Skywork-R1V3 Technical Report
Authors: name: Wei Shen; name: Jiangbo Pei; name: Yi Peng; name: Xuchen Song; name: Yang Liu; name: Jian Peng; name: Haofeng Sun; name: Yunzhuo Hao; name: Peiyu Wang; name: Yahui Zhou
Published: 2025-07-08 16:47:16

Abstract: 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.