GLM-V: Advancing Multimodal Reasoning with RLCS

The sources introduce GLM-4.1V-Thinking and GLM-4.5V, a new family of vision-language models (VLMs)developed by Zhipu AI & Tsinghua University, designed for advanced multimodal reasoning. These models are trained using a framework that combines large-scale pre-training, supervised fine-tuning, and a novel Reinforcement Learning with Curriculum Sampling (RLCS) approach. The RLCS significantly boosts performance across diverse tasks like STEM problem-solving, video understanding, GUI agents, and coding, as demonstrated by state-of-the-art results on 42 public benchmarks compared to existing open-source and some closed-source models. The research also highlights the challenges of reward system design in multi-domain reinforcement learning and the observed cross-domain generalization of capabilities, where training in one area benefits others.