Flow-OPD: On-Policy Distillation for Flow Matching Models 事件

Flow-OPD: On-Policy Distillation for Flow Matching Models arXiv:2605.08063v5 Announce Type: replace Abstract: Existing Flow Matching (FM) text-to-image models suffer from two critical bottlenecks under multi-task alignment: the reward sparsity induced by scalar-valued rewards, and the gradient interference arising from jointly optimizing heterogeneous objectives, which together give rise to a 'seesaw effect' of competing metrics and pervasive reward hacking. Inspired by the success of On-Policy