GLM-4.1V-Thinking: Advancing General-Purpose Multimodal Understanding and Reasoning

Vision-language models (VLM) play an important role in today’s intelligent systems by enabling the detailed understanding of visual content. The complexity of multimodal intelligence works has increased, including scientific problems to development of autonomous agents. The current demands on VLM have exceeded the simple visual material perception, with enhancing attention to advanced logic. While recent tasks suggest that the causes of long-term and scalable RL LLM significantly increase the solution capabilities, current efforts mainly focus on specific domains to improve VLM logic. The open-source community currently lacks a multimodal region model that improves traditional non-thinking models of comparable parameters scale in diverse functions.

Researchers at Zipu AI and Sinhahua University have proposed GLM-4.1V-Thinking, which is a VLM designed to carry forward the general-extended multimodal understanding and argument. The approach then introduces learning reinforcement with the syllabus sample (RLC) to unlock the complete capacity of the model, which enables the STEM problem solve, video understanding, material recognition, coding, grounding, GUI-based agents and long documents to improve understanding. Researchers opened GLM-4.1V-9b-thinking open-source, which sets a new benchmark between equally shaped models. It also provides competitive, and in some cases on challenging tasks such as the GPT -4O, such as the ownership models, such as long documents understanding and STEM logic.

GLM-4.1V- Thinking consists of three main components: one vision encoder, an MLP adapter and an LLM decoder. It uses AIMV 2-huge as LLM in the form of vision encoders and GLM, which replaces the original 2D compliance with 3D compulsion for temporal downsampling. The model integrates 2D-Rassi to support arbitrary image resolutions and aspect ratio, and processes high resolutions beyond the extreme aspect ratio of more than 1 and 4K. Researchers expand rope to 3D-Rassi in LLM to improve spatial understanding in multimodal contexts. For temporal modeling in the video, time index tokens are added after each frame token, timestamps are encoded as strings so that the model can help understand the temporary interval of the real world between the frame

During pre-training, researchers use a variety of datasets, enrich large academic corpora in knowledge with interlative image-text data. By incorporating pure text data, the main language capabilities of the model are preserved, resulting in a better pass@K compared to other state-of-the-art pre-educated base models of the same size. The supervised fine-tuning phase base is capable of enabling VLM in a long-term cottage invention, using a curated long-cot corpus like stem problems, and is like non-versatile functions in the form of instructions. Finally, the RL phase employs the combination of RLVR and RLHF to conduct mass training in all multimodal domains, including STEM problem solution, grounding, optical character recognition, GUI agents and many more.

GLM-4.1V-9B-Thinking outperform all competitive open-sources models under 10B parameters in normal VQA functions covering both all single-images and multi-image settings. It receives the highest performance on the challenging Stem benchmark including MMMU_VAL, MMMU_PRO, VideoMMU and AI2D. In the OCR and chart domains, the model sets new state -of -the -art scores on Chartqapro and Chartmuseum. For the understanding of long documents, GLM-4.1V-9B-Thinking GUI leads all other models on mmlongbench, establishing new state-of-the-art results in agents and multimodal coding tasks. Finally, the model shows strong videos intelligent performance, vyomes, MMVUs, and motionbench benchmarks.

Finally, the researchers introduced the GLM-4.1V-thinking, which represents a step towards the common-purpose multimodal logic. Its 9B-parameter model improves large models that exceed 70B parameters. However, many limitations remain, such as inconsistent improvement in quality through RL, instability during training, and difficulties with complex cases. Future development should focus on improving the supervision and evaluation of model logic, evaluating intermediate logic stages by detecting hallucinations and logical discrepancies with the reward model. In addition, it is important to obtain a common-purpose intelligence to discover strategies to prevent reward hacking in subjective assessment functions.

Check it paper And Github page. All credit for this research goes to the researchers of this project.

Sajjad Ansari is a final year graduation from IIT Kharagpur. As a technical enthusiast, he delays practical applications of AI with focus on understanding the impact of AI technologies and their real -world implications. He aims to clarify complex AI concepts in a clear and accessible way.