We review the January 1, 2026 paper "GLM-4.5V and GLM-4.1V-Thinking: Towards Versatile Multimodal Reasoning with Scalable Reinforcement Learning" from the GLM-V Team as a collaboration between Zhipu AI & Tsinghua University which have released an a new open weight model. The GLM-Image model they produce could likely be the first SOTA multimodal model fully trained on Chinese-manufactured hardware (Huawei Ascend) chips, the Ascend Atlas 800T A2 hardware, on MindSpore, an open-source AI framework developed by Huawei. We review the GLM-4V family of vision-language models, specifically highlighting the GLM-4.5V and the reasoning-focused GLM-4.1V-9B-Thinking versions. These models utilize a sophisticated training pipeline that integrates multimodal pre-training, supervised fine-tuning for long-chain-of-thought reasoning, and large-scale reinforcement learning. A significant innovation is the use of 3D-RoPE and dynamic image resolution handling, allowing the models to process high-definition visual data and complex spatial relationships efficiently. The research emphasizes a multi-domain reinforcement learning approach where training in one area, such as GUI navigation or STEM, improves performance across unrelated tasks. Benchmarks demonstrate that these open-source models achieve state-of-the-art results, often rivaling or exceeding larger closed-source systems in visual reasoning and document understanding. Ultimately, the documentation serves as a technical overview of how reinforcement learning with verifiable rewards can stabilize and enhance multimodal intelligence.