Test-Time Scaling (TTS) paradigm offers a promising perspective for enhancing the generation performance of diffusion models. However, current solutions largely restrict their search to predefined noise candidates or suffer from inflexible 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. Unlike existing methods, RTS facilitates the synthesis of refined, high-fidelity images via two core innovations: 1) a coarse-to-fine noise optimization mechanism that exploits historical search experience to actively steer the exploration toward high-reward regions and 2) a unified sparse test-time scaling framework featuring PCA-driven curvature analysis, which eliminates temporal redundancy by flexiblely allocating compute to a sparse set of key timesteps that represent critical shifts in the denoising direction. Extensive experiments across SD v3, FLUX, and Qwen-Image architectures demonstrate that RTS outperforms baselines, improving the GenEval score by 20.7%, 15.6%, and 12.2%, respectively. Notably, empirical findings indicate that these key points primarily cluster in the mid-stage of the trajectory, distinct from the structure-sensitive early phases and the late attribute refinement phases.

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