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
Organization: Skywork

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.