arXiv:2606.18066v1 Announce Type: new Abstract: We introduce the Noise-Tilted Reverse Kernel (NTRK), a reward-guided diffusion sampler that injects reward gradients through the noise term, leaving the pretrained reverse kernel unchanged and requiring only a single sample per step. Reward-guided sampling at inference time has greatly expanded the versatility of pretrained diffusion models. Yet existing methods face a trade-off. Gradient-based guidance shifts the reverse mean, steering generation but pushing intermediate states outside the region that the model was trained on and degrading quali
The paper introduces a novel method for reward-guided diffusion sampling, addressing a known trade-off in existing techniques that degrades quality by pushing intermediate states outside the model's training distribution.
This breakthrough improves the efficiency and quality of reward-guided diffusion models, enabling more precise control over generative AI outputs without retraining, thus accelerating development in areas like image generation, drug discovery, and robotics.
Diffusion models can now be guided by rewards more effectively and efficiently at inference time, avoiding quality degradation and potentially lowering computational demands for specific generation tasks.
- · AI researchers
- · Generative AI companies
- · Industries using diffusion models (e.g., design, biotech)
- · Companies reliant on less efficient, gradient-based guidance methods
Improved quality and control in image and content generation using diffusion models.
Faster development and deployment of customized generative AI applications across various industries.
Potentially democratizes access to sophisticated AI content creation by reducing computational and expertise barriers.
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Read at arXiv cs.LG