Mono-InternVL-1.5: Towards Cheaper and Faster Monolithic Multimodal
  Large Language Models
This paper focuses on monolithic Multimodal Large Language Models (MLLMs),
which integrate visual encoding and language decoding into a single model.
Existing structures and pre-training strategies for monolithic MLLMs often
suffer from unstable optimization and catastrophic forgetting. To address these
challenges, our key idea is to embed a new visual parameter space into a
pre-trained LLM, enabling stable learning of visual knowledge from noisy data
via delta tuning. Based on this principle, we first introduce Mono-InternVL, an
advanced monolithic MLLM that incorporates a set of visual experts through a
multimodal mixture-of-experts architecture. In addition, we design an
innovative Endogenous Visual Pre-training (EViP) for Mono-InternVL to maximize
its visual capabilities via progressive learning. Mono-InternVL achieves
competitive performance against existing MLLMs but also leads to relatively
expensive data cost. Therefore, we further present Mono-InternVL-1.5, a cheaper
and stronger monolithic MLLM equipped with an improved EViP (EViP++). EViP++
introduces additional visual attention experts to Mono-InternVL-1.5 and
re-organizes the pre-training process in an efficient manner. During inference,
it includes a fused CUDA kernel to speed up its MoE operations. With these
designs, Mono-InternVL-1.5 significantly reduces training and inference costs,
while still maintaining competitive performance with Mono-InternVL. To evaluate
our approach, we conduct extensive experiments across 15 benchmarks. Results
demonstrate that Mono-InternVL outperforms existing monolithic MLLMs on 12 out
of 15 benchmarks, e.g., +114-point improvement over Emu3 on OCRBench. Compared
to its modular counterpart, i.e., InternVL-1.5, Mono-InternVL-1.5 achieves
similar multimodal performance while reducing first-token latency by up to 69%.
Code and models are released at https://github.com/OpenGVLab/Mono-InternVL.
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Gen Luo
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Wenhan Dou
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2025-07-21T15:59:10.836Z
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dou wenhan
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douwh
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Wenhao Li
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Zhaokai Wang
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Zhaokai Wang
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Xue Yang
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Changyao Tian
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2025-07-21T15:58:59.906Z
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Changyao Tian
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Changyao
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Hao Li
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Weiyun Wang
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Weiyun Wang
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Weiyun1025
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Wenhai Wang
687dd61e2e8db0930be6f110
Xizhou Zhu
admin_assigned
2025-07-21T15:58:49.646Z
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Xizhou Zhu
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Einsiedler
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Yu Qiao
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Jifeng Dai
admin_assigned
2025-07-21T15:58:43.488Z
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Jifeng Dai
user
daijifeng
Mono-InternVL-1.5: Towards Cheaper and Faster Monolithic Multimodal
  Large Language Models
This paper focuses on monolithic Multimodal Large Language Models (MLLMs),
which integrate visual encoding and language decoding into a single model.
Existing structures and pre-training strategies for monolithic MLLMs often
suffer from unstable optimization and catastrophic forgetting. To address these
challenges, our key idea is to embed a new visual parameter space into a
pre-trained LLM, enabling stable learning of visual knowledge from noisy data
via delta tuning. Based on this principle, we first introduce Mono-InternVL, an
advanced monolithic MLLM that incorporates a set of visual experts through a
multimodal mixture-of-experts architecture. In addition, we design an
innovative Endogenous Visual Pre-training (EViP) for Mono-InternVL to maximize
its visual capabilities via progressive learning. Mono-InternVL achieves
competitive performance against existing MLLMs but also leads to relatively
expensive data cost. Therefore, we further present Mono-InternVL-1.5, a cheaper
and stronger monolithic MLLM equipped with an improved EViP (EViP++). EViP++
introduces additional visual attention experts to Mono-InternVL-1.5 and
re-organizes the pre-training process in an efficient manner. During inference,
it includes a fused CUDA kernel to speed up its MoE operations. With these
designs, Mono-InternVL-1.5 significantly reduces training and inference costs,
while still maintaining competitive performance with Mono-InternVL. To evaluate
our approach, we conduct extensive experiments across 15 benchmarks. Results
demonstrate that Mono-InternVL outperforms existing monolithic MLLMs on 12 out
of 15 benchmarks, e.g., +114-point improvement over Emu3 on OCRBench. Compared
to its modular counterpart, i.e., InternVL-1.5, Mono-InternVL-1.5 achieves
similar multimodal performance while reducing first-token latency by up to 69%.
Code and models are released at https://github.com/OpenGVLab/Mono-InternVL.
Mono-InternVL, an advanced monolithic Multimodal Large Language Model, integrates visual experts and employs Endogenous Visual Pre-training to enhance visual learning and reduce computational costs.
Multimodal Large Language Models monolithic MLLMs visual parameter space delta tuning multimodal mixture-of-experts architecture Endogenous Visual Pre-training EViP EViP++ visual attention experts CUDA kernel MoE operations OCRBench first-token latency