Step Back to Move Forward: Reflection-Aware Preference Optimization for Visual Generation
2026-09-07 12:00Models🔥 42.2 heat score
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To overcome the local optimal dilemma commonly encountered in diffusion model reinforcement learning, researchers proposed a new framework called Reflection-Aware GRPO (RA-GRPO). This approach introduces a Diffusion Reflection mechanism to correct the sampling trajectory and utilizes Counterfactual Path Synthesis to internalize the optimization benefits into the strategy. Experimental results show that RA-GRPO outperforms existing methods in text-generation image and video tasks, effectively mitigating the reward hacking phenomenon and improving generalization ability. Additionally, this framework maintains architecture independence and can be seamlessly integrated into standard processes.
The Reflection-Aware GRPO framework is proposed to address the local optimization problem in diffusion model reinforcement learning. This method修正 the sampling trajectory through Diffusion Reflection and integrates the optimization benefits into the strategy using Counterfactual Path Synthesis. Experiments on text-to-image and text-to-video tasks show that RA-GRPO outperforms existing methods significantly, effectively reducing reward hacking and enhancing generalization ability. This framework maintains architecture independence and can be seamlessly integrated with standard processes.