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Skywork R1V: The World's First Industrial-Grade Open-Source Multimodal Reasoning Model by Kunlun
What is Skywork R1V?
Skywork R1V is a multimodal chain-of-thought reasoning model open-sourced by Kunlun Wanwei, officially released on March 18, 2025.
Skywork R1V is the world's first industrial-grade open-source multimodal reasoning model, focused on solving complex visual tasks through multi-step logical reasoning. It efficiently transfers text reasoning capabilities to visual tasks, achieving deep integration of visual and textual modalities. Skywork R1V can not only understand images and text but also perform multi-step logical reasoning, making it particularly adept at handling complex image-based problems.
Skywork R1V Features
Visual Chain-of-Thought Reasoning: Capable of performing multi-step logical reasoning on complex visual tasks, analyzing step by step and arriving at conclusions.
Math and Science Problem Solving: Can identify math or science problems in images and provide step-by-step solutions combined with reasoning capabilities.
Cross-Modal Fusion: Deeply integrates visual and textual information to enhance semantic understanding.
Efficient Reasoning Optimization: Dynamically adjusts reasoning depth through adaptive-length chain-of-thought distillation, saving computational resources and improving efficiency.
Strong Reasoning and Visual Comprehension Capabilities: Performs excellently across multiple benchmarks, with both logical reasoning and visual comprehension abilities reaching high levels.
Skywork R1V Technical Principles
Multimodal Transfer of Text Reasoning Capabilities: Through a Visual Projector, text reasoning capabilities can be transferred to visual tasks without retraining the language model and visual encoder.
Multimodal Hybrid Training (Iterative SFT + GRPO): Combines Iterative Supervised Fine-Tuning (Iterative SFT) and Group Relative Policy Optimization (GRPO) reinforcement learning, aligning visual and textual representations in stages to improve the model's performance on cross-modal tasks.
Adaptive-Length Chain-of-Thought Distillation: Introduces an adaptive reasoning chain length control mechanism based on visual-textual complexity, dynamically optimizing the model's reasoning process, avoiding "overthinking," and improving reasoning efficiency and quality.
Three-Stage Training Method:
Initial Alignment: A lightweight visual adapter (MLP) is used to connect the visual encoder and language model, trained on conventional multimodal data to initially align visual and language representations.
Reasoning Capability Transfer: The trained adapter is connected with a strong reasoning language model to form a visual reasoning model, granting the model initial visual reasoning capabilities.
Precise Alignment: Based on a hybrid optimization framework (Iterative SFT + GRPO), visual and language modalities are further precisely aligned to enhance multimodal reasoning capabilities.
Skywork R1V Performance
Logical Reasoning Capabilities:
Skywork R1V achieved a high score of 94.0 on the MATH-500 benchmark.
On the aiME 2024 benchmark, the pass rate reached 72.0%.
On the GPQA (General Physics Question Answering) benchmark, the pass rate reached 61.6%.
Visual Comprehension Capabilities:
Achieved a score of 67.5 on the MathVista (visual mathematical reasoning) benchmark.
Achieved a score of 69.0 on the MMMU (Multimodal Medical Understanding) benchmark.
Skywork R1V Application Scenarios
Educational Tutoring: Helps students solve problems in subjects such as mathematics and physics, providing solution steps and analysis.
Medical Image Analysis: Assists doctors in analyzing medical images, reasoning about pathological features, and providing diagnostic suggestions.
Scientific Research Assistance: Analyzes experimental images and literature, reasons about scientific phenomena, and helps researchers verify results.
Content Creation and Review: Analyzes artworks, detects non-compliant content, and assists in art appreciation and content review.
Industrial Quality Inspection and Market Analysis: Detects product defects, analyzes advertising and market data, and assists in quality control and business decision-making.
Related Links
GitHub Repository:https://github.com/SkyworkAI/Skywork-R1V
Hugging Face Model Library:https://huggingface.co/Skywork/Skywork-R1V
Technical Paper:https://github.com/SkyworkAI/Skywork-R1V/blob/main/Skywork_R1V.pdf
