arXiv:2602.12506v3 Announce Type: replace Abstract: Reinforcement learning (RL) finetuning has become a key technique for enhancing large language models (LLMs) on reasoning-intensive tasks, motivating its extension to vision-language models (VLMs). While RL-tuned VLMs improve on visual reasoning benchmarks, they remain vulnerable to weak visual grounding, hallucinations, and over-reliance on textual cues. We show that simple, controlled textual perturbations, including misleading captions or incorrect chain-of-thought (CoT) traces, cause substantial drops in robustness and confidence, and tha
The increasing adoption of RL-finetuning for large models necessitates a deeper understanding of their robustness to adversarial data, as models move from research to deployment.
This research highlights critical vulnerabilities in advanced AI models, particularly Vision-Language Models (VLMs), impacting their reliability and trustworthiness in real-world applications.
Our understanding of 'robustness' for RL-finetuned VLMs is updated, revealing that current methods for improving performance may inadvertently introduce new failure modes related to visual grounding and textual over-reliance.
- · AI researchers focusing on model interpretability and adversarial robustness
- · Developers of robust VLM architectures
- · AI developers relying solely on benchmark improvements for VLM deployment
- · Applications requiring high-stakes visual reasoning without robust validation
RL-finetuned VLMs are shown to be vulnerable to simple textual perturbations, undermining their perceived advances in visual reasoning.
This vulnerability could slow the deployment of VLMs in critical applications until more robust finetuning methods are developed.
Increased focus on multimodal adversarial defenses might lead to new paradigms for AI safety and trustworthiness beyond current benchmarks.
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