![[2602.15772] Understanding vs. Generation: Navigating Optimization Dilemma in Multimodal Models](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
[2602.15772] Understanding vs. Generation: Navigating Optimization Dilemma in Multimodal Models
Summary
This paper explores the optimization dilemma in multimodal models, where enhancing generative capabilities often compromises understanding. It introduces the Reason-Reflect-Refine (R3) framework to balance these aspects, leading to improved performance in both areas.
Why It Matters
As multimodal models become increasingly prevalent in AI applications, understanding the trade-offs between generation and comprehension is crucial. This research provides a framework that can enhance model design, potentially leading to more effective AI systems that leverage both capabilities.
Key Takeaways
- The optimization dilemma in multimodal models affects performance.
- The R3 framework proposes a multi-step process to enhance both generation and understanding.
- Improving understanding during generation can lead to better overall model performance.
- This research provides insights for future multimodal model development.
- Access to the code allows for practical application and further experimentation.
Computer Science > Computer Vision and Pattern Recognition arXiv:2602.15772 (cs) [Submitted on 17 Feb 2026] Title:Understanding vs. Generation: Navigating Optimization Dilemma in Multimodal Models Authors:Sen Ye, Mengde Xu, Shuyang Gu, Di He, Liwei Wang, Han Hu View a PDF of the paper titled Understanding vs. Generation: Navigating Optimization Dilemma in Multimodal Models, by Sen Ye and 5 other authors View PDF HTML (experimental) Abstract:Current research in multimodal models faces a key challenge where enhancing generative capabilities often comes at the expense of understanding, and vice versa. We analyzed this trade-off and identify the primary cause might be the potential conflict between generation and understanding, which creates a competitive dynamic within the model. To address this, we propose the Reason-Reflect-Refine (R3) framework. This innovative algorithm re-frames the single-step generation task into a multi-step process of "generate-understand-regenerate". By explicitly leveraging the model's understanding capability during generation, we successfully mitigate the optimization dilemma, achieved stronger generation results and improved understanding ability which are related to the generation process. This offers valuable insights for designing next-generation unified multimodal models. Code is available at this https URL. Comments: Subjects: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI) Cite as: arXiv:2602.15772 [cs.CV] ...
