Kavli Affiliate: Jing Wang | First 5 Authors: Jing Wang, Jing Wang, , , | Summary: Recently, GRPO-based reinforcement learning has shown remarkable progress in optimizing flow-matching models, effectively improving their alignment with task-specific rewards. Within these frameworks, the policy update relies on importance-ratio clipping to constrain overconfident positive and negative gradients. However, in practice, […]
Continue.. GRPO-Guard: Mitigating Implicit Over-Optimization in Flow Matching via Regulated Clipping