Researchers improved AI agent performance on unfamiliar tasks using ‘Dungeons and Dragons’

Organizations interested in deploying AI agents must first fine-tune them, especially in workflows that often feel rote. While some organizations want agents that only perform one kind of task in one workflow, sometimes agents need to be brought into new environments with the hope that they adapt. 

Researchers from the Beijing University of Posts and Telecommunications have unveiled a new method, AgentRefine. It teaches agents to self-correct, leading to more generalized and adaptive AI agents. 

The researchers said that current tuning methods limit agents to the same tasks as their training dataset, or “held-in” tasks, and do…