Computer Science > Computer Vision and Pattern Recognition
arXiv:2603.08812 (cs)
[Submitted on 9 Mar 2026]
Title:VisionCreator-R1: A Reflection-Enhanced Native Visual-Generation Agentic Model
Authors:Jinxiang Lai, Wenzhe Zhao, Zexin Lu, Hualei Zhang, Qinyu Yang, Rongwei Quan, Zhimin Li, Shuai Shao, Song Guo, Qinglin Lu
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Abstract:Visual content generation has advanced from single-image to multi-image workflows, yet existing agents remain largely plan-driven and lack systematic reflection mechanisms to correct mid-trajectory visual errors. To address this limitation, we propose VisionCreator-R1, a native visual generation agent with explicit reflection, together with a Reflection-Plan Co-Optimization (RPCO) training methodology. Through extensive experiments and trajectory-level analysis, we uncover reflection-plan optimization asymmetry in reinforcement learning (RL): planning can be reliably optimized via plan rewards, while reflection learning is hindered by noisy credit assignment. Guided by this insight, our RPCO first trains on the self-constructed VCR-SFT dataset with reflection-strong single-image trajectories and planning-strong multi-image trajectories, then co-optimization on VCR-RL dataset via RL. This yields our unified VisionCreator-R1 agent, which consistently outperforms Gemini2.5Pro on existing benchmarks and our VCR-bench covering single-image and multi-image tasks.
| Subjects: | Computer Vision and Pattern Recognition (cs.CV) |
| Cite as: | arXiv:2603.08812 [cs.CV] |
| (or arXiv:2603.08812v1 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2603.08812
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View a PDF of the paper titled VisionCreator-R1: A Reflection-Enhanced Native Visual-Generation Agentic Model, by Jinxiang Lai and 9 other authors
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