AI RESEARCH
FuzzingRL: Reinforcement Fuzz-Testing for Revealing VLM Failures
arXiv CS.LG
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ArXi:2603.06600v1 Announce Type: new Vision Language Models (VLMs) are prone to errors, and identifying where these errors occur is critical for ensuring the reliability and safety of AI systems. In this paper, we propose an approach that automatically generates questions designed to deliberately induce incorrect responses from VLMs, thereby revealing their vulnerabilities. The core of this approach lies in fuzz testing and reinforcement finetuning: we transform a single input query into a large set of diverse variants through vision and language fuzzing.