arXiv:2605.26013v1 Announce Type: new Abstract: We introduce AdvantageFlow, a forward-process reinforcement learning algorithm for rectified flow models. Unlike Flow-GRPO, which optimizes the reverse process, we optimize an advantage-weighted forward-process prediction loss. This optimization problem is unstable when advantages are negative and the loss becomes non-convex. We stabilize it by rollout policy regularization, which reduces variance and arises from fitting a local reward-improving target distribution. We evaluate AdvantageFlow on image generation tasks with Stable Diffusion 3.5 Med
The paper introduces a significant algorithmic advancement in reinforcement learning for rectified flow models, building directly on prior work (Flow-GRPO) and addressing stability issues for improved performance in generative AI.
This development can lead to more stable and efficient training of generative AI models, particularly in image generation, impacting the development and deployment of advanced AI applications.
The optimization approach for rectified flow models shifts to an advantage-weighted forward-process prediction loss, potentially making generative AI more robust and accessible.
- · AI researchers
- · Generative AI developers
- · Image generation platforms
- · Developers using less efficient generative model training methods
Improved stability and performance in generative AI models like Stable Diffusion.
Faster iteration and deployment of new AI capabilities, expanding applications in various industries.
Potentially democratizing advanced generative AI by lowering computational barriers for certain tasks.
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Read at arXiv cs.LG