Gradient-Free Noise Optimization for Reward Alignment in Generative Models

• URL: http://arxiv.org/abs/2605.11347v2
• Date: Wed, 13 May 2026 05:43:07 GMT
• Title: Gradient-Free Noise Optimization for Reward Alignment in Generative Models
• Abstract summary: ZeNO is a gradient-free framework that formulates noise optimization as a path-integral control problem.<n>It enables effective inference-time scaling and demonstrates strong performance across diverse generators and reward functions.
• Score: 52.42087778653147
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Existing reward alignment methods for diffusion and flow models rely on multi-step stochastic trajectories, making them difficult to extend to deterministic generators. A natural alternative is noise-space optimization, but existing approaches require backpropagation through the generator and reward pipeline, limiting applicability to differentiable settings. To address this, here we present ZeNO (Zeroth-order Noise Optimization), a gradient-free framework that formulates noise optimization as a path-integral control problem, estimable from zeroth-order reward evaluations alone. When instantiated with an Ornstein--Uhlenbeck reference process, the update connects to Langevin dynamics implicitly targeting a reward-tilted distribution. ZeNO enables effective inference-time scaling and demonstrates strong performance across diverse generators and reward functions, including a protein structure generation task where backpropagation is infeasible.

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