To Deceive is to Teach? Forging Perceptual Robustness via Adversarial Reinforcement Learning
#Multimodal Large Language Models #Adversarial Training #Perceptual Robustness #AOT-SFT #Machine Learning Research #AI Safety #Model Reliability
📌 Key Takeaways
- Researchers developed AOT-SFT, a large-scale adversarial dataset to improve MLLM robustness
- AOT framework uses a self-play approach with Attacker and Defender models in co-evolution
- The method generates diverse image manipulations to force continuous improvement in the Defender
- Experiments show enhanced perceptual robustness and reduced hallucinations in MLLMs
📖 Full Retelling
Researchers Yicheng Bao, Xuhong Wang, and Xin Tan introduced a novel approach to improve Multimodal Large Language Models (MLLMs) by addressing their perceptual weaknesses in complex visual scenes through adversarial training methods in a paper submitted to arXiv on January 24, 2026. The research reveals that despite their impressive capabilities, MLLMs exhibit perceptual fragility when confronted with visually complex scenes, a weakness stemming from their reliance on finite training datasets that are prohibitively expensive to scale and impose a ceiling on model robustness. To address this limitation, the researchers developed AOT-SFT, a large-scale adversarial dataset specifically designed for bootstrapping MLLM robustness, which serves as the foundation for their more significant innovation. Building on this dataset, the team proposed AOT (Adversarial Opponent Training), a self-play framework that forges MLLM robustness by creating its own training data through a co-evolution between an image-editing Attacker and a Defender MLLM. In this system, the Attacker generates a diverse and dynamic curriculum of image manipulations, forcing the Defender to continuously adapt and improve its perceptual capabilities. Extensive experiments conducted by the researchers demonstrate that the AOT method significantly enhances the Defender's perceptual robustness while reducing hallucinations, establishing a scalable paradigm for training more reliable MLLMs that can better handle complex visual inputs in real-world applications.
🏷️ Themes
Machine Learning, Artificial Intelligence, Computer Vision, Model Robustness
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Original Source
--> Computer Science > Machine Learning arXiv:2602.22227 [Submitted on 24 Jan 2026] Title: To Deceive is to Teach? Forging Perceptual Robustness via Adversarial Reinforcement Learning Authors: Yicheng Bao , Xuhong Wang , Xin Tan View a PDF of the paper titled To Deceive is to Teach? Forging Perceptual Robustness via Adversarial Reinforcement Learning, by Yicheng Bao and 2 other authors View PDF HTML Abstract: Despite their impressive capabilities, Multimodal Large Language Models 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 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. Subjects: Machine Learning (cs.LG) ; Artificial Intelligence (cs.AI) Cite as: arXiv:2602.22227 [cs.LG] (or arXiv:2602.22227v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2602.22227 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Yicheng Bao [ view email ] [v1] Sat, 24 Jan 2026 03:47:29 UTC (14,820 KB) Full-text links: Access Paper: View a PDF of the paper titled To Deceive is to Teach? Forging Perceptual Robustness via Adversarial Reinforcement Learning, by Yicheng Bao and 2 other authors View PDF HTML TeX Source view license Current browse context: cs.LG < prev | next > new | rece...
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