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…
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