（来源：吴说） 作者：Haseeb Qureshi，Dragonfly 管理合伙人 编译：谷昱，ChainCatcher 链接：https://www.chaincatcher.com/article/2235299 声明：本文为转载内容，读者可通过原文链接获得更多信息。如作者对转载形式有任何异议，请联系我 们，我们将按照作者要求进行修改。转载仅用于信息分享，不构成任何投资建议，不代表吴说观点与立 场。 LP 有时会问我，我认为加密货币领域最优秀的风险投资人是谁。 Dan Robinson 就是加密货币界的 Mike Speiser。 Dan 一次又一次地参与了加密货币领域几家具有划时代意义的公司的发展。他从 Uniswap 诞生之初就参 与其中，是 Uniswap V3 的联合撰稿人和奠基人之一，而 Uniswap V3 后来成为了链上现货交易的基 石。他也是 Flashbots 的早期关键贡献者，Flashbots 催生了现代 MEV 拍卖。此外，他还是 Plasma （Rollup 的前身）的早期研究贡献者，并因此领投了 Optimism 的种子轮融资。 Dan 是一位真正的博学家。他打破了传统风险投资 ...

Core Insights - The article discusses the advancements in Vision-Language-Action (VLA) models, particularly focusing on the robustness and generalization capabilities in real-world applications through a "generate-and-verify" paradigm [2][5][20]. Group 1: Key Findings - The research team found that increasing the number of candidate actions during the inference phase leads to a continuous decrease in action errors for VLA models [5]. - A power law relationship was established between action errors and the number of Gaussian perturbations sampled, indicating that the robot control problem should be viewed as a combination of generating candidate actions and verifying them [5][20]. - The proposed Test-Time Scaling Law demonstrates predictable improvements in task success rates and stability as the sampling and verification scale increases [2][20]. Group 2: Methodology Overview - The first phase involves training an action verifier using a synthetic action preference dataset derived from the RMSE differences between candidate and ground truth actions [8]. - The second phase focuses on expanding computational resources during inference, utilizing the trained action verifier to enhance the stability of VLA models [9][12]. Group 3: Experimental Results - The integration of RoboMonkey with VLA models resulted in significant performance improvements, including a 25% increase in success rates for out-of-distribution tasks and a 9% increase in the in-distribution SIMPLER environment [17]. - The accuracy of the RoboMonkey verifier showed a log-linear growth with the expansion of the synthetic dataset, leading to enhanced performance in various environments [16]. Group 4: Practical Deployment - A dedicated VLA serving engine was implemented to support high-speed action resampling and efficient construction of action proposal distributions, optimizing inference costs [19]. - The system architecture allows for higher throughput with larger high-bandwidth memory, further enhancing the generalization capabilities of the robotic foundational models [19].