GLM-Image: Towards Versatile Multimodal Reasoning with Scalable Reinforcement Learning

About this episode

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.