的同时恢复原始模型的大部分功能。我们的方法通过在一维空间内计算密集型和已修剪模型之间的插值来生成修正项。在推理过程中，每个被剪枝层利用这个校正项以极小的额外开销恢复丢失的能力。GLUESTICK不需要额外训练，并且对任何修剪算法都保持中立状态；它引入了一个单一超参数来控制效率与精度之间的权衡。 在各种VLA架构和操作及导航任务上，GLUESTICK实现了具有竞争力的记忆效率的同时大幅恢复成功率并减少安全违规行为。更多相关信息可以在以下链接找到：[此URL](this https URL)。
URL
https://arxiv.org/abs/2510.08464
