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FlowAWR: Online Adaptive Flow Reinforcement via Advantage-Weighted Rectification
Aligning generative flow models on continuous spaces via online reinforcement learning is constrained by intractable trajectory likelihoods. Existing density-approximated policy gradient methods rely on stochastic SDE samplers to construct tractable transition kernels, which introduce training-infer
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