Unverified50% confidenceFactExact time
A single-step generation in discrete diffusion can truly match the training data distribution only when the positions it writes to are mutually independent given the already-fixed tokens.
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10/2/2026
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UnverifiedWhen faced with questions outside the training distribution (out-of-distribution queries), models may degrade into guessing based on surface-level patterns.72% similarUnverifiedThe presenter arranged various generative algorithms along a spectrum by degree of endpoint dependence: at the far left are flow matching and stochastic interpolants with independent endpoints (simple to train but requiring multi-step integration at inference), and at the far right is full optimal transport (trivially straight paths but difficult to train).72% similarUnverifiedCompared to score-function-based diffusion models, the flow matching training objective is more concise, and the trajectory can be designed as a rectified flow, enabling high-quality generation in fewer inference steps.71% similarVerifiedIf the synthetic data generation process omits correlations between key variables, models trained on such data often perform poorly in real-world deployment environments.71% similar
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