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Researchers improved AI agent performance on unfamiliar tasks using ‘Dungeons and Dragons’
AgentRefine gives AI agents and models the ability to recognize errors and self-correct to work better for general tasks.
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. Taking their cue from the tabletop roleplaying game Dungeons & Dragons, the researchers created personas, scripts for the agent to follow and challenges. The researchers found that agents trained using the AgentRefine method and dataset performed better on diverse tasks and adapted to new scenarios.
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