Author

Austin S. Wang

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#machine learning Preprint Sep 2026

Aligning One-Step Generative Models with Reward-Weighted Transport Distillation

Theoretical analysis shows that the fixed-point distributions of RWTD interpolate between off-policy reward tilting of the reference and on-policy tilting of the current model, providing a principled approach to balancing reward adaptation with retention of prior knowledge.

Austin S. Wang, Zi-Heng Cheng, Le-Xing Ying · 0 citations

#machine learning May 2026

Beyond Pairwise Preferences: Listwise Reward-Aware Alignment for Diffusion Models

Diffusion LAIR is proposed, a reward-aware listwise preference optimization method for diffusion models that outperforms strong preference optimization baselines on SD1.5 and SDXL across text-to-image generation, compositional generation, and image editing benchmarks.

Austin S. Wang, Jiaqi Han, S. Ermon et al. · arXiv.org · 1 citation