Understanding vs. Generation: Navigating Optimization Dilemma in Multimodal Models
2026-02-17 • Computer Vision and Pattern Recognition
Computer Vision and Pattern RecognitionArtificial Intelligence
AI summaryⓘ
The authors found that improving a multimodal model's ability to generate content can make it worse at understanding, and vice versa, because these two goals compete inside the model. To fix this, they created a new approach called Reason-Reflect-Refine (R3), which breaks down the task into steps: first generate, then understand, and finally regenerate content. This step-by-step method helps the model use its understanding to improve generation, leading to better results in both areas. Their work provides useful ideas for building smarter all-in-one models.
multimodal modelsgenerative capabilitiesunderstandingoptimization dilemmaReason-Reflect-Refinegeneration processmodel trainingunified models
Authors
Sen Ye, Mengde Xu, Shuyang Gu, Di He, Liwei Wang, Han Hu
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 https://github.com/sen-ye/R3.