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.
Figures & tables
Figure 1: Comparison between without-Reflection, with Good-Reflection, Under-Reflection and Over-Reflection.
Figure 2: Performance - Reflection Quality.
Model
Single-Img
Ref. Quality (U/G/O)
Qwen-Image-Fast
0.505
–
Gemini2.5Pro
0.515
32.0%/55.4%/12.6%
Qwen3VL32B
0.500
41.3%/49.7%/9.0%
SFT-Reflection
0.499
58.2%/38.9%/2.9%
Reflection-s30
0.510
47.7%/48.9%/3.3%
Reflection-s130
0.521
33.7%/58.5%/7.8%
Table 1: Reflection experiment on Single-Img task in VCR-Bench. Ref. Quality (U/G/O): reflection quality with under-/good-/over-reflection percentages. And all models’ plan score on the single-image task is larger than 99.5%.
Model
Multi-Img
Ref. Quality (U/G/O)
Plan Score
SR
0.565
68.6%/21.6%/9.8%
0.8285
RPC
0.617
73.9%/16.5%/9.7%
0.8431
GPro
0.649
61.6%/31.8%/6.6%
0.9667
Table 2: Transfer from single-image reflection to multi-image tasks. Model: SR is Strong-Reflection model, RPC is Reflection-Plan Conflict model, GPro is Gemini2.5Pro.