Insight-V++: Towards Advanced Long-Chain Visual Reasoning with Multimodal Large Language Models

Abstract

Large Language Models (LLMs) have achieved remarkable reliability and advanced capabilities through extended test-time reasoning. However, extending these capabilities to Multi-modal Large Language Models (MLLMs) remains a significant challenge due to a critical scarcity of high-quality, long-chain reasoning data and optimized training pipelines. To bridge this gap, we present a unified multi-agent visual reasoning framework that systematically evolves from our foundational image-centric model, Insight-V, into a generalized spatial-temporal architecture, Insight-V++. We first propose a scalable data generation pipeline equipped with multi-granularity assessment that autonomously synthesizes structured, complex reasoning trajectories across image and video domains without human intervention. Recognizing that directly supervising MLLMs with such intricate data yields sub-optimal results, we design a dual-agent architecture comprising a reasoning agent to execute extensive analytical chains, and a summary agent to critically evaluate and distill final outcomes. While our initial framework utilized Direct Preference Optimization (DPO), its off-policy nature fundamentally constrained reinforcement learning potential. To overcome these limitations, particularly for long-horizon video understanding, Insight-V++ introduces two novel algorithms, ST-GRPO and J-GRPO, which enhance spatial-temporal reasoning and improve evaluative robustness. Crucially, by leveraging reliable feedback from the summary agent, we guide an iterative reasoning path generation process, retraining the entire multi-agent system in a continuous, self-improving loop. Extensive experiments on base models like LLaVA-NeXT and Qwen2.5-VL demonstrate significant performance gains across challenging image and video reasoning benchmarks while preserving strong capabilities on traditional perception-focused tasks.

Figures (5)

• Figure 1: Illustration and Performance of Insight-V and Insight-V++. Insight-V employs a dual-agent architecture, comprising dedicated reasoning and summarization modules to achieve significant performance gains across various image reasoning benchmarks. Building upon this foundation, Insight-V++ introduces a novel self-evolving training paradigm. By utilizing an iterative loop of Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL) to continuously refine its visual reasoning capabilities, Insight-V++ yields superior results on both image and video benchmarks.
• Figure 2: Data Generation Pipeline of Insight-V Series. The reasoning processes are generated progressively through a reasoning generator, and then fed into a multi-granularity assessment system to ensure high-quality reasoning.
• Figure 3: Overview of Insight-V and Insight-V++ Model Design. In Insight-V, we derive a multi-agent system from a single base model. By decomposing the task into reasoning and summarization, these two agents collaborate to enhance overall performance. For Insight-V++, we further introduce ST-GRPO and J-GRPO, which are tailored specifically to each agent. Coupled with a self-evolving strategy, this training loop consisting of SFT and customized RL drives the multi-agent system to excel at visual reasoning.
• Figure 4: Ablations on the amount of training data. The reasoning agent benefits from data scaling, providing more valuable insights for the summary agent.
• Figure 5: Qualitative Results of Insight-V Series. We present qualitative comparisons of Insight-V with Chain-of-Thought and learning Insight-V with direct SFT (Vanilla). For the Insight-V system, the reasoning agent delivers a more coherent and structured reasoning process, guiding the summary agent toward the correct answer, whereas other methods struggle with complex reasoning tasks and fail to solve such challenging problems.