Understanding the Role of Hallucination in Reinforcement Post-Training of Multimodal Reasoning Models

Gengwei Zhang, Jie Peng, Zhen Tan, Mufan Qiu, Hossein Nourkhiz Mahjoub et al.|April 3, 2026arXiv

Key Takeaway

RL post-training of multimodal models may improve performance through learned hallucination patterns rather than genuine visual reasoning, challenging assumptions about how these models actually learn from images.

Summary

This paper investigates how reinforcement learning improves multimodal AI models' visual reasoning by studying the role of hallucination—when models generate plausible-sounding but incorrect information.

training multimodal reasoning

Key Terms

hallucination reinforcement-learning multimodal-learning post-training visual-reasoning