Skywork-R1V 3.0 – A Multimodal Reasoning Model Open-Sourced by Kunlun Tech

What is Skywork-R1V 3.0

Skywork-R1V 3.0 is an open-source multimodal reasoning model developed by Skywork AI under Kunlun Wanwei. It features strong cross-modal reasoning capabilities and interdisciplinary generalization. The model achieved an impressive 142 points on China’s Gaokao mathematics test and scored 76 on the MMMU benchmark, surpassing many closed-source models and approaching the level of a junior human expert. By employing a reinforcement learning strategy, Skywork-R1V 3.0 unleashes reasoning potential with minimal data, incorporating a novel entropy-driven mechanism to select model versions with genuine reasoning ability. It also uses connector fine-tuning to balance interdisciplinary knowledge, making it widely applicable in education, scientific research, healthcare, and more—offering vital technical support for the development of multimodal intelligence.

Key Features of Skywork-R1V 3.0

• Cross-modal Reasoning: Capable of interpreting and analyzing combined image and text inputs, such as understanding force diagrams in physics or analyzing electric circuits.

• Interdisciplinary Generalization: Excels across a wide range of academic domains including mathematics, physics, geography, history, medicine, and art, handling complex interdisciplinary problems effectively.

• Logical and Mathematical Reasoning: Performs strongly in solving logical problems and advanced math questions.

• Educational & Research Applications: Supports intelligent tutoring in education and assists in data analysis and model validation in scientific research.

• Efficient Knowledge Transfer: Uses reinforcement learning to transfer reasoning abilities across domains, enhancing generalization across fields.

Technical Principles Behind Skywork-R1V 3.0

• Reinforcement Learning with GRPO: Utilizes Group Relative Policy Optimization (GRPO) to deeply activate the model’s reasoning abilities and enable reasoning transfer between image and text modalities.

• Entropy-driven Selection Mechanism: Monitors entropy values at key output points during training to select model versions with true reasoning abilities and avoid rote learning.

• Cold Start with Data Distillation: Uses distilled data from previous-generation models to build a high-quality multimodal reasoning training set, helping the model learn fundamental reasoning formats and techniques.

• Connector Fine-tuning: Focuses on fine-tuning cross-modal connectors to optimize the fusion of knowledge from different fields and improve performance in non-mathematical domains.

• Small Data, High Efficiency: Achieves powerful capabilities using only ~12,000 supervised fine-tuning samples and ~13,000 reinforcement learning samples, demonstrating a “small data, big ability” training approach.

Project Links

• GitHub Repository: https://github.com/SkyworkAI/Skywork-R1V

• HuggingFace Model Page: https://huggingface.co/Skywork/Skywork-R1V3-38B

• Technical Paper: Skywork_R1V3.pdf

Application Scenarios

• Education: Offers personalized tutoring for students, solving complex problems in subjects like mathematics and physics to enhance learning outcomes.

• Healthcare: Combines medical images with clinical texts to support physicians in accurate and efficient disease diagnosis.

• Scientific Research: Assists researchers in analyzing experimental data and extracting key insights, supporting interdisciplinary exploration and theoretical reasoning.

• Art and Design: Inspires artists by analyzing styles of artwork and generating new creative ideas to boost artistic productivity.

• Business Intelligence: Analyzes market trends and consumer feedback to assist enterprises in strategic decision-making.