OpenNovelty.org

Reverse-Engineered Reasoning for Open-Ended Generation

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 6.0Download Report PDF
reasoningopen-ended generationsynthetic data
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.

Disclaimer
This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.
NOTE that some papers exist in multiple, slightly different versions (e.g., with different titles or URLs). The system may retrieve several versions of the same underlying work. The current automated pipeline does not reliably align or distinguish these cases, so human reviewers will need to disambiguate them manually.
If you have any questions, please contact: mingzhang23@m.fudan.edu.cn

Overview

Overall Novelty Assessment

The paper introduces REER, a backward reasoning paradigm that derives step-by-step processes from known good solutions, and curates DeepWriting-20K, a dataset of 20,000 reasoning trajectories for open-ended tasks. According to the taxonomy, this work resides in the 'Reverse-Engineering and Data Curation' leaf, which currently contains only this single paper. This indicates a sparse research direction within the broader 'Alternative Training Paradigms' branch, suggesting the backward reasoning approach represents a relatively unexplored corner of the field compared to more populated areas like reward modeling or chain-of-thought fine-tuning.

The taxonomy reveals neighboring directions including 'Chain-of-Thought Fine-Tuning and MCTS Integration' and 'Bi-Directional and Deliberative Reasoning Mechanisms', both focusing on forward reasoning generation or hybrid forward-backward architectures. The broader 'Reasoning Paradigms and Training Methods' branch encompasses reinforcement learning approaches and decoding strategies, which the paper explicitly positions against. The scope note for the parent 'Alternative Training Paradigms' node clarifies that this branch excludes RL-based optimization and inference-time methods, emphasizing that REER's data curation approach occupies a distinct methodological space focused on pre-training data quality rather than online optimization or prompting techniques.

Among 27 total candidates examined across three contributions, the REER paradigm shows limited prior overlap: 10 candidates examined, with only 1 appearing to provide refutable prior work. The DeepWriting-20K dataset and DeepWriter-8B model show no clear refutation among their respective candidate sets (10 and 7 papers examined). This suggests that within the limited semantic search scope, the backward reasoning methodology and open-ended reasoning dataset represent relatively novel contributions, though the small candidate pool (27 papers, not hundreds) means the search captured a focused but incomplete view of potentially relevant prior work.

The analysis indicates moderate novelty given the sparse taxonomy position and limited refutation signals, though the restricted search scope (top-K semantic matches plus citations) leaves open the possibility of unexamined related work. The backward reasoning paradigm appears less explored than forward methods, but the single-paper taxonomy leaf and modest candidate examination suggest caution in drawing definitive conclusions about the field's coverage of reverse-engineering approaches.

Taxonomy

Core-task Taxonomy Papers
50
3
Claimed Contributions
27
Contribution Candidate Papers Compared
1
Refutable Paper

Research Landscape Overview

Core task: Instilling deep reasoning for open-ended generation. The field is organized around five major branches that collectively address how to equip language models with robust reasoning capabilities for tasks lacking fixed answers or closed-form solutions. Reasoning Paradigms and Training Methods explores diverse training strategies—ranging from alternative paradigms like reverse-engineering and data curation (Reverse-Engineered Reasoning[0]) to direct optimization techniques (Direct Reasoning Optimization[4]) and self-consistency approaches (Universal Self-Consistency[6]). Application Domains and Task-Specific Reasoning focuses on deploying reasoning in specialized contexts such as scientific discovery (SciReasoner[17]), multimodal understanding (MMReason[25]), and collaborative deliberation (Democratic Deliberation AI[5]). Representation, Retrieval, and Knowledge Integration examines how external knowledge and structured retrieval (Agentic RAG Survey[8], Graphrag-bench[2]) support reasoning, while Evaluation and Benchmarking develops metrics and testbeds for assessing open-ended outputs (ACPBench Hard[21], MedGEN-Bench[49]). Finally, Instruction Understanding and Intent Alignment investigates how models interpret user goals and align their reasoning with human intentions (LLM Human Intentions[20]). A particularly active line of work centers on alternative training paradigms that curate high-quality reasoning traces without relying solely on standard supervised fine-tuning. Reverse-Engineered Reasoning[0] sits squarely within this cluster, emphasizing data curation and reverse-engineering strategies to extract or synthesize reasoning steps. This contrasts with methods like Direct Reasoning Optimization[4], which directly optimizes reasoning pathways, and Marco-o1[1], which may integrate structured reasoning into model architectures. Meanwhile, works such as Universal Self-Consistency[6] and Scalable Best-of-N[19] explore inference-time aggregation and selection to improve reasoning reliability. The trade-offs revolve around whether to invest effort in curating diverse reasoning data upfront or to refine reasoning dynamically during generation. Reverse-Engineered Reasoning[0] aligns closely with data-centric approaches that prioritize the quality and diversity of training examples, offering a complementary perspective to optimization-focused or architectural innovations seen in nearby efforts.

Claimed Contributions

REverse-Engineered Reasoning (REER) paradigm

The authors propose a novel paradigm that synthesizes deep reasoning trajectories by working backwards from high-quality outputs rather than building reasoning forwards through reinforcement learning or distillation. This gradient-free approach computationally discovers the latent step-by-step reasoning process that could have produced known good solutions.

10 retrieved papers
Can Refute
DeepWriting-20K dataset

The authors contribute a comprehensive open-source dataset containing 20,000 query-response pairs with deep reasoning trajectories spanning 25 categories across ordinary-life question-answering, academic writing, functional writing, and creative writing. This resource addresses data scarcity for research into planning and structured thought in open-ended generation.

10 retrieved papers
DeepWriter-8B model achieving competitive performance

The authors demonstrate that their model, trained entirely on synthesized data using the REER paradigm, matches or exceeds the performance of premier proprietary models on challenging writing benchmarks. This provides empirical evidence that human-like deep reasoning can be cultivated from scratch without costly distillation or reinforcement learning.

7 retrieved papers

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Within the taxonomy built over the current TopK core-task papers, the original paper is assigned to a leaf with no direct siblings and no cousin branches under the same grandparent topic. In this retrieved landscape, it appears structurally isolated, which is one partial signal of novelty, but still constrained by search coverage and taxonomy granularity.

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

REverse-Engineered Reasoning (REER) paradigm

The authors propose a novel paradigm that synthesizes deep reasoning trajectories by working backwards from high-quality outputs rather than building reasoning forwards through reinforcement learning or distillation. This gradient-free approach computationally discovers the latent step-by-step reasoning process that could have produced known good solutions.

Contribution

DeepWriting-20K dataset

The authors contribute a comprehensive open-source dataset containing 20,000 query-response pairs with deep reasoning trajectories spanning 25 categories across ordinary-life question-answering, academic writing, functional writing, and creative writing. This resource addresses data scarcity for research into planning and structured thought in open-ended generation.

Contribution

DeepWriter-8B model achieving competitive performance

The authors demonstrate that their model, trained entirely on synthesized data using the REER paradigm, matches or exceeds the performance of premier proprietary models on challenging writing benchmarks. This provides empirical evidence that human-like deep reasoning can be cultivated from scratch without costly distillation or reinforcement learning.

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