用强化学习恢复压缩后视觉语言动作模型性能，适配设备端部署。
RLRC: Reinforcement Learning-based Recovery for Compressed Vision-Language-Action Models
- 分三阶段压缩：结构化剪枝、SFT与RL恢复、量化
- 内存减少8倍，推理速度提升2.3倍，任务成功率不变
- 适合资源受限设备上部署大型视觉语言动作模型
视觉语言动作模型（VLA）在复杂机器人操作中展现出强大能力，但其庞大的参数量和高推理延迟阻碍了实际部署，尤其在资源受限平台。为此，我们对VLAs的模型压缩进行了系统性实证研究。基于此，提出RLRC——一种三阶段压缩与恢复流程：结构化剪枝、通过监督微调（SFT）与强化学习（RL）恢复性能、后续量化。其中RL阶段引入评判器预热策略和行为克隆（BC）损失正则化，以稳定训练并保留策略行为。RLRC实现最高8倍内存压缩和2.3倍推理加速，同时保持原始任务成功率。在多个VLA骨干网络上的大量实验表明，该方法显著优于现有压缩基线，证明其在设备端部署中的有效性。
原文摘要 · Abstract (English)
Vision-Language-Action models (VLA) have demonstrated remarkable capabilities and strong potential in complex robotic manipulation. However, their large parameter sizes and high inference latency hinder real-world deployment, especially on resource-constrained platforms. To address this, we conduct a systematic empirical study of model compression for VLAs. Building on these insights, we present \textit{RLRC}, a three-stage compression and recovery pipeline consisting of structured pruning, performance recovery via SFT and RL, and subsequent quantization. The RL stage incorporates a critic warm-up strategy and BC loss regularization to stabilize training and preserve policy behavior. RLRC achieves up to an 8 times memory reduction and 2.3 times inference speedup while maintaining the original task success rate. Extensive experiments across multiple VLA backbones show that RLRC consistently outperforms existing compression baselines, highlighting its effectiveness for on-device deployment. Project website: https://rlrc-vla.github.io
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