GLASS Flows: Efficient Inference for Reward Alignment of Flow and Diffusion Models

ICLROral2026

Authors
Peter Holderrieth, Uriel Singer, Tommi Jaakkola, Ricky T. Q. Chen, Yaron Lipman, Brian Karrer
Affiliation
MIT
Venue
ICLR 2026
Track
Oral

TL;DR

We improve inference-time reward alignment of flow matching and diffusion models by proposing a novel sampling paradigm that enables more efficient exploration.

Opening excerpt from the authors’ abstract. source

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Topics

flow matching exploration alignment diffusion efficient

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