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

Page: https://stenobird.com/podcast/daily-paper-cast-7079649/alphagrpo-unlocking-self-reflective-multimodal-generation-in-umms-via-decompositional-verifiable-reward
Text version: https://stenobird.com/podcast/daily-paper-cast-7079649/alphagrpo-unlocking-self-reflective-multimodal-generation-in-umms-via-decompositional-verifiable-reward.md
Podcast: [Daily Paper Cast](https://stenobird.com/podcast/daily-paper-cast-7079649)
Published: 2026-05-14T04:31:06+00:00
Episode link: https://share.transistor.fm/s/ee158ab8
Audio file: https://media.transistor.fm/ee158ab8/c4487b65.mp3
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JSON: https://stenobird.com/v1/public/podcasts/daily-paper-cast-7079649/episodes/alphagrpo-unlocking-self-reflective-multimodal-generation-in-umms-via-decompositional-verifiable-reward
Duration seconds: 1439

## Resource

🤗 Upvotes: 28 | cs.CV, cs.AI, cs.LG Authors: Runhui Huang, Jie Wu, Rui Yang, Zhe Liu, Hengshuang Zhao Title: AlphaGRPO: Unlocking Self-Reflective Multimodal Generation in UMMs via Decompositional Verifiable Reward Arxiv: http://arxiv.org/abs/2605.12495v1 Abstract: In this paper, we propose AlphaGRPO, a novel framework that applies Group Relative Policy Optimization (GRPO) to AR-Diffusion Unified Multimodal Models (UMMs) to enhance multimodal generation capabilities without an additional cold-start stage. Our approach unlocks the model's intrinsic potential to perform advanced reasoning tasks: Reasoning Text-to-Image Generation, where the model actively infers implicit user intents, and Self-Reflective Refinement, where it autonomously diagnoses and corrects misalignments in generated outputs. To address the challenge of providing stable supervision for real-world multimodal generation, we introduce the Decompositional Verifiable Reward (DVReward). Unlike holistic scalar rewards, DVReward utilizes an LLM to decompose complex user requests into atomic, verifiable semantic and quality questions, which are then evaluated by a general MLLM to provide reliable and interpretable feedback. Extensive experiments demonstrate that AlphaGRPO yields robust improvements across multimodal generation benchmarks, including GenEval, TIIF-Bench, DPG-Bench and WISE, while also achieving significant gains in editing tasks on GEdit without training on editing tasks. These results validate that our self-reflective reinforcement approach effectively leverages inherent understanding to guide high-fidelity generation. Project page: https://huangrh99.github.io/AlphaGRPO/

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