Skywork-R1V3 Technical Report
We introduce Skywork-R1V3, an advanced, open-source vision-language model
(VLM) that pioneers a new approach to visual reasoning. Its key innovation lies
in effectively transferring reasoning skills from text-only Large Language
Models (LLMs) to visual tasks. The strong performance of Skywork-R1V3 primarily
stems from our elaborate post-training RL framework, which effectively
activates and enhances the model's reasoning ability, without the need for
additional continue pre-training. Through this framework, we further uncover
the fundamental role of the connector module in achieving robust cross-modal
alignment for multimodal reasoning models. In addition, we introduce a unique
indicator of reasoning capability, the entropy of critical reasoning tokens,
which has proven highly effective for checkpoint selection during RL training.
Skywork-R1V3 achieves state-of-the-art results on MMMU, significantly improving
from 64.3% to 76.0%. This performance matches entry-level human capabilities.
Remarkably, our RL-powered post-training approach enables even the 38B
parameter model to rival top closed-source VLMs. The implementation
successfully transfers mathematical reasoning to other subject-related
reasoning tasks. We also include an analysis of curriculum learning and
reinforcement finetuning strategies, along with a broader discussion on
multimodal reasoning. Skywork-R1V3 represents a significant leap in multimodal
reasoning, showcasing RL as a powerful engine for advancing open-source VLM
capabilities.
686dc8b5cb5725779c60b36d
Wei Shen
686dc8b5cb5725779c60b36e
Jiangbo Pei
686dc8b5cb5725779c60b36f
Yi Peng
686dc8b5cb5725779c60b370
Xuchen Song
686dc8b5cb5725779c60b371
Yang Liu
686dc8b5cb5725779c60b372
Jian Peng
686dc8b5cb5725779c60b373
Haofeng Sun
686dc8b5cb5725779c60b374
Yunzhuo Hao
686dc8b5cb5725779c60b375
Peiyu Wang
claimed_verified
2025-07-11T08:03:51.106Z
620f5a1c3f76c50e6458a9b6
https://cdn-avatars.huggingface.co/v1/production/uploads/620f5a1c3f76c50e6458a9b6/pXh_f5F0UvufxuUa-eS-v.jpeg
Peiyu Wang
user
OrlandoHugBot
686dc8b5cb5725779c60b376
Yahui Zhou
Skywork-R1V3 Technical Report
We introduce Skywork-R1V3, an advanced, open-source vision-language model
(VLM) that pioneers a new approach to visual reasoning. Its key innovation lies
in effectively transferring reasoning skills from text-only Large Language
Models (LLMs) to visual tasks. The strong performance of Skywork-R1V3 primarily
stems from our elaborate post-training RL framework, which effectively
activates and enhances the model's reasoning ability, without the need for
additional continue pre-training. Through this framework, we further uncover
the fundamental role of the connector module in achieving robust cross-modal
alignment for multimodal reasoning models. In addition, we introduce a unique
indicator of reasoning capability, the entropy of critical reasoning tokens,
which has proven highly effective for checkpoint selection during RL training.
Skywork-R1V3 achieves state-of-the-art results on MMMU, significantly improving
from 64.3% to 76.0%. This performance matches entry-level human capabilities.
Remarkably, our RL-powered post-training approach enables even the 38B
parameter model to rival top closed-source VLMs. The implementation
successfully transfers mathematical reasoning to other subject-related
reasoning tasks. We also include an analysis of curriculum learning and
reinforcement finetuning strategies, along with a broader discussion on
multimodal reasoning. Skywork-R1V3 represents a significant leap in multimodal
reasoning, showcasing RL as a powerful engine for advancing open-source VLM
capabilities.
Skywork-R1V3, an open-source vision-language model, enhances visual reasoning through a post-training reinforcement learning framework, achieving state-of-the-art performance on multimodal reasoning tasks.
vision-language model visual reasoning Large Language Models post-training RL framework connector module cross-modal alignment multimodal reasoning models entropy of critical reasoning tokens reinforcement finetuning curriculum learning