AI RESEARCH
Guided Trajectory Optimization with Sparse Scaling for Test-Time Diffusion
arXiv CS.CV
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ArXi:2605.21907v1 Announce Type: new The efficient Test-Time Scaling (TTS) paradigm offers a promising perspective for enhancing the generation performance of diffusion models. However, current solutions are limited to a static, pre-defined noise pool and suffer from inflexible noise exploration across the denoising trajectory. To bridge this gap, we propose RTS, a novel Reward-guided Trajectory Scaling method to fully unlock the generative potential of diffusion models.