2 papers
cs.LG2026
Unbalanced W-Flow: Unbalanced Wasserstein Drifting under Finite-Batch Marginal Fluctuations
Jongwook Jeon, Mingue Park, Jiwon Choi +3
Teacher-free one-step generative models such as W-Flow learn directly from distributional objectives, with the Sinkhorn divergence providing the strongest empirical performance amo…
cs.LG2026
Learning Multimodal One-step Flow Policy via Value-weighted Optimal Transport
Jaehun Shon, Jinha Choi, Jongwook Jeon +1
Offline reinforcement learning aims to learn a policy solely from fixed datasets, which often contain multimodal action distributions. Flow policies can naturally represent such mu…