3 accepted papers
Diffusion models have achieved outstanding success in image generation, yet their objectives are often limited to reconstruction, making it difficult to align with human preferences directly. Reinforcement learning (RL) offers a promising approach to address this by optimizing models using explicit
Achieving an optimal balance between exploration and exploitation remains a fundamental challenge in reinforcement learning. This work revisits the exploration-exploitation dilemma through the lens of entropy, offering a novel perspective on this enduring problem. It establishes a theoretical connec
While fine-tuning diffusion models with reinforcement learning (RL) has demonstrated effectiveness in directly optimizing downstream objectives, existing RL frameworks are prone to overfitting the rewards, leading to outputs that deviate from the true data distribution and exhibit reduced diversity.…