Guided Trajectory Optimization with Sparse Scaling for Test-Time Diffusion

• URL: http://arxiv.org/abs/2605.21907v1
• Date: Thu, 21 May 2026 02:30:01 GMT
• Title: Guided Trajectory Optimization with Sparse Scaling for Test-Time Diffusion
• Abstract summary: We propose a Reward-guided Trajectory Scaling method to unlock the generative potential of diffusion models.<n>Unlike existing methods, RTS facilitates the synthesis of refined, high-fidelity images via two core innovations.<n> Experiments show our approach outperforms baselines by 15.6% across GenEval Score, and a 60.4% enhancement in ImageReward score, setting a new SOTA.
• Score: 12.576259610691851
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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. Unlike existing methods, RTS facilitates the synthesis of refined, high-fidelity images via two core innovations: 1) a reward-guided noise optimization strategy to actively direct the search towards promising regions; and 2) a sparse test-time scaling framework together with a PCA-driven curvature analysis scheme to prioritize key intermediate steps in the entire denoising space, effectively compressing the search space. Experiments show our approach outperforms baselines by 15.6% across GenEval Score, and a 60.4% enhancement in ImageReward score, setting a new SOTA while providing a practical guideline for more effective test-time scaling across diffusion-specific architectures.

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