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October 20, 2025Open Access

Advantage Weighted Matching: Aligning RL with Pretraining in Diffusion Models

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Novel method aligns RL with pretraining objectives in diffusion models, reducing variance and enhancing convergence.

Advantage Weighted Matching significantly accelerates convergence in diffusion models, optimizing with lower variance.
AWM achieves up to a $24\times$ speedup over traditional methods, improving performance on GenEval and OCR benchmarks.
Leveraging the same objective as pretraining, AWM effectively raises the impact of high-reward samples in RL.
This approach aligns pretraining and RL, presenting a unified framework consistent with policy-gradient theory.

Xue et al. (2025) studied this question.

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