CyCLeGen: Cycle-Consistent Layout Prediction and Image Generation

We present CyCLeGen, an autoregressive framework that integrates layout understanding and layout-to-image generation through cycle consistency: predicted layouts must produce faithful images, and generated images must yield consistent layouts. We enforce this constraint via CycleGRPO, a bidirectional reinforcement learning strategy with complementary geometric and perceptual rewards, enabling self-introspective learning from only 8k RL samples. This creates a natural loop, generation helps understanding by rewarding only those layouts that lead to high-quality images, and understanding helps generation by rewarding only those images whose spatial structure can be faithfully recovered. Extensive experiments show that CyCLeGen achieves significant gains across diverse image understanding and generation benchmarks, with emergent gains on image captioning.