How can the learning synergy between multimodal content understanding and creation emerge? In this paper, we present DREAMLLM, a comprehensive framework for developing MLLMs that not only understands but also creates multimodal content via diffusion models. Through score distillation of conditional-image synthesis distributions, we avoid the need for intermediate representation targets. The employment of interleaved documents further enriches the multimodal distributions, fostering the learning of multimodal encoding and decoding. Our extensive empirical evaluations across diverse VL benchmarks demonstrate the effectiveness of DREAMLLM and the emerging learning synergy between multimodal content understanding and creation. Besides, this work initiates the first step towards interleaved content creation. As a general learning framework, we hope it will spur further research in the multimodal machine learning field.

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