{"ID":2873827,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.06160","arxiv_id":"2509.06160","title":"Reverse-Engineered Reasoning for Open-Ended Generation","abstract":"While the ``deep reasoning'' paradigm has spurred significant advances in verifiable domains like mathematics, its application to open-ended, creative generation remains a critical challenge. The two dominant methods for instilling reasoning -- reinforcement learning (RL) and instruction distillation -- falter in this area; RL struggles with the absence of clear reward signals and high-quality reward models, while distillation is prohibitively expensive and capped by the teacher model's capabilities. To overcome these limitations, we introduce REverse-Engineered Reasoning (REER), a new paradigm that fundamentally shifts the approach. Instead of building a reasoning process ``forwards'' through trial-and-error or imitation, REER works ``backwards'' from known-good solutions to computationally discover the latent, step-by-step deep reasoning process that could have produced them. Using this scalable, gradient-free approach, we curate and open-source DeepWriting-20K, a large-scale dataset of 20,000 deep reasoning trajectories for open-ended tasks. Our model, DeepWriter-8B, trained on this data, not only surpasses strong open-source baselines but also achieves performance competitive with, and at times superior to, leading proprietary models like GPT-4o and Claude 3.5.","short_abstract":"While the ``deep reasoning'' paradigm has spurred significant advances in verifiable domains like mathematics, its application to open-ended, creative generation remains a critical challenge. The two dominant methods for instilling reasoning -- reinforcement learning (RL) and instruction distillation -- falter in this...","url_abs":"https://arxiv.org/abs/2509.06160","url_pdf":"https://arxiv.org/pdf/2509.06160v1","authors":"[\"Haozhe Wang\",\"Haoran Que\",\"Qixin Xu\",\"Minghao Liu\",\"Wangchunshu Zhou\",\"Jiazhan Feng\",\"Wanjun Zhong\",\"Wei Ye\",\"Tong Yang\",\"Wenhao Huang\",\"Ge Zhang\",\"Fangzhen Lin\"]","published":"2025-09-07T18:07:58Z","proceeding":"cs.AI","tasks":"[\"cs.AI\",\"cs.CL\"]","methods":"[\"Reinforcement Learning\"]","has_code":false}