In this work, we aim to develop effective data synthesis techniques that autonomously synthesize multimodal training data for enhancing MLLMs in solving complex real-world tasks. To this end, we propose Collective Adversarial Data Synthesis (CADS), a novel and general approach to synthesize high-quality, diverse and challenging multimodal data for MLLMs. The core idea of CADS is to leverage collective intelligence to ensure high-quality and diverse generation, while exploring adversarial learning to synthesize challenging samples for effectively driving model improvement. Specifically, CADS operates with two cyclic phases, i.e., Collective Adversarial Data Generation (CAD-Generate) and Collective Adversarial Data Judgment (CAD-Judge). CAD-Generate leverages collective knowledge to jointly generate new and diverse multimodal data, while CAD-Judge collaboratively assesses the quality of synthesized data. In addition, CADS introduces an Adversarial Context Optimization mechanism to optimize the generation context to encourage challenging and high-value data generation. With CADS, we construct MMSynthetic-20K and train our model R1-SyntheticVL, which demonstrates superior performance on various benchmarks.

翻译：在本工作中，我们旨在开发有效的数据合成技术，自主合成多模态训练数据，以增强多模态大语言模型解决复杂现实任务的能力。为此，我们提出了集体对抗数据合成方法——一种新颖且通用的方法，用于为多模态大语言模型合成高质量、多样且富有挑战性的多模态数据。其核心思想是利用集体智能确保生成数据的高质量与多样性，同时探索对抗学习以合成具有挑战性的样本，从而有效驱动模型改进。具体而言，CADS包含两个循环阶段：集体对抗数据生成与集体对抗数据评判。CAD-Generate利用集体知识联合生成新颖多样的多模态数据，而CAD-Judge则协同评估合成数据的质量。此外，CADS引入了对抗上下文优化机制，通过优化生成上下文以促进高价值、高难度数据的生成。基于CADS，我们构建了MMSynthetic-20K数据集并训练了R1-SyntheticVL模型，该模型在多个基准测试中展现出卓越性能。