Understanding vs. Generation: Navigating Optimization Dilemma in Multimodal Models

TL;DR

This paper introduces the R3 framework to balance understanding and generation in multimodal models, improving both capabilities by reframing the task into a multi-step process.

Contribution

The paper proposes the Reason-Reflect-Refine (R3) algorithm, a novel multi-step approach that mitigates the trade-off between understanding and generation in multimodal models.

Findings

R3 enhances both generation quality and understanding ability.

The multi-step process outperforms single-step methods in experiments.

Code is available at https://github.com/sen-ye/R3.

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.