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.
REER is efficient because it's gradient-free and uses existing good outputs as anchors, avoiding endless trial-and-error. It produces diverse, high-quality reasoning data tailored to creative tasks, helping AI learn planning, alternative exploration, and self-correction. This leads to more thoughtful, human-like outputs without needing massive resources or perfect rewards, making deep reasoning accessible for open-ended generation.