✨ReViSE: Towards Reason-Informed Video Editing in Unified Models with Self-Reflective Learning

📝 Summary:
The ReViSE framework enables reason-informed video editing by addressing the disconnect between models reasoning and editing capabilities. It uses a self-reflective learning mechanism with an internal VLM to provide intrinsic feedback. This significantly enhances editing accuracy and visual fidel...

🔹 Publication Date: Published on Dec 10

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.09924
• PDF: https://arxiv.org/pdf/2512.09924
• Github: https://github.com/Liuxinyv/ReViSE

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#VideoEditing #AI #MachineLearning #VLM #SelfReflectiveLearning

🔥 AlphaGRPO: Unlocking Self-Reflective Multimodal Generation in UMMs via Decompositional Verifiable Reward

💡 The paper introduces AlphaGRPO, a novel framework that enhances multimodal generation capabilities in unified multimodal models. The problem addressed is the need for improved multimodal generation without requiring an additional cold-start stage. To solve this, the authors apply Group Relative Policy Optimization to AR-Diffusion Unified Multimodal Models, enabling self-reflective refinement and decompositional verifiable reward mechanisms.

The method involves using Decompositional Verifiable Reward, which decomposes complex user requests into atomic, verifiable semantic and quality questions. These questions are then evaluated by a general multimodal language model to provide reliable and interpretable feedback. This approach allows the model to perform advanced reasoning tasks, including reasoning text-to-image generation and self-reflective refinement.

The results show that AlphaGRPO yields robust improvements across multimodal generation benchmarks, including GenEval, TIIF-Bench, DPG-Bench, and WISE. The framework also achieves significant gains in editing tasks on GEdit without training on editing tasks. The experiments demonstrate that the self-reflective reinforcement approach effectively leverages inherent understanding to guide high-fidelity generation, validating the effectiveness of AlphaGRPO. Overall, the paper contributes to the development of more advanced and reliable multimodal generation models.

📅 Published on May 12

🔗 Links:
• arXiv: https://arxiv.org/abs/2605.12495
• PDF: https://arxiv.org/pdf/2605.12495
• Project Page: https://huangrh99.github.io/AlphaGRPO/
• GitHub: https://github.com/huangrh99/AlphaGRPO ⭐ 37

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#MultimodalGeneration #UnifiedMultimodalModels #SelfReflectiveLearning #DecompositionalReward #MultimodalDeepLearning