The pursuit of unified multimodal models (UMMs) has long been hindered by a fundamental schism between multimodal understanding and generation. Current approaches typically disentangle the two and treat them as separate endeavors with disjoint objectives, missing the mutual benefits. We argue that true unification requires more than just merging two tasks. It requires a unified, foundational objective that intrinsically links them. In this paper, we introduce an insightful paradigm through the Auto-Encoder lens, i.e., regarding understanding as the encoder (I2T) that compresses images into text, and generation as the decoder (T2I) that reconstructs images from that text. To implement this, we propose UAE, where we begin by pre-training the decoder with the proposed 700k long-context image-caption pairs to direct it to "understand" the fine-grained and complex semantics from the text. We then propose Unified-GRPO via reinforcement learning (RL) to unify the two, which covers two complementary stages: (1) Generation for Understanding, where the encoder is trained to generate informative captions that maximize the decoder's reconstruction quality, enhancing its visual perception; (2) Understanding for Generation, where the decoder is refined to reconstruct from these captions, forcing it to leverage every detail and improving its long-context instruction following and generation fidelity. Our empirical results suggest that understanding can largely enhance generation (verified on GenEval), while generation, in turn, notably strengthens fine-grained visual perception like small object and color recognition (verified on MMT-Bench). This bidirectional improvement reveals a deep synergy: under the unified reconstruction objective, generation and understanding can mutually benefit each other, moving closer to truly unified multimodal intelligence.

翻译：长期以来，统一多模态模型（UMMs）的追求一直受到多模态理解与生成之间根本性割裂的阻碍。当前方法通常将两者解耦，并将其视为具有不同目标的独立任务，错失了相互增益的机会。我们认为，真正的统一需要的不仅仅是合并两个任务，它需要一个统一的、基础性的目标，从本质上将两者联系起来。在本文中，我们通过自编码器的视角引入一个富有洞察力的范式，即将理解视为将图像压缩为文本的编码器（I2T），而将生成视为从该文本重建图像的解码器（T2I）。为实现此目标，我们提出了UAE。我们首先使用提出的70万条长上下文图像-描述对预训练解码器，引导其从文本中"理解"细粒度和复杂的语义。随后，我们通过强化学习（RL）提出Unified-GRPO来统一两者，该过程涵盖两个互补的阶段：（1）为理解而生成：训练编码器生成信息丰富的描述，以最大化解码器的重建质量，从而增强其视觉感知能力；（2）为生成而理解：精炼解码器使其能从这些描述中重建图像，迫使其利用每一个细节，从而提升其长上下文指令遵循和生成保真度。我们的实证结果表明，理解可以极大地增强生成能力（在GenEval上验证），而生成反过来又显著加强了细粒度视觉感知能力，如小物体和颜色识别（在MMT-Bench上验证）。这种双向改进揭示了一种深层次的协同作用：在统一的重建目标下，生成和理解可以相互促进，从而更接近真正统一的多模态智能。