title: Skywork R1V: Pioneering Multimodal Reasoning with Chain-of-Thought
summary: 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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paper_authors.name: Ai Jian
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paper_authors.name: Yang Liu
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paper_authors.name: Yahui Zhou
paper_title: Skywork R1V: Pioneering Multimodal Reasoning with Chain-of-Thought
paper_summary: 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.
paper_ai_summary: 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.
paper_ai_keywords: 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