  Recently, there has been an increasing interest in automated prompt
optimization based on reinforcement learning (RL). This approach offers
important advantages, such as generating interpretable prompts and being
compatible with black-box foundation models. However, the substantial prompt
space size poses challenges for RL-based methods, often leading to suboptimal
policy convergence. This paper introduces MultiPrompter, a new framework that
views prompt optimization as a cooperative game between prompters which take
turns composing a prompt together. Our cooperative prompt optimization
effectively reduces the problem size and helps prompters learn optimal prompts.
We test our method on the text-to-image task and show its ability to generate
higher-quality images than baselines.
