Diffusion models exhibit a structural limitation when generating samples on low-dimensional feasible regions for constrained tasks, and sequential autoregressive generation using RL and MCTS improves constraint satisfaction.
Constrained synthesis with projected diffusion models.Ad- vances in Neural Information Processing Systems, 37: 89307–89333
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When Diffusion Breaks Constraints: Sequential Autoregressive Generation with RL and MCTS
Diffusion models exhibit a structural limitation when generating samples on low-dimensional feasible regions for constrained tasks, and sequential autoregressive generation using RL and MCTS improves constraint satisfaction.