Unverified50% confidenceFactExact time
MintFlow is a training-free constrained sampling framework that reformulates the constraint imposition process as minimal intervention on pre-trained flow trajectories.
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10/5/2026
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UnverifiedThe paper evaluates MintFlow on tasks including generative vision and physical system modeling, and the results show that it achieves competitive constraint satisfaction while significantly better preserving the pretrained generative distribution compared to current state-of-the-art constraint methods.72% similarUnverifiedDistilBERT incorporates distillation during the pre-training phase, demonstrating that distillation can be effective in unsupervised pre-training rather than being limited to supervised fine-tuning.70% similarUnverifiedTraining from scratch means not using any pretrained weights, but instead completing the full training process on a new dataset with randomly initialized parameters.67% similarUnverifiedscikit-learn's Pipeline mechanism encapsulates preprocessing steps so that fit is executed only on the training set, thereby preventing information from the test set from leaking into the preprocessing steps.63% similarUnverifiedOn infilling tasks, Diffusion-LM outperforms continuous-relaxation-based methods such as COLD and DELOREAN on automatic evaluation metrics, and reaches the level of autoregressive models trained from scratch specifically for this purpose without requiring targeted training.62% similar
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