Spectral Evolution Search: Efficient Inference-Time Scaling for Reward-Aligned Image Generation

• URL: http://arxiv.org/abs/2602.03208v1
• Date: Tue, 03 Feb 2026 07:19:39 GMT
• Title: Spectral Evolution Search: Efficient Inference-Time Scaling for Reward-Aligned Image Generation
• Authors: Jinyan Ye, Zhongjie Duan, Zhiwen Li, Cen Chen, Daoyuan Chen, Yaliang Li, Yingda Chen,
• Abstract summary: Inference-time scaling offers a versatile paradigm for aligning visual generative models with downstream objectives without parameter updates.<n>We show that existing approaches that optimize the high-dimensional initial noise suffer from severe inefficiency, as many search directions exert negligible influence on the final generation.<n>We propose Spectral Evolution Search (SES), a plug-and-play framework for initial noise optimization that executes gradient-free evolutionary search within a low-frequency subspace.
• Score: 45.717539734334906
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Inference-time scaling offers a versatile paradigm for aligning visual generative models with downstream objectives without parameter updates. However, existing approaches that optimize the high-dimensional initial noise suffer from severe inefficiency, as many search directions exert negligible influence on the final generation. We show that this inefficiency is closely related to a spectral bias in generative dynamics: model sensitivity to initial perturbations diminishes rapidly as frequency increases. Building on this insight, we propose Spectral Evolution Search (SES), a plug-and-play framework for initial noise optimization that executes gradient-free evolutionary search within a low-frequency subspace. Theoretically, we derive the Spectral Scaling Prediction from perturbation propagation dynamics, which explains the systematic differences in the impact of perturbations across frequencies. Extensive experiments demonstrate that SES significantly advances the Pareto frontier of generation quality versus computational cost, consistently outperforming strong baselines under equivalent budgets.

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