Improving Generalization Robustness of Multimodal RLVR
Reinforcement Learning with Verifiable Rewards (RLVR) makes Multimodal Large Language Models more accurate, but the gains are brittle: simply paraphrasing a question or changing the prompt template can degrade them, which challenges reliable deployment in high-stakes scenarios like medical VQA. We trace this to two issues of the standard RL objective. First, the binary verifier conflates format with content, so the reward signal cannot tell a wrong answer apart from a misformatted one. Second, the training distribution covers only a thin slice of the real-world prompts that the model might meet at deployment, so policies that perform well on the training distribution can behave differentl...
| Authors | Pengfei Zhou, Zhiwei Tang, Xiaopeng Peng et al. |
|---|---|
| Primary category | cs.AI |
| Published | 9 August 2026 |
| Last updated at source | 14 August 2026 |
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