InternVL3: Exploring Advanced Training and Test-Time Recipes for
  Open-Source Multimodal Models
We introduce InternVL3, a significant advancement in the InternVL series
featuring a native multimodal pre-training paradigm. Rather than adapting a
text-only large language model (LLM) into a multimodal large language model
(MLLM) that supports visual inputs, InternVL3 jointly acquires multimodal and
linguistic capabilities from both diverse multimodal data and pure-text corpora
during a single pre-training stage. This unified training paradigm effectively
addresses the complexities and alignment challenges commonly encountered in
conventional post-hoc training pipelines for MLLMs. To further improve
performance and scalability, InternVL3 incorporates variable visual position
encoding (V2PE) to support extended multimodal contexts, employs advanced
post-training techniques such as supervised fine-tuning (SFT) and mixed
preference optimization (MPO), and adopts test-time scaling strategies
alongside an optimized training infrastructure. Extensive empirical evaluations
demonstrate that InternVL3 delivers superior performance across a wide range of
multi-modal tasks. In particular, InternVL3-78B achieves a score of 72.2 on the
MMMU benchmark, setting a new state-of-the-art among open-source MLLMs. Its
capabilities remain highly competitive with leading proprietary models,
including ChatGPT-4o, Claude 3.5 Sonnet, and Gemini 2.5 Pro, while also
maintaining strong pure-language proficiency. In pursuit of open-science
principles, we will publicly release both the training data and model weights
to foster further research and development in next-generation MLLMs.
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InternVL3: Exploring Advanced Training and Test-Time Recipes for
  Open-Source Multimodal Models
We introduce InternVL3, a significant advancement in the InternVL series
featuring a native multimodal pre-training paradigm. Rather than adapting a
text-only large language model (LLM) into a multimodal large language model
(MLLM) that supports visual inputs, InternVL3 jointly acquires multimodal and
linguistic capabilities from both diverse multimodal data and pure-text corpora
during a single pre-training stage. This unified training paradigm effectively
addresses the complexities and alignment challenges commonly encountered in
conventional post-hoc training pipelines for MLLMs. To further improve
performance and scalability, InternVL3 incorporates variable visual position
encoding (V2PE) to support extended multimodal contexts, employs advanced
post-training techniques such as supervised fine-tuning (SFT) and mixed
preference optimization (MPO), and adopts test-time scaling strategies
alongside an optimized training infrastructure. Extensive empirical evaluations
demonstrate that InternVL3 delivers superior performance across a wide range of
multi-modal tasks. In particular, InternVL3-78B achieves a score of 72.2 on the
MMMU benchmark, setting a new state-of-the-art among open-source MLLMs. Its
capabilities remain highly competitive with leading proprietary models,
including ChatGPT-4o, Claude 3.5 Sonnet, and Gemini 2.5 Pro, while also
maintaining strong pure-language proficiency. In pursuit of open-science
principles, we will publicly release both the training data and model weights
to foster further research and development in next-generation MLLMs.
InternVL3 is a multimodal pre-trained language model that jointly learns from both multimodal data and text, improving performance and scalability through advanced techniques and setting a new state-of-the-art in multimodal tasks.
multimodal pre-training large language model multimodal large language model variable visual position encoding supervised fine-tuning mixed preference optimization test-time scaling MMLM pure-language proficiency MMOU benchmark