Insight-V: Exploring Long-Chain Visual Reasoning with Multimodal Large Language Models

Abstract: 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 LLMs (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.

• The paper introduces a scalable dual-agent framework combining a reasoning agent and a summary agent to enhance long-chain visual reasoning in MLLMs.
• The methodology employs a two-step pipeline with a progressive strategy and multi-granularity quality assessment, achieving a 7.0% average improvement on seven benchmarks.
• Implications include enhanced multi-modal reasoning for applications like autonomous driving and robotics, offering a blueprint for future AI research.

Analysis of Insight-V: Advancing Long-Chain Visual Reasoning in Multi-modal LLMs

"Insight-V: Exploring Long-Chain Visual Reasoning with Multimodal LLMs" investigates the enhancement of reasoning capabilities within Multi-modal LLMs (MLLMs). While there have been substantial advancements in LLMs, particularly with improvements in reasoning through Chain-of-Thought prompting, translating these improvements to vision-language tasks remains an underexplored challenge. This paper introduces Insight-V, an innovative approach to address the complexities inherent in multi-modal reasoning tasks.

Overview of Insight-V Approach

Central to Insight-V is the dual objective of creating scalable, high-quality reasoning datasets and developing an effective training pipeline to bolster MLLMs' reasoning capabilities. The authors target the gap in visual reasoning data by introducing a two-step pipeline that utilizes a progressive strategy and a multi-granularity assessment method for quality assurance. Insight-V advocates for a multi-agent system composed of a reasoning agent and a summary agent. The former is dedicated to long-chain reasoning, while the latter focuses on distilling and summarizing these reasoning paths. This system is theoretically augmented with an iterative Direct Preference Optimization (DPO) algorithm, showcasing improvements in both generation stability and reasoning quality.

Technical Execution and Results

The experimentation employs the LLaVA-NeXT model as a base to demonstrate substantial performance gains across challenging multi-modal benchmarks. Results show an average performance improvement of 7.0% across seven visual reasoning benchmarks, indicating the high potential of Insight-V to advance state-of-the-art models. This is notably achieved without compromising performance on perception-focused multi-modal tasks, a key consideration for preserving MLLMs' general utility.

Implications and Future Directions

The key implication of Insight-V lies in its potential to create more reliable MLLMs that are capable of complex visual reasoning. This advancement offers practical benefits across domains requiring high-level multi-modal reasoning, such as autonomous driving and sophisticated robotics. Theoretical contributions include providing a scalable framework for reasoning data generation and illustrating the synergetic potential of reasoning and summarization agents in a multi-agent system.

Future developments might focus on refining the efficiency of data sampling and exploring methods to distribute reasoning and summarization tasks more equitably across the agents, potentially reducing computational overhead while maintaining or enhancing reasoning effectiveness. Experimenting with smaller model architectures for summarization tasks or applying more refined inference scaling techniques could also enhance scalability and accuracy. Investigating feedback mechanisms during reasoning steps might provide further improvements in reasoning precision, thus facilitating a more nuanced, reflective reasoning process analogous to human cognition.

Insight-V significantly contributes to the field of AI by expanding the capabilities of MLLMs in handling complex reasoning tasks, offering an architecture that could serve as a blueprint for further research and development in the quest for generalist AI systems capable of nuanced understanding and decision-making.