Credit: VentureBeat, created with DALL-E

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 not perform as well for “held-out,” or new environments. By following only the rules laid out through the training data, agents trained with these frameworks would have trouble “learning” from their mistakes and cannot be made into general agents and brought into to new workflows. 

To combat that limitation, AgentRefine aims to create more generalized agent training datasets that enable the model to learn from mistakes and fit into new workflows. In a new paper, the researchers said that AgentRefine’s goal is “to develop generalized agent-tuning data and establish the correlation between agent generalization and self-refinement.” If agents self-correct, they will not perpetuate any errors they learned and bring these same mistakes to other environments they’re deployed in. 

“We find that agent-tuning on the self-refinement data enhances the agent to explore more viable actions while meeting bad situations, thereby resu...