Policy and World Modeling Co-Training for Language Agents
Researchers have developed a new framework called PaW for training language agents. This method co-trains policy and world modeling components simultaneously during reinforcement learning. PaW leverages existing RL data to provide world modeling supervision, avoiding the need for separate simulators or additional computation. AI
IMPACT Introduces a more efficient method for training language agents by integrating world modeling with reinforcement learning.