Simplex Relaxation for Discrete Diffusion
Simplax adds a simplex-valued auxiliary variable to uniform discrete diffusion while keeping the original categorical corruption process intact.
The paper frames this as an exact Dirichlet-categorical augmentation, not a replacement for the diffusion kernel. It gives a tractable Rao-Blackwellized reverse-bridge objective and a stochastic reverse sampler, with the denoiser still seeing the corrupted categorical state. In reported tests, Simplax improves the perplexity-entropy tradeoff on unconditional OpenWebText generation. On Sudoku, training only on 30-clue puzzles produced the top accuracy among compared methods across evaluated clue densities, including 17-clue puzzles, and the highest unconditional validity. HF Daily Papers' note
The paper frames this as an exact Dirichlet-categorical augmentation, not a replacement for the diffusion kernel. It gives a tractable Rao-Blackwellized reverse-bridge objective and a stochastic reverse sampler, with the denoiser still seeing the corrupted categorical state. In reported tests, Simplax improves the perplexity-entropy tradeoff on unconditional OpenWebText generation. On Sudoku, training only on 30-clue puzzles produced the top accuracy among compared methods across evaluated clue densities, including 17-clue puzzles, and the highest unconditional validity. HF Daily Papers' note
score 4