MDM-VGB augments masked diffusion with backtracking-style reward-guided remasking to achieve quadratic-complexity high-reward generation and sample editing, with proofs of noise robustness.
arXiv preprint arXiv:2510.03149 , year=
3 Pith papers cite this work. Polarity classification is still indexing.
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Establishes a quadratic lower bound on query complexity for sampling from large classes of distributions given approximate density oracles, answers an open question on optimality of random walks, and shows circumvention for bounded classes as an abstraction of TTT.
The choice of closeness measure in diffusion reward alignment determines the computational primitives and tractable reward classes, with linear exponential tilts sufficing for KL with convex rewards and proximal oracles for Wasserstein with concave or low-dimensional Lipschitz rewards.
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The Power of Test-Time Training for Approximate Sampling
Establishes a quadratic lower bound on query complexity for sampling from large classes of distributions given approximate density oracles, answers an open question on optimality of random walks, and shows circumvention for bounded classes as an abstraction of TTT.