Understanding vs. Generation: Navigating Optimization Dilemma in Multimodal Models

ICLR 2026 Conference Submission25103 Authors

Keywords: Unified Multimodal Large Models, Text-to-image generation, Reasoning Models

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.

Primary Area: applications to computer vision, audio, language, and other modalities

Submission Number: 25103