[2604.04733] Discovering Failure Modes in Vision-Language Models using RL

Computer Science > Computer Vision and Pattern Recognition arXiv:2604.04733 (cs) [Submitted on 6 Apr 2026] Title:Discovering Failure Modes in Vision-Language Models using RL Authors:Kanishk Jain, Qian Yang, Shravan Nayak, Parisa Kordjamshidi, Nishanth Anand, Aishwarya Agrawal View a PDF of the paper titled Discovering Failure Modes in Vision-Language Models using RL, by Kanishk Jain and 5 other authors View PDF HTML (experimental) Abstract:Vision-language Models (VLMs), despite achieving strong performance on multimodal benchmarks, often misinterpret straightforward visual concepts that humans identify effortlessly, such as counting, spatial reasoning, and viewpoint understanding. Previous studies manually identified these weaknesses and found that they often stem from deficits in specific skills. However, such manual efforts are costly, unscalable, and subject to human bias, which often overlooks subtle details in favor of salient objects, resulting in an incomplete understanding of a model's vulnerabilities. To address these limitations, we propose a Reinforcement Learning (RL)-based framework to automatically discover the failure modes or blind spots of any candidate VLM on a given data distribution without human intervention. Our framework trains a questioner agent that adaptively generates queries based on the candidate VLM's responses to elicit incorrect answers. Our approach increases question complexity by focusing on fine-grained visual details and distinct skill ...