RL for Image Generation

This field uses reinforcement learning to post-train image generators, including diffusion and flow-matching models, autoregressive systems, and unified multimodal models. Common approaches optimize policies with methods such as GRPO, using rewards from human preferences, vision-language models, prompt-specific rubrics, verifiable visual criteria, or the model’s own understanding signals. Researchers also study finer-grained feedback, including token-, region-, and denoising-step rewards, as well as reasoning, reflection, and iterative editing; progress is assessed on tasks such as GenEval and T2I-CompBench. A central challenge is finding rewards and optimization rules that improve faithfulness and image quality without reward hacking, loss of diversity, unstable training, or excessive compute.