ART-RL learns adaptive diffusion sampling timesteps via continuous-time control and Gaussian actor–critic RL, improving and transferring over hand-designed grids at matched budgets.
Haoran Wang, Thaleia Zariphopoulou, and Xun Yu Zhou
3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 3representative citing papers
AID amortizes guidance for diffusion inpainting by training a reusable module via an auxiliary Gaussian formulation and continuous-time actor-critic algorithm, improving quality-speed trade-off with under 1% overhead.
ART reparameterizes diffusion sampling time and uses RL to learn optimal timestep schedules that reduce discretization error and improve generation quality across budgets and datasets.
citing papers explorer
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ART for Diffusion Sampling: Continuous-Time Control and Actor-Critic Learning
ART-RL learns adaptive diffusion sampling timesteps via continuous-time control and Gaussian actor–critic RL, improving and transferring over hand-designed grids at matched budgets.
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Amortized Guidance for Image Inpainting with Pretrained Diffusion Models
AID amortizes guidance for diffusion inpainting by training a reusable module via an auxiliary Gaussian formulation and continuous-time actor-critic algorithm, improving quality-speed trade-off with under 1% overhead.
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ART for Diffusion Sampling: A Reinforcement Learning Approach to Timestep Schedule
ART reparameterizes diffusion sampling time and uses RL to learn optimal timestep schedules that reduce discretization error and improve generation quality across budgets and datasets.