NormGuard: Reward-Preserving Norm Constraints in Flow-Matching Reinforcement Learning
Reinforcement learning (RL) post-training improves the reward alignment of flow-based generators, but often degrades perceptual quality in ways that are not captured by the reward proxy. We identify a simple structural signature of this drift: across three post-training methods (NFT, AWM, DPO), RL fine-tuning inflates the per-step velocity norm $\|v_θ\|$ by $5\%$ to $15\%$ relative to the reference. A form of norm inflation has been studied in classifier-free guidance (CFG), where rescaling the velocity back to a reference norm at inference time can mitigate the resulting artifacts. However, this inference-time correction does not transfer cleanly to RL: rescaling $v_θ$ to match $\|v_{\te...
| Authors | Tianlin Pan, Lianyu Pang, Cheng Da et al. |
|---|---|
| Primary category | cs.LG |
| Published | 26 June 2026 |
| Last updated at source | 6 August 2026 |
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