DeLoop Method to Reduce Agentic Loops via Self-Supervision

DeLoop uses direct preference optimization to train agents to avoid repetitive tool calls and redundant actions. It identifies loops in agent rollouts and uses the first subsequent non-looping action as a positive training signal to improve long-horizon task performance.