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A culturally situated reward model that assigns rewards according to cultural appropriateness in open-ended social scenarios, and a collaborative multi-agent framework that instantiates implicit cultural norms into diverse social scenarios to construct culturally situated data are introduced.
We introduce LLaDA-Image, a unified framework that pairs a 6B Diffusion Transformer (DiT) trained from scratch with a frozen vision-language understanding module built on the LLaDA2.0-Mini diffusion language model backbone. Instead of relying heavily on paired image-text data from the beginning, we first build a strong...
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