Reasoning Traces from QA Pairs (huggingface.co)

A new AI paradigm called REER (REverse-Engineered Reasoning) offers a fresh approach to deep reasoning by reverse-engineering step-by-step thought processes from high-quality end solutions, rather than building reasoning forwards through trial-and-error or imitation. This method addresses key challenges in open-ended generative tasks where reinforcement learning struggles due to unclear reward signals, and instruction distillation is costly and limited by teacher models. By computationally extracting latent reasoning chains, REER enables models to better understand and mimic complex problem solving in creative domains. The team behind REER has also released DeepWriting-20K, a large-scale dataset containing 20,000 deep reasoning trajectories tailored for open-ended tasks, underpinning their newly trained DeepWriter-8B model. This model not only outperforms strong open-source baselines but compares favorably—and sometimes exceeds—the performance of top proprietary models such as GPT-4o and Claude 3.5. REER’s gradient-free, scalable paradigm represents a significant technical advancement in enabling more interpretable, verifiable, and effective reasoning in AI systems tackling complex, open-ended generation challenges.