InternVL3.5: Advancing Open-Source Multimodal Models in Versatility,
  Reasoning, and Efficiency
Authors: name: Weiyun Wang, name: Zhangwei Gao, name: Lixin Gu, name: Hengjun Pu, name: Long Cui, name: Xingguang Wei, name: Zhaoyang Liu, name: Linglin Jing, name: Shenglong Ye, name: Jie Shao, name: Zhaokai Wang, name: Zhe Chen, name: Hongjie Zhang, name: Ganlin Yang, name: Haomin Wang, name: Qi Wei, name: Jinhui Yin, name: Wenhao Li, name: Erfei Cui, name: Guanzhou Chen, name: Zichen Ding, name: Changyao Tian, name: Zhenyu Wu, name: Jingjing Xie, name: Zehao Li, name: Bowen Yang, name: Yuchen Duan, name: Xuehui Wang, name: Songze Li, name: Xiangyu Zhao, name: Haodong Duan, name: Nianchen Deng, name: Bin Fu, name: Yinan He, name: Yi Wang, name: Conghui He, name: Botian Shi, name: Junjun He, name: Yingtong Xiong, name: Han Lv, name: Lijun Wu, name: Wenqi Shao, name: Kaipeng Zhang, name: Huipeng Deng, name: Biqing Qi, name: Jiaye Ge, name: Qipeng Guo, name: Wenwei Zhang, name: Wanli Ouyang, name: Limin Wang, name: Min Dou, name: Xizhou Zhu, name: Tong Lu, name: Dahua Lin, name: Jifeng Dai, name: Bowen Zhou, name: Weijie Su, name: Kai Chen, name: Yu Qiao, name: Wenhai Wang, name: Gen Luo

We introduce InternVL 3.5, a new family of open-source multimodal models that
significantly advances versatility, reasoning capability, and inference
efficiency along the InternVL series. A key innovation is the Cascade
Reinforcement Learning (Cascade RL) framework, which enhances reasoning through
a two-stage process: offline RL for stable convergence and online RL for
refined alignment. This coarse-to-fine training strategy leads to substantial
improvements on downstream reasoning tasks, e.g., MMMU and MathVista. To
optimize efficiency, we propose a Visual Resolution Router (ViR) that
dynamically adjusts the resolution of visual tokens without compromising
performance. Coupled with ViR, our Decoupled Vision-Language Deployment (DvD)
strategy separates the vision encoder and language model across different GPUs,
effectively balancing computational load. These contributions collectively
enable InternVL3.5 to achieve up to a +16.0\% gain in overall reasoning
performance and a 4.05times inference speedup compared to its predecessor,
i.e., InternVL3. In addition, InternVL3.5 supports novel capabilities such as
GUI interaction and embodied agency. Notably, our largest model, i.e.,
InternVL3.5-241B-A28B, attains state-of-the-art results among open-source MLLMs
across general multimodal, reasoning, text, and agentic tasks -- narrowing the
performance gap with leading commercial models like GPT-5. All models and code
are publicly released.