Skywork R1V: Pioneering Multimodal Reasoning with Chain-of-Thought
We introduce Skywork R1V, a multimodal reasoning model extending the an
R1-series Large language models (LLM) to visual modalities via an efficient
multimodal transfer method. Leveraging a lightweight visual projector, Skywork
R1V facilitates seamless multimodal adaptation without necessitating retraining
of either the foundational language model or the vision encoder. To strengthen
visual-text alignment, we propose a hybrid optimization strategy that combines
Iterative Supervised Fine-Tuning (SFT) with Group Relative Policy Optimization
(GRPO), significantly enhancing cross-modal integration efficiency.
Additionally, we introduce an adaptive-length Chain-of-Thought distillation
approach for reasoning data generation. This approach dynamically optimizes
reasoning chain lengths, thereby enhancing inference efficiency and preventing
excessive reasoning overthinking. Empirical evaluations demonstrate that
Skywork R1V, with only 38B parameters, delivers competitive performance,
achieving a score of 69.0 on the MMMU benchmark and 67.5 on MathVista.
Meanwhile, it maintains robust textual reasoning performance, evidenced by
impressive scores of 72.0 on AIME and 94.0 on MATH500. The Skywork R1V model
weights have been publicly released to promote openness and reproducibility.
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Yang Liu
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Yahui Zhou
Skywork R1V: Pioneering Multimodal Reasoning with Chain-of-Thought
We introduce Skywork R1V, a multimodal reasoning model extending the an
R1-series Large language models (LLM) to visual modalities via an efficient
multimodal transfer method. Leveraging a lightweight visual projector, Skywork
R1V facilitates seamless multimodal adaptation without necessitating retraining
of either the foundational language model or the vision encoder. To strengthen
visual-text alignment, we propose a hybrid optimization strategy that combines
Iterative Supervised Fine-Tuning (SFT) with Group Relative Policy Optimization
(GRPO), significantly enhancing cross-modal integration efficiency.
Additionally, we introduce an adaptive-length Chain-of-Thought distillation
approach for reasoning data generation. This approach dynamically optimizes
reasoning chain lengths, thereby enhancing inference efficiency and preventing
excessive reasoning overthinking. Empirical evaluations demonstrate that
Skywork R1V, with only 38B parameters, delivers competitive performance,
achieving a score of 69.0 on the MMMU benchmark and 67.5 on MathVista.
Meanwhile, it maintains robust textual reasoning performance, evidenced by
impressive scores of 72.0 on AIME and 94.0 on MATH500. The Skywork R1V model
weights have been publicly released to promote openness and reproducibility.
Skywork R1V extends large language models to multimodal reasoning with efficient transfer, enhanced visual-text alignment, and dynamic reasoning chain optimization, achieving competitive performance in various benchmarks.
multimodal reasoning model R1-series Large language models multimodal transfer method lightweight visual projector Iterative Supervised Fine-Tuning Group Relative Policy Optimization adaptive-length Chain-of-Thought distillation MMMU benchmark MathVista AIME MATH500