InternVL3.5: Advancing Open-Source Multimodal Models in Versatility,
  Reasoning, and Efficiency
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.
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Zichen Ding
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Gen Luo
InternVL3.5: Advancing Open-Source Multimodal Models in Versatility,
  Reasoning, and Efficiency
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.
InternVL 3.5 introduces Cascade RL, ViR, and DvD to enhance reasoning, efficiency, and performance in multimodal models.
Cascade RL offline RL online RL Visual Resolution Router ViR Decoupled Vision-Language Deployment DvD multimodal models reasoning performance inference speedup GUI interaction embodied agency