𝗖𝗮𝗻 𝗮 𝗺𝘂𝗹𝘁𝗶𝗺𝗼𝗱𝗮𝗹 𝗺𝗼𝗱𝗲𝗹 𝗯𝗲𝗰𝗼𝗺𝗲 𝗮 𝗯𝗲𝘁𝘁𝗲𝗿 𝗶𝗺𝗮𝗴𝗲 𝗴𝗲𝗻𝗲𝗿𝗮𝘁𝗼𝗿 𝗯𝘆 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗳𝗿𝗼𝗺 𝗶𝘁𝘀 𝗼𝘄𝗻 𝗳𝗲𝗲𝗱𝗯𝗮𝗰𝗸? 🚀 Excited to share 𝗨𝗻𝗶𝗘𝘃𝗼-𝗩𝗟, our new work on on-policy self-distillation for multimodal model self-improvement! The motivation is simple: as generation and understanding come together in multimodal systems, models can provide feedback on their own outputs. Can that feedback become a useful training signal—turning their ability to evaluate into improvements in generation? 📈 𝗧𝗵𝗲 𝗿𝗲𝘀𝘂𝗹𝘁𝘀 Building on Qwen-image-2512, UniEvo-VL improves: • GenEval: 𝟬.𝟳𝟰𝟳 → 𝟬.𝟴𝟬𝟴 • GenEval2 Soft-TIFA: 𝟯𝟮.𝟵𝟳 → 𝟯𝟱.𝟱𝟯 These gains show the potential of learning from self-generated feedback. But the boundaries are just as interesting. 🔍 𝗪𝗵𝗮𝘁 𝘀𝗲𝘁𝘀 𝘁𝗵𝗲 𝗰𝗲𝗶𝗹𝗶𝗻𝗴? Experiments with stronger external critics suggest room for further improvement. This raises a central challenge: producing feedback is only the beginning. That feedback must be reliable enough to guide learning. Understanding what makes a good critic matters for building a better generator. 🔍 𝗗𝗼𝗲𝘀 𝗶𝗺𝗽𝗿𝗼𝘃𝗲𝗺𝗲𝗻𝘁 𝘁𝗿𝗮𝗻𝘀𝗳𝗲𝗿 𝗮𝗰𝗿𝗼𝘀𝘀 𝘁𝗮𝘀𝗸𝘀? Not uniformly. Text-rendering results remain mixed, even as the overall generation benchmarks improve. Progress on one benchmark does not mean every capability improves alongside it—a useful boundary to understand when studying self-improving systems. For me, the broader research question is: 𝗪𝗵𝗲𝗻 𝗶𝘀 𝗮 𝗺𝗼𝗱𝗲𝗹’𝘀 𝗼𝘄𝗻 𝗳𝗲𝗲𝗱𝗯𝗮𝗰𝗸 𝗿𝗲𝗹𝗶𝗮𝗯𝗹𝗲 𝗲𝗻𝗼𝘂𝗴𝗵 𝘁𝗼 𝗵𝗲𝗹𝗽 𝗶𝘁 𝗶𝗺𝗽𝗿𝗼𝘃𝗲? UniEvo-VL takes a step toward answering this question for multimodal generation, exploring both the opportunities and the limitations. Grateful to all my collaborators for making this work possible! If you’re working on self-distillation, multimodal post-training, or self-improving models, I’d love to hear your perspective. 📄 Paper: arxiv.org/abs/2609.38721 🤗 Hugging Face: huggingface.co/papers/2609.3… If you find the work interesting, a repost or an upvote on Hugging Face would help more researchers discover it. Thanks for your support! 🙏 #UniEvoVL #SelfDistillation #MultimodalAI #ImageGeneration #PostTraining