Meta’s DreamGym framework trains AI agents in a simulated world to cut reinforcement learning costs

Researchers at Meta, the University of Chicago, and UC Berkeley have developed a new framework that addresses the high costs, infrastructure complexity, and unreliable feedback associated with using reinforcement learning (RL) to train large language model (LLM) agents. The framework, DreamGym, simulates an RL environment to train agents for complex applications. As it progresses through the training process, the framework dynamically adjusts task difficulty, ensuring the agent gradually learns to solve more challenging problems as it improves.Experiments by the research team show that DreamGym substantially improves RL training in both fully synthetic settings and scenarios where the model must apply its simulated learning to the real world. In settings where RL is possible but expensive, it matches the performance of popular algorithms using only synthetic interactions, significantly cutting the costs of data gathering and environment interaction. This approach could be vital for ent