Research Paper#Diffusion Models, Reinforcement Learning, Image Generation🔬 ResearchAnalyzed: Jan 3, 2026 16:48
GARDO: Preventing Reward Hacking in Diffusion Models
Published:Dec 30, 2025 10:55
•1 min read
•ArXiv
Analysis
This paper addresses a critical problem in reinforcement learning for diffusion models: reward hacking. It proposes a novel framework, GARDO, that tackles the issue by selectively regularizing uncertain samples, adaptively updating the reference model, and promoting diversity. The paper's significance lies in its potential to improve the quality and diversity of generated images in text-to-image models, which is a key area of AI development. The proposed solution offers a more efficient and effective approach compared to existing methods.
Key Takeaways
- •GARDO is a framework designed to mitigate reward hacking in diffusion models trained with reinforcement learning.
- •It uses selective regularization, adaptive reference model updates, and diversity-aware optimization.
- •The approach aims to improve image quality, generation diversity, and sample efficiency.
- •Experiments show GARDO's effectiveness across various proxy rewards and evaluation metrics.
Reference
“GARDO's key insight is that regularization need not be applied universally; instead, it is highly effective to selectively penalize a subset of samples that exhibit high uncertainty.”