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Vetoing unstable tokens can boost dMLLM reasoning accuracy by up to 9% without any extra training costs.
Diffusion models can reason better and faster: a simple penalty and guidance scheme closes the visual grounding gap with autoregressive models and boosts accuracy by 7.5%.
