title: Skywork R1V2: Multimodal Hybrid Reinforcement Learning for Reasoning
summary: We present Skywork R1V2, a next-generation multimodal reasoning model and a
  major leap forward from its predecessor, Skywork R1V. At its core, R1V2
  introduces a hybrid reinforcement learning paradigm that harmonizes
  reward-model guidance with rule-based strategies, thereby addressing the
  long-standing challenge of balancing sophisticated reasoning capabilities with
  broad generalization. To further enhance training efficiency, we propose the
  Selective Sample Buffer (SSB) mechanism, which effectively counters the
  ``Vanishing Advantages'' dilemma inherent in Group Relative Policy Optimization
  (GRPO) by prioritizing high-value samples throughout the optimization process.
  Notably, we observe that excessive reinforcement signals can induce visual
  hallucinations--a phenomenon we systematically monitor and mitigate through
  calibrated reward thresholds throughout the training process. Empirical results
  affirm the exceptional capability of R1V2, with benchmark-leading performances
  such as 62.6 on OlympiadBench, 79.0 on AIME2024, 63.6 on LiveCodeBench, and
  74.0 on MMMU. These results underscore R1V2's superiority over existing
  open-source models and demonstrate significant progress in closing the
  performance gap with premier proprietary systems, including Gemini 2.5 and
  OpenAI o4-mini. The Skywork R1V2 model weights have been publicly released to
  promote openness and reproducibility
  https://huggingface.co/Skywork/Skywork-R1V2-38B.
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paper_authors.name: Wei Shen
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paper_authors.name: Yang Liu
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paper_authors.name: Yahui Zhou
paper_title: Skywork R1V2: Multimodal Hybrid Reinforcement Learning for Reasoning
paper_summary: We present Skywork R1V2, a next-generation multimodal reasoning model and a
  major leap forward from its predecessor, Skywork R1V. At its core, R1V2
  introduces a hybrid reinforcement learning paradigm that harmonizes
  reward-model guidance with rule-based strategies, thereby addressing the
  long-standing challenge of balancing sophisticated reasoning capabilities with
  broad generalization. To further enhance training efficiency, we propose the
  Selective Sample Buffer (SSB) mechanism, which effectively counters the
  ``Vanishing Advantages'' dilemma inherent in Group Relative Policy Optimization
  (GRPO) by prioritizing high-value samples throughout the optimization process.
  Notably, we observe that excessive reinforcement signals can induce visual
  hallucinations--a phenomenon we systematically monitor and mitigate through
  calibrated reward thresholds throughout the training process. Empirical results
  affirm the exceptional capability of R1V2, with benchmark-leading performances
  such as 62.6 on OlympiadBench, 79.0 on AIME2024, 63.6 on LiveCodeBench, and
  74.0 on MMMU. These results underscore R1V2's superiority over existing
  open-source models and demonstrate significant progress in closing the
  performance gap with premier proprietary systems, including Gemini 2.5 and
  OpenAI o4-mini. The Skywork R1V2 model weights have been publicly released to
  promote openness and reproducibility
  https://huggingface.co/Skywork/Skywork-R1V2-38B.
paper_ai_summary: Skywork R1V2 enhances multimodal reasoning through a hybrid reinforcement learning approach that balances reward-model guidance and rule-based strategies, improving training efficiency with the Selective Sample Buffer mechanism and mitigating visual hallucinations.
paper_ai_keywords: hybrid reinforcement learning reward-model guidance rule-based strategies Selective Sample Buffer (SSB) Vanishing Advantages Group Relative Policy Optimization (GRPO) visual hallucinations OlympiadBench AIME2024 LiveCodeBench MMMU