RTS introduces reward-guided noise optimization and sparse PCA-based scaling to enhance test-time performance of diffusion models, claiming 15.6% GenEval and 60.4% ImageReward gains over baselines.
A primer on zeroth-order optimization in signal processing and machine learning: Principals, recent advances, and applications.IEEE Signal Processing Magazine, 37(5):43–54,
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Guided Trajectory Optimization with Sparse Scaling for Test-Time Diffusion
RTS introduces reward-guided noise optimization and sparse PCA-based scaling to enhance test-time performance of diffusion models, claiming 15.6% GenEval and 60.4% ImageReward gains over baselines.