NoisEasier: Test-Time Noise Optimization for Text-to-Video Generation

Diffusion models have recently advanced text-to-video (T2V)generation, yet they still struggle with fine-grained compositional align-ment, such as attribute binding, spatial relations, and object interactions.While reward-based fine-tuning improves alignment, it is susceptible toreward hacking and adapts poorly to new prompt distributions. In thiswork, we propose NoisEasier, a test-time scaling framework that im-proves T2V generation through differentiable reward-guided noise opti-mization without modifying the underlying model. By combining effi-cient short-step generators with a multi-objective reward formulation,NoisEasier enables stable and practical test-time optimization under re-alistic inference budgets. Our key insight is that jointly optimizing theentire stochastic trajectory accelerates reward convergence and improvescompositional alignment over optimizing only the initial latent, with neg-ligible additional computational and time cost. Experiments on VBenchand T2V-CompBench demonstrate consistent improvements across mul-tiple backbones, achieving over 10% average gains on challenging dimen-sions such as attribute binding, object interaction, and numeracy. Over-all, NoisEasier serves as both a flexible alternative and a complementaryenhancement to reward-based fine-tuning, establishing test-time scalingas an effective paradigm for controllable text-to-video generation.