Understanding vs. Generation: Navigating Optimization Dilemma in Multimodal Models
Sen Ye, Mengde Xu, Di He, Winston Hu, Liwei Wang, Shuyang Gu
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.
BibTeX
@inproceedings{
ye2026understanding,
title={Understanding vs. Generation: Navigating Optimization Dilemma in Multimodal Models},
author={Sen Ye and Mengde Xu and Di He and Winston Hu and Liwei Wang and Shuyang Gu},
booktitle={The Fourteenth International Conference on Learning Representations},
year={2026},
url={https://openreview.net/forum?id=1smez00sCm}
}