Unverified50% confidenceFactExact time
ALD uses a marginal preferential decomposition, expressing stable noise in the form of a scalar Gaussian scale mixture, so that the training objective returns to the standard Gaussian noise prediction loss, requiring only one additional scalar scale input.
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10/15/2026
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UnverifiedIn ALD's annealing strategy, the schedule design of the hyperparameter α(t), the performance of different samplers on large-scale high-resolution tasks, and the impact of heavy-tailed noise on training stability remain questions requiring further verification.77% similarUnverifiedRegions with different target noise levels tend to reach their respective optimal prediction performance at different training epochs, making a single global early-stopping checkpoint a forced compromise among regions of varying uncertainty.74% similarUnverifiedALD-renoise adopts a strategy of estimating a clean sample and then re-injecting marginal noise, introducing a random reset between each step that may lose the dependency structure between adjacent states.72% similarUnverifiedUnder the SDE framework, the effect of batch size on gradient noise variance can be compensated by adjusting the balance between the drift term and the diffusion term, thereby achieving consistent generation quality across different batch sizes.67% similarUnverifiedThe training objective of Diffusion-LM includes three loss terms: the diffusion denoising loss, an embedding matching term, and a rounding loss.67% similar
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