arXiv Machine Learning June 10, 2026 By Ruoyu Wang, Boye Niu, Xiangxin Zhou, Yushi Huang, Tongliang Liu, Chi Zhang

Exploring the Design Space of Reward Backpropagation for Flow Matching

Read the original on arXiv Machine Learning →

arXiv:2606. 11075v1 Announce Type: new Abstract: Aligning text-to-image flow matching models with human preferences via direct reward backpropagation is sample-efficient but hampered by two well-known pathologies: activations cannot be stored across the full sampling trajectory at modern model scale, and chained Jacobian products across steps inflate the reward gradient as it travels back to early indices.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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