Reverse-Engineered Reasoning for Open-Ended Generation (arxiv.org)

Researchers introduced REER (REverse-Engineered Reasoning), a new paradigm that derives step-by-step reasoning processes by working backward from high-quality outputs rather than forward via reinforcement learning or instruction distillation. The paper argues RL is hampered in open-ended creative tasks by missing reward signals and brittle reward models, while distillation is expensive and limited by teacher-model quality. REER uses a scalable, gradient-free search to computationally uncover latent reasoning trajectories that could have produced known-good solutions, and the team has open-sourced a large corpus, DeepWriting-20K, containing 20,000 deep reasoning trajectories for open-ended generation. They trained DeepWriter-8B on this dataset and report that it outperforms strong open-source baselines and is competitive with—and in some cases superior to—proprietary models such as GPT-4o and Claude 3.5. Key technical takeaways: REER bypasses the need for reward engineering or costly teacher models by reverse-engineering chains of thought, relies on gradient-free discovery rather than gradient-driven RL/distillation, and scales via curated trajectory data. Implications for the AI/ML community include a cheaper, more accessible route to instilling structured reasoning in models for creative tasks, faster dataset-driven improvements in chain-of-thought capability, and new avenues to democratize high-quality open-ended generation—though broader validation and analysis of generalization and compute trade-offs remain important next steps.