Reproduce “Qwen3-VL Technical Report” on OpenResearch

About this paper

Shuai Bai, Yuxuan Cai, Ruizhe Chen, Keqin Chen, Xionghui Chen, Zesen Cheng, Lianghao Deng, Wei Ding and 56 more

The Qwen Team at Alibaba Group introduced Qwen3-VL, an open-source family of vision-language models that integrates text, image, and video understanding with native ultra-long context support up to 256K tokens. It achieved state-of-the-art results across a wide range of multimodal benchmarks, including VQA, multimodal reasoning, grounding, and video understanding, demonstrating over 70% accuracy for 32 out of 39 supported OCR languages.

Key insights

• Architectural upgrades like interleaved MRoPE and DeepStack are effective in enhancing spatio-temporal understanding and fine-grained vision-language alignment without significantly increasing context length.
• A multi-stage training pipeline with a data-centric overhaul, including specific datasets for complex reasoning and agentic tasks, is critical for developing robust and versatile multimodal foundation models.
• Bifurcating models into 'thinking' (Chain-of-Thought enabled) and 'instruct' variants, along with agentic learning and tool augmentation, leads to superior reasoning and perception across diverse multimodal benchmarks.

Summary by alphaXiv.