AdvantageFlow: Advantage-Weighted Least Squares for RL in Flow Models
📰 ArXiv cs.AI
Learn how AdvantageFlow stabilizes reinforcement learning in flow models using advantage-weighted least squares, improving upon existing methods like Flow-GRPO
Action Steps
- Implement AdvantageFlow algorithm using advantage-weighted least squares for forward-process prediction
- Apply rollout policy regularization to reduce variance and stabilize the optimization problem
- Compare the performance of AdvantageFlow with existing methods like Flow-GRPO
- Configure the hyperparameters of AdvantageFlow for optimal results
- Test AdvantageFlow on various flow models to evaluate its effectiveness
Who Needs to Know This
Researchers and engineers working on reinforcement learning and flow models can benefit from this article to improve their understanding of stable optimization techniques
Key Insight
💡 AdvantageFlow stabilizes the optimization problem by using rollout policy regularization, reducing variance and improving performance
Share This
💡 Introducing AdvantageFlow: stabilizing RL in flow models with advantage-weighted least squares! 🚀
Key Takeaways
Learn how AdvantageFlow stabilizes reinforcement learning in flow models using advantage-weighted least squares, improving upon existing methods like Flow-GRPO
Full Article
Title: AdvantageFlow: Advantage-Weighted Least Squares for RL in Flow Models
Abstract:
arXiv:2605.26013v1 Announce Type: cross 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
Abstract:
arXiv:2605.26013v1 Announce Type: cross 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
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