To Deceive is to Teach? Forging Perceptual Robustness via Adversarial Reinforcement Learning

• URL: http://arxiv.org/abs/2602.22227v1
• Date: Sat, 24 Jan 2026 03:47:29 GMT
• Title: To Deceive is to Teach? Forging Perceptual Robustness via Adversarial Reinforcement Learning
• Authors: Yicheng Bao, Xuhong Wang, Xin Tan,
• Abstract summary: We introduce textbfAOT (Adversarial Opponent Training), a self-play framework that forges MLLM robustness by creating its own training data.<n>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.
• Score: 6.973386095391195
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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