解决多模态模型生成与理解的矛盾，通过三步循环提升两者能力。
Understanding vs. Generation: Navigating Optimization Dilemma in Multimodal Models
- 将生成任务拆解为‘生成-理解-重生成’三步流程，主动利用理解能力优化生成。
- 在多个基准上同时提升生成质量与理解能力，实现双赢效果。
- 适合关注多模态统一建模、模型优化机制的研究者参考。
当前多模态模型研究面临一个核心挑战：提升生成能力常以牺牲理解能力为代价，反之亦然。我们分析了这一权衡现象，发现其根源可能在于生成与理解之间的潜在冲突，导致模型内部产生竞争动态。为此，我们提出一种新的推理-反思-优化（Reason-Reflect-Refine, R3）框架。该算法将单步生成任务重构为多步过程：‘生成-理解-重生成’。通过在生成过程中显式利用模型的理解能力，有效缓解了优化困境，在保持或提升生成性能的同时，显著增强了理解能力。该方法为设计下一代统一的多模态模型提供了重要启示。代码已开源：https://github.com/sen-ye/R3。
原文摘要 · Abstract (English)
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.
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