MultiPrompter: Cooperative Prompt Optimization with Multi-Agent
  Reinforcement Learning
Dong-Ki Kim Sungryull Sohn Lajanugen Logeswaran Dongsub Shim Honglak Lee
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.