arXiv AI
TryOnReward: Learning Foveated Consistency for Reinforcement Fine-Tuning of Virtual Try-On
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The Reward Was in Your Data All Along: Correcting Flow Matching with Discriminator-Guided RL
arXiv:2606. 19162v1 Announce Type: new Abstract: Score- and flow-matching models often rely on preference-based reinforcement learning for two purposes: aligning with subjective preferences and, surprisingly, recovering properties such as visual realism and coherent object structure that matching-based training is intended to learn from the data itself.
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Beyond Binary Preferences: Graded Preference Optimization for Limb-Motion Captioning
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VA-Judger: Reward Modeling from Human Preference Feedback for Joint Video-Audio Generation
arXiv:2608. 18607v2 Announce Type: replace Abstract: Using reinforcement learning to post-train joint video-audio generation models requires a reward signal.
arXiv AI
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TrustRoboReward: Preference-Ordered Isotonic Score Editing for Multi-Paradigm Robot Reward Models
arXiv:2608. 08491v1 Announce Type: new Abstract: Reward models are a bottleneck for reinforcement learning in embodied AI.
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Debiasing Text-to-Image Evaluation via Implicit Cultural Alignment Reward Modeling
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arXiv Computer Vision
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WorldReward: Reward Modeling for Camera-Conditioned World Models
WorldReward introduces a vision‑language model–based reward system for camera‑conditioned world models, combining action consistency and visual quality evaluation. It processes paired videos by splitting them into action‑aligned chunks, structuring visual evidence, and aggregating decisions through voting. The model is trained on a large, reasoning‑augmented preference dataset and outperforms GPT‑5.5 on a human‑annotated benchmark, improving both action execution and visual quality when applied to RL post‑training.