# Reward-Weighted Transport Distillation

The work introduces Reward-Weighted Transport Distillation (RWTD), a post-training method for one-step generators that needs only generated samples and scalar reward evaluations, builds an adaptive target mixing separately reward-tilted current and reference distributions, realizes it through feature-space optimal transport and fixed-point regression, and raises the one-step SANA Sprint 1.6B GenEval score from 0.73 to 0.80 while showing cross-reward generalization in preference alignment experiments.

The work introduces Reward-Weighted Transport Distillation (RWTD), a post-training method for one-step generators that needs only generated samples and scalar reward evaluations, builds an adaptive target mixing separately reward-tilted current and reference distributions, realizes it through feature-space optimal transport and fixed-point regression, and raises the one-step SANA Sprint 1.6B GenEval score from 0.73 to 0.80 while showing cross-reward generalization in preference alignment experiments.