REER, or Reverse-Engineered Reasoning, is a new way to teach AI models how to think deeply and step-by-step for open-ended tasks like writing stories or essays. Unlike traditional methods that build reasoning from scratch, REER starts with a high-quality final answer and works backward to uncover the hidden thinking process that could have led to it. This creates useful "reasoning trajectories"—detailed paths of thought—for training AI to handle creative, unstructured problems.
Forward methods, like trial-and-error (reinforcement learning) or copying from a teacher model (distillation), struggle with open-ended tasks. Reinforcement learning needs clear rewards to guide improvements, but creative writing lacks obvious "right" or "wrong" answers—there's no simple score for a poem or story. Distillation requires a super-smart teacher model and is too expensive to scale. These approaches often produce shallow or random outputs because they can't reliably explore the vast space of possible ideas without strong guidance.