# Reasoning Capability

InternVL2_5-26B-MPO-AWQ is a multimodal large language model developed by OpenGVLab, designed to enhance model reasoning capabilities through hybrid preference optimization. This model excels in multimodal tasks, effectively managing the complex relationships between images and text. It utilizes cutting-edge model architecture and optimization techniques, providing significant advantages in handling multimodal data. The model is ideal for scenarios requiring efficient processing and understanding of multimodal data, such as image description generation and multimodal question answering. Its main advantages include powerful reasoning capabilities and an efficient model architecture.

PRIME-RL/Eurus-2-7B-PRIME is a language model with 7 billion parameters, trained on the PRIME methodology with the aim of improving reasoning abilities via online reinforcement learning. Starting from the Eurus-2-7B-SFT model, this model was fine-tuned using the Eurus-2-RL-Data dataset. The PRIME methodology employs an implicit reward system, fostering an emphasis on the reasoning process during output generation, rather than focusing solely on the results. This model has demonstrated exceptional performance in various reasoning benchmark tests, achieving an average improvement of 16.7% over its SFT version. Key advantages include enhanced reasoning capabilities, lower data and resource requirements, and outstanding performance in mathematical and programming tasks. It is well-suited for scenarios requiring complex reasoning abilities, such as programming and mathematical problem solving.

PRIME is an open-source online reinforcement learning solution that boosts the reasoning capabilities of language models through implicit process rewards. One of the main advantages of this technology is its ability to provide dense reward signals effectively without relying on explicit process labels, thus accelerating both model training and enhancements in reasoning abilities. PRIME performs exceptionally well in mathematical competition benchmarks, surpassing existing large language models. It has been collaboratively developed by multiple researchers and has relevant code and datasets published on GitHub. PRIME is positioned to provide robust model support for users requiring complex reasoning tasks.