Learning $ω$-Powered Conditional Distributions with Importance-Weighted Flow Matching
Classifier-Free Guidance (CFG) with guidance scale $γ$ is commonly motivated by the $ω$-powered conditional distribution obtained by setting $ω=γ$, but CFG does not, in general, sample from this distribution exactly. We study whether the powered target can instead be learned explicitly. We introduce Importance-Weighted Conditional Flow Matching (IW-CFM), which reweights conditional flow-matching examples using density ratios obtained from frozen conditional and unconditional flow models. With exact normalized weights, the resulting objective has the same gradient as flow matching on the powered target; in practice, we use self-normalized estimation and a regularized empirical approximatio...
| Authors | Maciej Falkiewicz, Frantzeska Lavda, Jason Ramapuram et al. |
|---|---|
| Primary category | cs.LG |
| Published | 12 October 2026 |
| Last updated at source | 12 October 2026 |
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