HERO uses hindsight reflection to improve agent self-distillation

A new arXiv paper introduces HERO, a hindsight-enhanced reflection method for agentic self-distillation.

The idea is to use environment observations after the fact to improve how an agent reflects on its own behavior. That can help turn failed or imperfect trajectories into better training signals.

The method fits a broader trend in agent research: moving beyond prompt-only behavior toward systems that learn from execution traces and feedback loops.