Public articles linked to the same research event.
arXiv The authors propose iADD, a reinforcement-learning post-training method for discrete-time diffusion models that theoretically shows updating early denoising timesteps preserves diversity better than updating only late timesteps, and combines an incremental sparse-timestep curriculum with discrete-time Feynman-Kac path pruning and branching; across rare-prompt image generation, vanishing-point correction, and 3D indoor scene synthesis, iADD improves reward, Inception Score, rare-event success, and AUC over DDPO, B2-DiffuRL, and related baselines, and its components compose with GRPO-style optimization.
The authors propose iADD, a reinforcement-learning post-training method for discrete-time diffusion models that theoretically shows updating early denoising timesteps preserves diversity better than updating only late timesteps, and combines an incremental sparse-timestep curriculum with discrete-time Feynman-Kac path pruning and branching; across rare-prompt image generation, vanishing-point correction, and 3D indoor scene synthesis, iADD improves reward, Inception Score, rare-event success, and AUC over DDPO, B2-DiffuRL, and related baselines, and its components compose with GRPO-style optimization.
The authors propose iADD, a reinforcement-learning post-training method for discrete-time diffusion models that theoretically shows updating early denoising timesteps preserves diversity better than updating only late timesteps, and combines an incremental sparse-timestep curriculum with discrete-time Feynman-Kac path pruning and branching; across rare-prompt image generation, vanishing-point correction, and 3D indoor scene synthesis, iADD improves reward, Inception Score, rare-event success, and AUC over DDPO, B2-DiffuRL, and related baselines, and its components compose with GRPO-style optimization.
The authors propose iADD, a reinforcement-learning post-training method for discrete-time diffusion models that theoretically shows updating early denoising timesteps preserves diversity better than updating only late timesteps, and combines an incremental sparse-timestep curriculum with discrete-time Feynman-Kac path pruning and branching; across rare-prompt image generation, vanishing-point correction, and 3D indoor scene synthesis, iADD improves reward, Inception Score, rare-event success, and AUC over DDPO, B2-DiffuRL, and related baselines, and its components compose with GRPO-style optimization.