Core Insights - The article discusses the limitations of current Vision-Language Models (VLMs) in accurately interpreting and executing spatial commands in robotics, emphasizing the need for precise geometric reasoning and tool integration [2][5]. Group 1: TIGeR Framework - The Tool-Integrated Geometric Reasoning (TIGeR) framework enhances VLMs by integrating tool usage and reinforcement learning to improve their ability to perform precise calculations in a three-dimensional space [2][6]. - TIGeR allows AI models to transition from qualitative perception to quantitative computation, addressing the core pain points of existing VLMs [2][7]. Group 2: Advantages of TIGeR - TIGeR provides precise localization by integrating depth information and camera parameters, enabling the accurate conversion of commands like "10 centimeters above" into three-dimensional coordinates [7]. - The framework supports multi-view unified reasoning, allowing information from various perspectives to be merged and reasoned within a consistent world coordinate system [7]. - The model's reasoning process is transparent, making it easier to debug and optimize by clearly showing the tools used, parameters input, and results obtained [7]. Group 3: Training Process - The training of TIGeR involves a two-phase process: first, supervised learning to teach basic tool usage and reasoning chains, followed by reinforcement learning to refine the model's tool usage skills through a hierarchical reward mechanism [8][10]. - The hierarchical reward mechanism evaluates not only the correctness of the final answer but also the accuracy of the process, including tool selection and parameter precision [8]. Group 4: Data Utilization - The TIGeR-300K dataset, consisting of 300,000 samples, was created to train the model in solving geometric problems, ensuring both accuracy and diversity in the tasks covered [10][13]. - The dataset construction involved template-based generation and large model rewriting to enhance generalization and flexibility, ensuring the model can handle complex real-world instructions [13]. Group 5: Performance Metrics - TIGeR outperforms other leading VLMs in spatial understanding benchmarks, achieving scores such as 93.85 in 2D-Rel and 96.33 in 3D-Depth [10][14]. - The model's performance in various spatial reasoning tasks demonstrates its capability to execute operations that require precise three-dimensional positioning, which other models struggle to achieve [16].