arXiv Machine Learning
Reward-guided Fine-Tuning of One-Step Generative Models via Wasserstein Gradient Flow
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Optimizing Visual Generative Models via Distribution-wise Rewards
arXiv:2607. 02291v1 Announce Type: new Abstract: Conventional reinforcement learning strategies for visual generation typically employ sample-wise reward functions, yet this practice frequently results in reward hacking that degrades image diversity and introduces visual anomalies.
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arXiv Machine Learning
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FlowAWR: Online Adaptive Flow Reinforcement via Advantage-Weighted Rectification
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