Skywork-R1V 2.0 - Kunlun Tech's new open-source multimodal inference model
Skywork-R1V 2.0 is Kunlun Wanwei's latest open-source multimodal reasoning model, designed specifically for complex reasoning tasks, possessing powerful visual and textual reasoning capabilities. The model is based on a hybrid reinforcement learning and multimodal reward model (Skywork...
What is Skywork-R1V 2.0?
Skywork-R1V 2.0 is Kunlun Wanwei's latest open-source multimodal inference model, designed specifically for complex inference tasks, possessing powerful visual and textual inference capabilities. The model achieves a balance between inference and generalization abilities based on a hybrid reinforcement learning and multimodal reward model (Skywork-VL Reward), and introduces a Selective Sample Buffer (SSB) mechanism to address the "vanishing advantage" problem. It performs exceptionally well in authoritative benchmark tests such as AIME2024 and OlympiadBench, with performance approaching or even surpassing some closed-source models. The model weights and code are fully open-sourced, promoting the development of the multimodal ecosystem and supporting education, scientific research, and other fields.
Main features of Skywork-R1V 2.0
- Complex reasoning tasksIt supports handling complex math, physics, chemistry and other STEM problems, providing in-depth reasoning and problem-solving strategies.
- Multimodal understandingIt combines textual and image information to conduct integrated visual and linguistic reasoning.
- General task adaptation: Performs well in general tasks such as creative writing and open-ended questions.
- Educational SupportAs a problem-solving assistant for science questions in the college entrance examination, it helps students understand and solve complex mathematical, physical, and chemical problems.
- Scientific researchIt supports scientific analysis and experimental design, and provides logical reasoning and data analysis capabilities.
- Programming competitionIt assists in solving algorithmic problems in programming competitions, providing suggestions for code generation and debugging.
Technical Principles of Skywork-R1V 2.0
- Hybrid reinforcement learningThis approach combines a multimodal reward model (Skywork-VL Reward) with rule-driven feedback to provide high-quality reward signals, balancing inference and generalization capabilities. It also utilizes a selective sample buffer (SSB) mechanism to address the vanishing advantage problem in reinforcement learning, thereby improving training efficiency.
- Hybrid Preference Optimization (MPO) is employed, which combines preference signals and rule feedback to improve the model's inference ability and format compliance.
- Multimodal fusionThis approach leverages a lightweight MLP adapter to connect the visual encoder (InternViT-6B) and language model (such as QwQ-32B), reducing reliance on large-scale multimodal data. It directly combines a pre-trained language model with the visual adapter, preserving reasoning capabilities while enhancing visual understanding.
- Modular ReorganizationModular design allows for independent optimization of the vision and language modules while maintaining efficient cross-modal alignment. Different combinations of training the visual encoder, adapter, and language model enhance the overall performance of the model.
- Training strategy:
- Group Relative Policy Optimization (GRPO)The model is optimized based on the relative reward comparison of candidate responses within the group.
- Multiple loss functions of MPOThis includes quality loss (BCO) and generation loss (SFT), which improve the stability and generalization ability of the model.
Skywork-R1V 2.0 project address
- HuggingFace model library:https://huggingface.co/Skywork/Skywork-R1V2-38B
- arXiv technical paper:https://arxiv.org/pdf/2504.16656
Application scenarios of Skywork-R1V 2.0
- Educational SupportIt helps students solve difficult science problems in the college entrance examination by providing problem-solving ideas and steps.
- Scientific researchIt assists researchers in experimental design, data analysis, and literature knowledge extraction.
- Programming DevelopmentProvides code generation, debugging, and optimization suggestions for programming competitions and software development.
- Creative Writing: Assist creators in generating creative content and answering open-ended questions.
- Multimodal understandingTasks involving combining images and text, and analyzing multimedia content.