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.
1. **Gather Data**: Collect input-output pairs (e.g., a writing prompt and a great response).
2. **Initialize**: Create a simple starting trajectory, like a one-sentence plan.
3. **Refine Iteratively**: Divide the trajectory into parts. For each part, generate variations and score them by perplexity. Replace with the best version if it improves the whole path's score.
4. **Filter and Curate**: Discard low-quality paths (e.g., those not ending coherently). Add diversity by covering genres like stories or essays.
5. **Use for Training**: Feed these trajectories into AI models to teach deep thinking patterns.