Reward Tilting via Semantic Noising

Steering a generative model toward a desired property typically requires separate fine-tuning for each reward or additional computation during sampling. We propose *semantic noising* which conditions generation on a noisy semantic representation of the data. The resulting posteriors correspond to exponential tilts by linear-quadratic rewards in semantic space, allowing a single model to support objectives and strengths chosen after training. A conditional generator can be trained to approximate these posteriors directly, while models that generate semantic representations by diffusion provide analytic tilted denoisers for the same family of rewards. We illustrate semantic noising in three settings. First, we train MolGPT, an autoregressive molecular generator, to condition on noisy semantic representations without using molecular property values, and use independently fitted linear predictors to control properties. Our model achieves a better reward–KL trade-off than separately trained property-conditioned models. Second, our joint diffusion model of images and their CLIP embeddings achieves FID comparable to REG on ImageNet while enabling prompt-based editing and adjustable aesthetic-score tilting. Finally, we show that pretrained factorized models such as Kandinsky support analytic tilting without retraining or additional denoiser evaluations.