The success of Step3-VL-10B can be attributed to its two core designs: high-quality multimodal pre-training and large-scale multimodal reinforcement learning. The model is trained on a massive dataset of 1.2T tokens and undergoes over 1,400 iterations of reinforcement learning, allowing it to develop a deep understanding of various tasks and domains. This enables it to perform exceptionally well in benchmark tests, including MMMU, MathVision, and MMBench.
Step3-VL-10B has a wide range of applications, including but not limited to, STEM reasoning, recognition, OCR, GUI grounding, spatial understanding, and code generation. The model's architecture consists of a visual encoder, decoder, and projector, which work together to process and generate high-quality outputs. The model's performance is further enhanced by its ability to aggregate evidence from multiple sources, making it a powerful tool for various tasks and applications.
