Reverse-Engineered Reasoning for Open-Ended Generation
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.
68bf936f207285de11b07b79
Haozhe Wang
68bf936f207285de11b07b7a
Haoran Que
68bf936f207285de11b07b7b
Qixin Xu
claimed_verified
2025-09-09T13:48:04.349Z
6680f0b20b72be136708af26
/avatars/5d8fd5be0cf94e246b46abb9d3cc8f5c.svg
XuQixin
user
Racktic
68bf936f207285de11b07b7c
Minghao Liu
claimed_verified
2025-09-09T13:48:08.356Z
6417d9ea8f689506e7148417
https://cdn-avatars.huggingface.co/v1/production/uploads/6417d9ea8f689506e7148417/bAYcruWNw4WvmuQcGgcwC.jpeg
minghao
user
Liam-Liu
68bf936f207285de11b07b7d
Wangchunshu Zhou
68bf936f207285de11b07b7e
Jiazhan Feng
68bf936f207285de11b07b7f
Wanjun Zhong
68bf936f207285de11b07b80
Wei Ye
68bf936f207285de11b07b81
Tong Yang
68bf936f207285de11b07b82
Wenhao Huang
68bf936f207285de11b07b83
Ge Zhang
claimed_verified
2025-09-09T13:47:57.642Z
638efcf4c67af472d316d424
/avatars/97a57859d7d87a3a8f1bb41d32a72bc2.svg
Ge Zhang
user
zhangysk
68bf936f207285de11b07b84
Fangzhen Lin
Reverse-Engineered Reasoning for Open-Ended Generation
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.
REER, a new paradigm for deep reasoning, uses reverse engineering to discover step-by-step reasoning processes, enabling a model to perform competitively on open-ended tasks.
deep reasoning reinforcement learning instruction distillation REER gradient-free DeepWriting-20K DeepWriter-8B GPT-4o Claude 3.5