剪枝会破坏视觉语言动作模型，但可修复。
Don't Run with Scissors: Pruning Breaks VLA Models but They Can Be Recovered
- 通过权重空间插值生成修正项，恢复剪枝后模型能力。
- 无需重新训练，可使成功率大幅回升，安全违规减少。
- 适合资源受限设备部署的机器人模型优化。
视觉-语言-动作（VLA）模型虽提升了机器人能力，但在资源受限硬件上部署仍具挑战。剪枝已成功压缩大语言模型，但在机器人领域研究较少。令人意外的是，剪枝会导致VLA模型性能急剧下降并增加安全风险。我们提出GLUESTICK，一种剪枝后恢复方法，在保持稀疏性优势的同时恢复模型功能。该方法在权重空间中对稠密与剪枝模型进行一次插值，计算出修正项，并在推理时由每个剪枝层使用以恢复丢失能力，开销极小。GLUESTICK无需额外训练，与剪枝算法无关，仅需一个超参数控制效率与精度权衡。在多种VLA架构和操作、导航任务中，显著提升内存效率，同时大幅恢复成功率并降低安全违规。更多内容见：https://gluestick-vla.github.io/。
原文摘要 · Abstract (English)
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: https://gluestick-vla.github.io/.
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