title: Insight-V: Exploring Long-Chain Visual Reasoning with Multimodal Large
    Language Models
summary: Large Language Models (LLMs) demonstrate enhanced capabilities and
  reliability by reasoning more, evolving from Chain-of-Thought prompting to
  product-level solutions like OpenAI o1. Despite various efforts to improve LLM
  reasoning, high-quality long-chain reasoning data and optimized training
  pipelines still remain inadequately explored in vision-language tasks. In this
  paper, we present Insight-V, an early effort to 1) scalably produce long and
  robust reasoning data for complex multi-modal tasks, and 2) an effective
  training pipeline to enhance the reasoning capabilities of multi-modal large
  language models (MLLMs). Specifically, to create long and structured reasoning
  data without human labor, we design a two-step pipeline with a progressive
  strategy to generate sufficiently long and diverse reasoning paths and a
  multi-granularity assessment method to ensure data quality. We observe that
  directly supervising MLLMs with such long and complex reasoning data will not
  yield ideal reasoning ability. To tackle this problem, we design a multi-agent
  system consisting of a reasoning agent dedicated to performing long-chain
  reasoning and a summary agent trained to judge and summarize reasoning results.
  We further incorporate an iterative DPO algorithm to enhance the reasoning
  agent's generation stability and quality. Based on the popular LLaVA-NeXT model
  and our stronger base MLLM, we demonstrate significant performance gains across
  challenging multi-modal benchmarks requiring visual reasoning. Benefiting from
  our multi-agent system, Insight-V can also easily maintain or improve
  performance on perception-focused multi-modal tasks.
paper_authors._id: 673ff66bbf2e9c35b20570fe
paper_authors.name: Yuhao Dong
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paper_authors.fullname: Yuhao Dong
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paper_authors.name: Zuyan Liu
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paper_authors.name: Hai-Long Sun
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paper_authors.fullname: Hailong Sun
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paper_authors.name: Jingkang Yang
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paper_authors.fullname: Jingkang Yang
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paper_authors.name: Winston Hu
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paper_authors.fullname: Qingqiao Hu
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paper_authors._id: 673ff66bbf2e9c35b2057103
paper_authors.name: Yongming Rao
paper_authors.status: admin_assigned
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paper_authors.fullname: Yongming Rao
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paper_authors.user: raoyongming
paper_authors._id: 673ff66bbf2e9c35b2057104
paper_authors.name: Ziwei Liu
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paper_title: Insight-V: Exploring Long-Chain Visual Reasoning with Multimodal Large
    Language Models
paper_summary: Large Language Models (LLMs) demonstrate enhanced capabilities and
  reliability by reasoning more, evolving from Chain-of-Thought prompting to
  product-level solutions like OpenAI o1. Despite various efforts to improve LLM
  reasoning, high-quality long-chain reasoning data and optimized training
  pipelines still remain inadequately explored in vision-language tasks. In this
  paper, we present Insight-V, an early effort to 1) scalably produce long and
  robust reasoning data for complex multi-modal tasks, and 2) an effective
  training pipeline to enhance the reasoning capabilities of multi-modal large
  language models (MLLMs). Specifically, to create long and structured reasoning
  data without human labor, we design a two-step pipeline with a progressive
  strategy to generate sufficiently long and diverse reasoning paths and a
  multi-granularity assessment method to ensure data quality. We observe that
  directly supervising MLLMs with such long and complex reasoning data will not
  yield ideal reasoning ability. To tackle this problem, we design a multi-agent
  system consisting of a reasoning agent dedicated to performing long-chain
  reasoning and a summary agent trained to judge and summarize reasoning results.
  We further incorporate an iterative DPO algorithm to enhance the reasoning
  agent's generation stability and quality. Based on the popular LLaVA-NeXT model
  and our stronger base MLLM, we demonstrate significant performance gains across
  challenging multi-modal benchmarks requiring visual reasoning. Benefiting from
  our multi-agent system, Insight-V can also easily maintain or improve
  performance on perception-focused multi-modal tasks.
paper_ai_summary: Insight-V enhances multi-modal large language models through scalable reasoning data generation and a multi-agent system, achieving performance improvements in visual and perceptual tasks.
paper_ai_keywords: LLMs reasoning Chain-of-Thought OpenAI vision-language tasks multi-modal tasks long-chain reasoning data training pipeline multi-agent system reasoning agent summary agent DPO algorithm LLaVA-NeXT