Conditional Total Correlation and the Serial Depth of Adaptive Parallel Sampling
Motivated by parallel decoding in masked diffusion models, we study adaptive parallel sampling of discrete vectors: in each round, a deterministic policy selects unrevealed coordinates on the basis of the values observed so far, and the selected coordinates are sampled independently from their exact conditional marginals. Approximation error is measured by forward Kullback-Leibler divergence, and serial depth is the minimum target-averaged number of rounds meeting a prescribed error budget. Our central result is an exact identity: the divergence of every policy equals the expected conditional total correlation accumulated over its reveal rounds, so conditional total correlation is the exa...
| Authors | Chuling Wen, Weijie Liang, Jian Lu |
|---|---|
| Primary category | cs.IT |
| Published | 26 August 2026 |
| Last updated at source | 26 August 2026 |
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