用自演化对抗训练提升多模态大模型的视觉鲁棒性
Dynamic Adversarial Reinforcement Learning for Robust Multimodal Large Language Models
- 构建攻击者-防御者自对弈框架，动态生成对抗样本
- 在多个视觉任务上显著降低幻觉率，提升模型稳健性
- 适合关注多模态模型可靠性与对抗训练的研究者
尽管多模态大语言模型（MLLMs）能力出众，但在面对复杂视觉场景时仍存在感知脆弱性。这一缺陷源于对有限训练数据集的依赖，而这些数据集因成本高昂难以扩展，限制了模型鲁棒性的提升。我们提出 extbf{AOT-SFT}，一个大规模对抗数据集，用于启动 MLLM 的鲁棒性训练。基于此，我们进一步设计 extbf{AOT（Adversarial Opponent Training）}，一种自对弈框架，通过让图像编辑攻击者与防御型 MLLM 协同进化，自动生成多样化且动态的图像扰动作为训练课程。该机制迫使防御模型持续适应新挑战，实现性能迭代。大量实验表明，AOT 显著提升了防御模型的感知鲁棒性并减少了幻觉现象，为训练更可靠的 MLLMs 提供了一种可扩展的新范式。
原文摘要 · Abstract (English)
Despite their impressive capabilities, Multimodal Large Language Models (MLLMs) exhibit perceptual fragility when confronted with visually complex scenes. This weakness stems from a reliance on finite training datasets, which are prohibitively expensive to scale and impose a ceiling on model robustness. We introduce \textbf{AOT-SFT}, a large-scale adversarial dataset for bootstrapping MLLM robustness. Building on this, we propose \textbf{AOT (Adversarial Opponent Training)}, a self-play framework that forges MLLM robustness by creating its own training data. Our method orchestrates a co-evolution between an image-editing Attacker and a Defender MLLM, where the Attacker generates a diverse and dynamic curriculum of image manipulations, forcing the Defender to adapt and improve. Extensive experiments demonstrate that AOT enhances the Defender's perceptual robustness and reduces hallucinations, establishing a scalable paradigm for training more reliable MLLMs.
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