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
Published: 2025-08-25 17:58:17

Abstract: 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.