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Image models do not need noisy vision-language reward models to master compositional reasoning: training on programmatically verified synthetic scenes yields a 10x boost in spatial accuracy that transfers directly to natural prompts.
Discriminator-Guided RL achieves a remarkable reduction in FID from 9.38 to 2.62, showcasing a new way to align model outputs with real data without human preferences.
