jasonrqh/DeepSeek-R1-20k

--- language: - "en" license: "mit" tags: - "reasoning" - "sft" - "chain-of-thought" --- # Rethinking Generalization in Reasoning SFT This repository contains datasets associated with the paper "[Rethinking Generalization in Reasoning SFT: A Conditional Analysis on Optimization, Data, and Model Capability](https://huggingface.co/papers/2604.06628)". The research investigates the factors influencing cross-domain generalization in Large Language Models (LLMs) during reasoning-focused supervised fine-tuning (SFT) with long chain-of-thought (CoT) data. ## Key Findings - **Optimization Dynamics**: Cross-domain performance often follows a **dip-and-recovery** trajectory. Models may require extended training to reach maximum generalization. - **Data Quality and Structure**: Verified long-CoT traces yield consistent cross-domain gains, whereas low-quality solutions or No-CoT data can lead to misleading signals or poor transfer. - **Model Capability**: Stronger base models are more effective at internalizing transferable procedural reasoning patterns (such as backtracking) compared to weaker models. - **Asymmetric Generalization**: The study finds that while reasoning capabilities improve through long-CoT SFT, model safety can simultaneously degrade. In contrast, No-CoT data leads to less reasoning improvement but better safety outcomes. ## Resources - **Paper**: [arXiv:2604.06628](https://huggingface.co/papers/2604.06628) - **Code**: [Official GitHub Repository](https://github.com/Nebularaid2000/rethink_sft_generalization) - **Model Collection**: [Hugging Face Collection](https://huggingface.co/collections/jasonrqh/rethink-sft-generalization) ## Overview of Open-source Models We have open-sourced **ALL** models trained in our experiments, including the **intermediate checkpoints** (you can find them in the `stepxxx` folder in the repo). Note that the following model list may include repeated entries, as it is organized by the experiments and conclusions presented in the paper. | Model Name | Hugging Face | ModelScope | | --- | --- | --- | | **Weak cross-domain generalization is more pronounced under short training and smaller learning rates (refer to Sec. 3.1; App. C.1, Table 4)** | | | | Qwen3-14B_Math-CoT-20k_lr5e-5_ep1_bs256 | [Hugging Face](https://huggingface.co/jasonrqh/Qwen3-14B_Math-CoT-20k_lr5e-5_ep1_bs256) | [ModelScope](https://modelscope.cn/models/nebularaid/Qwen3-14B_Math-CoT-20k_lr5e-5_ep1_bs256) | | Qwen3-14B_Math-CoT-20k_lr1e-5_ep1_bs256 | [Hugging Face](https://huggingface.co/jasonrqh/Qwen3-14B_Math-CoT-20k_lr1e-5_ep1_bs256) | [ModelScope](https://modelscope.cn/models/nebularaid/Qwen3-14B_Math-CoT-20k_lr1e-5_ep1_bs256) | | Qwen3-14B_Math-CoT-20k_lr1e-5_ep2_bs256 | [Hugging Face](https://huggingface.co/jasonrqh/Qwen3-14B_Math-CoT-20k_lr1e-5_ep2_bs256) | [ModelScope](https://modelscope.cn/models/nebularaid/Qwen3-14B_Math-CoT-20k_lr1e-5_ep2_bs256) | | **Apparent non-generalization can be an under-optimization artifact, with a dip-and-recovery pattern under extended training (refer to Sec. 3.1-3.2, Fig. 3)** | | | | Qwen3-14B_Math-CoT-20k_lr5e-5_ep8_bs256 | [Hugging Face](https://huggingface.co/jasonrqh/Qwen3-14B_Math-CoT-20k_lr5e-5_ep8_bs256) | [ModelScope](https://modelscope.cn/models/nebularaid/Qwen3-14B_Math-CoT-20k_lr5e-5_ep8_bs256) | | Qwen3-8B_Math-CoT-20k_lr5e-5_ep8_bs256 | [Hugging Face](https://huggingface.co/jasonrqh/Qwen3-8B_Math-CoT-20k_lr5e-5_ep8_bs256) | [ModelScope](https://modelscope.cn/models/nebularaid/Qwen3-8B_Math-CoT-20k_lr5e-5_ep8_bs256) | | InternLM2.5-20B_Math-CoT-20k_lr5e-5_ep8_bs256 | [Hugging Face](https://huggingface.co/jasonrqh/InternLM2.5-20B_Math-CoT-20k_lr5e-5_ep8_bs256) | [ModelScope](https://modelscope.cn/models/nebularaid/InternLM2.5-20B_Math-CoT-20k_lr5e-5_ep8_bs256) | | **The above optimization dynamics remain robust under a different teacher model (refer to App. C.2, Fig. 7)** | | | | Qwen3-14B_DeepSeek-R1-20k_lr5e-5_ep8_bs256 | [Hugging Face](https://huggingface.co/jasonrqh/Qwen3-14B_DeepSeek-R1-20k_lr5e-5_ep8_bs256) | [ModelScope](https://modelscope.cn/models/nebularaid/Qwen3-14B_DeepSeek-R1-20k_lr5e-5_ep8_bs256) | | Qwen3-8B_DeepSeek-R1-20k_lr5e-5_ep8_bs256 | [Hugging Face](https://huggingface.co/jasonrqh/Qwen3-8B_DeepSeek-R1-20k_lr5e-5_ep8_bs256) | [ModelScope](https://modelscope.cn/models/nebularaid/Qwen3-8B_DeepSeek-R1-20k_lr5e-5_ep8_bs256) | | InternLM2.5-20B_DeepSeek-R1-20k_lr5e-5_ep8_bs256 | [Hugging Face](https://huggingface.co/jasonrqh/InternLM2.5-20B_DeepSeek-R1-20k_lr5e-5_ep8_bs256) | [ModelScope](https://modelscope.cn/models/nebularaid/InternLM2.5-20B_DeepSeek-R1-20k_lr5e-5_ep8_bs256) | | **Under a fixed 640-step budget, repeated exposure is more effective than one-pass coverage (refer to Sec. 3.3, Table 1)** | | | | Qwen3-14B_Math-CoT-20k_lr5e-5_ep8_bs256 | [Hugging Face](https://huggingface.co/jasonrqh/Qwen3-14B_Math-CoT-20k_lr5e-5_ep8_bs256) | [ModelScope](https://modelscope.cn/models/nebularaid/Qwen3-14B_Math-CoT-20k_lr5e-5_ep8_bs256) | | Qwen3-14B_Math-CoT-2.5k_lr5e-5_ep8_bs32 | [Hugging Face](https://huggingface.co/jasonrqh/Qwen3-14B_Math-CoT-2.5k_lr5e-5_ep8_bs32) | [ModelScope](https://modelscope.cn/models/nebularaid/Qwen3-14B_Math-CoT-2.5k_lr5e-5_ep8_bs32) | | Qwen3-14B_Math-CoT-20k_lr5e-5_ep1_bs32 | [Hugging Face](https://huggingface.co/jasonrqh/Qwen3-14B_Math-CoT-20k_lr5e-5_ep1_bs32) | [ModelScope](https://modelscope.cn/models/nebularaid/Qwen3-14B_Math-CoT-20k_lr5e-5_ep1_bs32) | | **Overfitting symptoms emerge mainly under combined aggressive schedules (refer to Sec. 3.4, Fig. 4; App. C.4)** | | | | Qwen3-14B_Math-CoT-20k_lr5e-5_ep8_bs256 | [Hugging Face](https://huggingface.co/jasonrqh/Qwen3-14B_Math-CoT-20k_lr5e-5_ep8_bs256) | [ModelScope](https://modelscope.cn/models/nebularaid/Qwen3-14B_Math-CoT-20k_lr5e-5_ep8_bs256) | | Qwen3-14B_Math-CoT-20k_lr5e-5_ep16_bs256 | [Hugging Face](https://huggingface.co/jasonrqh/Qwen3-14B_Math-CoT-20k_lr5e-5_ep16_bs256) | [ModelScope](https://modelscope.cn/models/nebularaid/Qwen3-14B_Math-CoT-20k_lr5e-5_ep16_bs256) | | Qwen3-14B_Math-CoT-20k_lr5e-5_ep16_bs256_ConstLR | [Hugging Face](https://huggingface.co/jasonrqh/Qwen3-14B_Math-CoT-20k_lr5e-5_ep16_bs256_ConstLR) | [ModelScope](https://modelscope.cn/models/nebularaid/Qwen3-14B_Math-CoT-20k_lr5e-5_ep16_bs256_ConstLR) | | Qwen3-14B_Math-CoT-20k_lr1e-4_ep16_bs256_ConstLR | [Hugging Face](https://huggingface.co/jasonrqh/Qwen3-14B_Math-CoT-20k_lr1e-4_ep16_bs256_ConstLR) | [ModelScope](https://modelscope.cn/models/nebularaid/Qwen3-14B_Math-CoT-20k_lr1e-4_ep16_bs256_ConstLR) | | **Training data quality and structure jointly shape generalization (refer to Sec. 4, Table 2)** | | | | Qwen3-14B_Math-CoT-20k_lr5e-5_ep8_bs256 | [Hugging Face](https://huggingface.co/jasonrqh/Qwen3-14B_Math-CoT-20k_lr5e-5_ep8_bs256) | [ModelScope](https://modelscope.cn/models/nebularaid/Qwen3-14B_Math-CoT-20k_lr5e-5_ep8_bs256) | | Qwen3-14B_Math-NoCoT-20k_lr5e-5_ep8_bs256 | [Hugging Face](https://huggingface.co/jasonrqh/Qwen3-14B_Math-NoCoT-20k_lr5e-5_ep8_bs256) | [ModelScope](https://modelscope.cn/models/nebularaid/Qwen3-14B_Math-NoCoT-20k_lr5e-5_ep8_bs256) | | Qwen3-14B_Numina-Math-20k_lr5e-5_ep8_bs256 | [Hugging Face](https://huggingface.co/jasonrqh/Qwen3-14B_Numina-Math-20k_lr5e-5_ep8_bs256) | [ModelScope](https://modelscope.cn/models/nebularaid/Qwen3-14B_Numina-Math-20k_lr5e-5_ep8_bs256) | | Qwen3-14B_Countdown-CoT-20k_lr5e-5_ep8_bs256 | [Hugging Face](https://huggingface.co/jasonrqh/Qwen3-14B_Countdown-CoT-20k_lr5e-5_ep8_bs256) | [ModelScope](https://modelscope.cn/models/nebularaid/Qwen3-14B_Countdown-CoT-20k_lr5e-5_ep8_bs256) | | Qwen3-8B_Math-CoT-20k_lr5e-5_ep8_bs256 | [Hugging Face](https://huggingface.co/jasonrqh/Qwen3-8B_Math-CoT-20k_lr5e-5_ep8_bs256) | [ModelScope](https://modelscope.cn/models/nebularaid/Qwen3-8B_Math-CoT-20k_lr5e-5_ep8_bs256) | | Qwen3-8B_Math-NoCoT-20k_lr5e-5_ep8_bs256 | [Hugging Face](https://huggingface.co/jasonrqh/Qwen3-8B_Math-NoCoT-20k_lr5e-5_ep8_bs256) | [ModelScope](https://modelscope.cn/models/nebularaid/Qwen3-8B_Math-NoCoT-20k_lr5e-5_ep8_bs256) | | Qwen3-8B_Numina-Math-20k_lr5e-5_ep8_bs256 | [Hugging Face](https://huggingface.co/jasonrqh/Qwen3-8B_Numina-Math-20k_lr5e-5_ep8_bs256) | [ModelScope](https://modelscope.cn/models/nebularaid/Qwen3-8B_Numina-Math-20k_lr5e-5_ep8_bs256) | | Qwen3-8B_Countdown-CoT-20k_lr5e-5_ep8_bs256 | [Hugging Face](https://huggingface.co/jasonrqh/Qwen3-8B_Countdown-CoT-20k_lr5e-5_ep8_bs256) | [ModelScope](https://modelscope.cn/models/nebularaid/Qwen3-8B_Countdown-CoT-20k_lr5e-5_ep8_bs256) | | InternLM2.5-20B_Math-CoT-20k_lr5e-5_ep8_bs256 | [Hugging Face](https://huggingface.co/jasonrqh/InternLM2.5-20B_Math-CoT-20k_lr5e-5_ep8_bs256) | [ModelScope](https://modelscope.cn/models/nebularaid/InternLM2.5-20B_Math-CoT-20k_lr5e-5_ep8_bs256) | | InternLM2.5-20B_Math-NoCoT-20k_lr5e-5_ep8_bs256 | [Hugging Face](https://huggingface.co/jasonrqh/InternLM2.5-20B_Math-NoCoT-20k_lr5e-5_ep8_bs256) | [ModelScope](https://modelscope.cn/models/nebularaid/InternLM2.5-20B_Math-NoCoT-20k_lr5e-5_ep8_bs256) | | InternLM2.5-20B_Numina-Math-20k_lr5e-5_ep8_bs256 | [Hugging Face](https://huggingface.co/jasonrqh/InternLM2.5-20B_Numina-Math-20k_lr5e-5_ep8_bs256) | [ModelScope](https://modelscope.cn/models/nebularaid/InternLM2.5-20B_Numina-Math-20k_lr5e-5_ep8_bs256) | | InternLM2.5-20B_Countdown-CoT-20k_lr5e-5_ep8_bs256 | [Hugging Face](https://huggingface.co/jasonrqh/InternLM2.5-20B_Countdown-CoT-20k_lr5e-5_ep8_bs256) | [ModelScope](https://modelscope.cn/models/nebularaid/InternLM2.5-20B_Countdown-CoT-20k_lr5e-5_ep8_bs256) | | **Higher-capability models internalize transferable reasoning patterns more effectively and generalize better (refer to Sec. 5, Fig. 5)** | | | | Qwen3-1.7B_Math-CoT-20k_lr5e-5_ep8_bs256 | [Hugging Face](https://huggingface.co/jasonrqh/Qwen3-1.7B_Math-CoT-20k_lr5e-5_ep8_bs256) | [ModelScope](https://modelscope.cn/models/nebularaid/Qwen3-1.7B_Math-CoT-20k_lr5e-5_ep8_bs256) | | Qwen3-4B_Math-CoT-20k_lr5e-5_ep8_bs256 | [Hugging Face](https://huggingface.co/jasonrqh/Qwen3-4B_Math-CoT-20k_lr5e-5_ep8_bs256) | [ModelScope](https://modelscope.cn/models/nebularaid/Qwen3-4B_Math-CoT-20k_lr5e-5_ep8_bs256) | | Qwen3-8B_Math-CoT-20k_lr5e-5_ep8_bs256 | [Hugging Face](https://huggingface.co/jasonrqh/Qwen3-8B_Math-CoT-20k_lr5e-5_ep8_bs256) | [ModelScope](https://modelscope.cn/models/nebularaid/Qwen3-8B_Math-CoT-20k_lr5e-5_ep8_bs256) | | Qwen3-14B_Math-CoT-20k_lr5e-5_ep8_bs256 | [Hugging Face](https://huggingface.co/jasonrqh/Qwen3-14B_Math-CoT-20k_lr5e-5_ep8_bs256) | [ModelScope](https://modelscope.cn/models/nebularaid/Qwen3-14B_Math-CoT-20k_lr5e-5_ep8_bs256) | | **The capability-dependent trend extends to another model family (refer to App. C.2/C.5, Fig. 8/14/15)** | | | | Qwen2.5-1.5B_Math-CoT-20k_lr5e-5_ep8_bs256 | [Hugging Face](https://huggingface.co/jasonrqh/Qwen2.5-1.5B_Math-CoT-20k_lr5e-5_ep8_bs256) | [ModelScope](https://modelscope.cn/models/nebularaid/Qwen2.5-1.5B_Math-CoT-20k_lr5e-5_ep8_bs256) | | Qwen2.5-3B_Math-CoT-20k_lr5e-5_ep8_bs256 | [Hugging Face](https://huggingface.co/jasonrqh/Qwen2.5-3B_Math-CoT-20k_lr5e-5_ep8_bs256) | [ModelScope](https://modelscope.cn/models/nebularaid/Qwen2.5-3B_Math-CoT-20k_lr5e-5_ep8_bs256) | | Qwen2.5-7B_Math-CoT-20k_lr5e-5_ep8_bs256 | [Hugging Face](https://huggingface.co/jasonrqh/Qwen2.5-7B_Math-CoT-20k_lr5e-5_ep8_bs256) | [ModelScope](https://modelscope.cn/models/nebularaid/Qwen2.5-7B_Math-CoT-20k_lr5e-5_ep8_bs256) | | Qwen2.5-14B_Math-CoT-20k_lr5e-5_ep8_bs256 | [Hugging Face](https://huggingface.co/jasonrqh/Qwen2.5-14B_Math-CoT-20k_lr5e-5_ep8_bs256) | [ModelScope](https://modelscope.cn/models/nebularaid/Qwen2.5-14B_Math-CoT-20k_lr5e-5_ep8_bs256) | | **Asymmetric generalization: reasoning improves while safety degrades under long-CoT SFT (refer to Sec. 6, Fig. 6)** | | | | Qwen3-14B_Math-CoT-20k_lr5e-5_ep8_bs256 | [Hugging Face](https://huggingface.co/jasonrqh/Qwen3-14B_Math-CoT-20k_lr5e-5_ep8_bs256) | [ModelScope](https://modelscope.cn/models/nebularaid/Qwen3-14B_Math-CoT-20k_lr5e-5_ep8_bs256) | | Qwen3-14B_Math-NoCoT-20k_lr5e-5_ep8_bs256 | [Hugging Face](https://huggingface.co/jasonrqh/Qwen3-14B_Math-NoCoT-20k_lr5e-5_ep8_bs256) | [ModelScope](https://modelscope.cn/models/nebularaid/Qwen3-14B_Math-NoCoT-20k_lr5e-5_ep8_bs256) | | Qwen3-8B_Math-CoT-20k_lr5e-5_ep8_bs256 | [Hugging Face](https://huggingface.co/jasonrqh/Qwen3-8B_Math-CoT-20k_lr5e-5_ep8_bs256) | [ModelScope](https://modelscope.cn/models/nebularaid/Qwen3-8B_Math-CoT-20k_lr5e-5_ep8_bs256) | | Qwen3-8B_Math-NoCoT-20k_lr5e-5_ep8_bs256 | [Hugging Face](https://huggingface.co/jasonrqh/Qwen3-8B_Math-NoCoT-20k_lr5e-5_ep8_bs256) | [ModelScope](https://modelscope.cn/models/nebularaid/Qwen3-8B_Math-NoCoT-20k_lr5e-5_ep8_bs256) | | InternLM2.5-20B_Math-CoT-20k_lr5e-5_ep8_bs256 | [Hugging Face](https://huggingface.co/jasonrqh/InternLM2.5-20B_Math-CoT-20k_lr5e-5_ep8_bs256) | [ModelScope](https://modelscope.cn/models/nebularaid/InternLM2.5-20B_Math-CoT-20k_lr5e-5_ep8_bs256) | | InternLM2.5-20B_Math-NoCoT-20k_lr5e-5_ep8_bs256 | [Hugging Face](https://huggingface.co/jasonrqh/InternLM2.5-20B_Math-NoCoT-20k_lr5e-5_ep8_bs256) | [ModelScope](https://modelscope.cn/models/nebularaid/InternLM2.5-20B_Math-NoCoT-20k_lr5e-5_ep8_bs256) | | **Appendix: smaller and mid-scale models across data configurations (refer to App. D)** | | | | Qwen3-1.7B_Countdown-CoT-20k_lr5e-5_ep8_bs256 | [Hugging Face](https://huggingface.co/jasonrqh/Qwen3-1.7B_Countdown-CoT-20k_lr5e-5_ep8_bs256) | [ModelScope](https://modelscope.cn/models/nebularaid/Qwen3-1.7B_Countdown-CoT-20k_lr5e-5_ep8_bs256) | | Qwen3-1.7B_Math-NoCoT-20k_lr5e-5_ep8_bs256 | [Hugging Face](https://huggingface.co/jasonrqh/Qwen3-1.7B_Math-NoCoT-20k_lr5e-5_ep8_bs256) | [ModelScope](https://modelscope.cn/models/nebularaid/Qwen3-1.7B_Math-NoCoT-20k_lr5e-5_ep8_bs256) | | Qwen3-4B_Countdown-CoT-20k_lr5e-5_ep8_bs256 | [Hugging Face](https://huggingface.co/jasonrqh/Qwen3-4B_Countdown-CoT-20k_lr5e-5_ep8_bs256) | [ModelScope](https://modelscope.cn/models/nebularaid/Qwen3-4B_Countdown-CoT-20k_lr5e-5_ep8_bs256) | | Qwen3-4B_Math-NoCoT-20k_lr5e-5_ep8_bs256 | [Hugging Face](https://huggingface.co/jasonrqh/Qwen3-4B_Math-NoCoT-20k_lr5e-5_ep8_bs256) | [ModelScope](https://modelscope.cn/models/nebularaid/Qwen3-4B_Math-NoCoT-20k_lr5e-5_ep8_bs256) | ## Overview of Open-source Datasets We provide the main datasets used in our experiments. | Dataset Name | Description | Size | Hugging Face | ModelScope | | --- | --- | --- | --- | --- | | Math-CoT-20k | Verified long-CoT math reasoning data (default setting in the paper) | 20,480 | [Hugging Face](https://huggingface.co/datasets/jasonrqh/Math-CoT-20k) | [ModelScope](https://modelscope.cn/datasets/nebularaid/Math-CoT-20k) | | Math-NoCoT-20k | Math-CoT-20k with CoT traces removed (final summary/answer retained) | 20,480 | [Hugging Face](https://huggingface.co/datasets/jasonrqh/Math-NoCoT-20k) | [ModelScope](https://modelscope.cn/datasets/nebularaid/Math-NoCoT-20k) | | Countdown-CoT-20k | Countdown arithmetic-game long-CoT data for procedural transfer analysis | 20,480 | [Hugging Face](https://huggingface.co/datasets/jasonrqh/Countdown-CoT-20k) | [ModelScope](https://modelscope.cn/datasets/nebularaid/Countdown-CoT-20k) | | NuminaMath-20k | No-CoT math data with the matched queries, sourced from NuminaMath-1.5 | 20,480 | [Hugging Face](https://huggingface.co/datasets/jasonrqh/NuminaMath-20k) | [ModelScope](https://modelscope.cn/datasets/nebularaid/NuminaMath-20k) | | DeepSeek-R1-20k | Verified long-CoT responses from DeepSeek-R1 on the same queries, sourced from the LUFFY dataset | 20,480 | [Hugging Face](https://huggingface.co/datasets/jasonrqh/DeepSeek-R1-20k) | [ModelScope](https://modelscope.cn/datasets/nebularaid/DeepSeek-R1-20k) | ## Citation ```bibtex @article{ren2026rethinking_sft_generalization, title={Rethinking Generalization in Reasoning SFT: A Conditional Analysis on Optimization, Data, and Model Capability}, author={Qihan Ren and Peng Wang and Ruikun Cai and Shuai Shao and Dadi Guo and Yuejin Xie and Yafu Li and Quanshi Zhang and Xia Hu and Jing Shao and Dongrui Liu}, journal={arXiv preprint arXiv:2604.06628}, year={2026} } ```语言： - "英语" 许可证："MIT许可证" 标签： - "推理" - "监督微调(SFT)" - "思维链(CoT)" # 重新思考推理监督微调中的泛化能力 本仓库包含与论文《重新思考推理监督微调中的泛化能力：关于优化、数据与模型能力的条件性分析》相关的数据集与开源模型。 本研究针对大语言模型(LLM)在推理任务中采用长思维链数据进行监督微调(SFT)时的跨域泛化能力展开分析，针对优化、数据与模型能力三个维度进行条件性分析。 ## 核心发现 - **优化动力学**：跨域性能通常呈现先降后升的轨迹。模型往往需要更长时间的训练才能达到最优泛化性能。 - **数据质量与结构**：经验证的长思维链数据能够带来稳定的跨域性能提升，而低质量的推理结果或无思维链(No-CoT)数据则可能引入误导性信号，或导致泛化表现不佳。 - **模型能力**：相较于参数规模较小的基础模型，能力更强的基础模型更擅长内化可迁移的程序性推理模式（例如回溯推理）。 - **非对称泛化**：研究发现，尽管通过长思维链监督微调能够提升推理能力，但模型的安全性可能同时出现下降。与之相对，无思维链数据虽然带来的推理提升有限，却能带来更优的安全性表现。 ## 资源 - **论文**：[arXiv:2604.06628](https://huggingface.co/papers/2604.06628) - **代码**：[官方GitHub仓库](https://github.com/Nebularaid2000/rethink_sft_generalization) - **模型集合**：[Hugging Face 模型集合](https://huggingface.co/collections/jasonrqh/rethink-sft-generalization) ## 开源模型列表 以下为实验中训练的所有模型，包括中间检查点与最终模型： | 模型名称 | Hugging Face 链接 | ModelScope 链接 | | --- | --- | --- | | Qwen3-14B_Math-CoT-20k_lr5e-5_ep1_bs256 | [Hugging Face](https://huggingface.co/jasonrqh/Qwen3-14B_Math-CoT-20k_lr5e-5_ep1_bs256) | [ModelScope](https://modelscope.cn/models/nebularaid/Qwen3-14B_Math-CoT-20k_lr5e-5_ep1_bs256) | ...（其余模型列表省略） ## 开源数据集列表 以下为实验中使用的主要数据集： | 数据集名称 | 描述 | 规模 | Hugging Face 链接 | ModelScope 链接 | | --- | --- | --- | --- | --- | | Math-CoT-20k | 经验证的长思维链数学推理数据，为论文中的默认设置 | 20,480 | [Hugging Face](https://huggingface.co/datasets/jasonrqh/Math-CoT-20k) | [ModelScope](https://modelscope.cn/datasets/nebularaid/Math-CoT-20k) | | Math-NoCoT-20k | 移除了思维链轨迹的Math-CoT-20k数据，保留最终总结与答案 | 20,480 | [Hugging Face](https://huggingface.co/datasets/jasonrqh/Math-NoCoT-20k) | [ModelScope](https://modelscope.cn/datasets/nebularaid/Math-NoCoT-20k) | | Countdown-CoT-20k | 倒计时算术游戏的长思维链数据 | 20,480 | [Hugging Face](https://huggingface.co/datasets/jasonrqh/Countdown-CoT-20k) | [ModelScope](https://modelscope.cn/datasets/nebularaid/Countdown-CoT-20k) | | NuminaMath-20k | 源自NuminaMath-1.5的无思维链数学数据 | 20,480 | [Hugging Face](https://huggingface.co/datasets/jasonrqh/NuminaMath-20k) | [ModelScope](https://modelscope.cn/datasets/nebularaid/NuminaMath-20k) | | DeepSeek-R1-20k | 源自LUFFY数据集的DeepSeek-R1生成的长思维链数据 | 20,480 | [Hugging Face](https://huggingface.co/datasets/jasonrqh/DeepSeek-R1-20k) | [ModelScope](https://modelscope.cn/datasets/nebularaid/DeepSeek-R1-20k) | ## 引用 bibtex @article{ren2026rethinking, title={Rethinking Generalization in Reasoning SFT: A Conditional Analysis on Optimization, Data, and Model Capability}, author={Qihan Ren, Peng Wang, Ruikun Cai, Shuai Shao, Dadi Guo, Yuejin Xie, Yafu Li, Quanshi Zhang, Xia Hu, Jing Shao, Dongrui Liu}, journal={arXiv preprint arXiv:2604.06628}, year={2026} }

搜集汇总

数据集介绍

构建方式

在推理导向的监督微调研究领域，DeepSeek-R1-20k数据集作为一项关键资源应运而生。该数据集构建于LUFFY数据集的基础之上，其核心内容源自DeepSeek-R1模型对相同查询所生成的、经过验证的长链思维推理轨迹。研究团队精心筛选了20,480条高质量样本，每条样本均包含完整的、可追溯的推理步骤，确保了数据在逻辑连贯性与答案准确性上的严谨性。这种构建方式旨在为探索大语言模型在跨领域泛化中的内在机制，提供一套标准化且可靠的长链思维数据基准。

特点

该数据集在推理数据集中展现出鲜明的结构性特征。其核心在于提供了由先进推理模型DeepSeek-R1产生的、经过人工或自动化验证的长链思维过程，这为研究模型如何内化复杂的程序性推理模式（如回溯、多步推导）提供了直接素材。与无思维链或低质量解答的数据相比，本数据集蕴含的已验证推理轨迹被证实能带来更一致的跨领域性能增益。同时，它作为对照数据源，与Math-CoT-20k等数据集并列，使得研究者能够系统剖析不同教师模型或数据质量对泛化能力的影响，揭示了数据质量与结构共同塑造模型泛化性能的深层规律。

使用方法

该数据集主要用于大语言模型在推理任务上的监督微调及其泛化能力的研究。使用者可将其直接加载至Hugging Face或ModelScope平台，作为训练数据对基座模型进行微调，以探究长链思维数据对模型跨领域推理能力的影响。在实际应用中，研究者常将其与Math-NoCoT-20k、NuminaMath-20k等不同结构的数据集进行对比实验，以分离数据质量、思维链存在与否等因素的效应。相关论文中的开源模型集合提供了丰富的参照，用户可复现或基于这些模型检查点，深入分析优化动态、模型能力与数据配置之间的复杂交互关系，从而深化对推理泛化机制的理解。

背景与挑战

背景概述

DeepSeek-R1-20k数据集源于2026年发表的论文《Rethinking Generalization in Reasoning SFT》，由任启涵等研究人员构建，旨在探究大语言模型在推理导向的监督微调中的跨领域泛化机制。该数据集包含两万余条经过验证的长链思维推理轨迹，源自DeepSeek-R1模型对数学问题的解答，核心研究聚焦于优化动态、数据质量与模型能力如何共同塑造可迁移的程序性推理模式。其创建为深入理解复杂推理任务的泛化边界提供了关键实证基础，推动了推理微调领域从经验性实践向理论化分析的范式转变。

当前挑战

该数据集致力于解决大语言模型在复杂推理任务上的跨领域泛化挑战，其核心难题在于如何平衡推理能力的提升与安全性的保持，即长链思维数据可能导致模型在未知领域产生非预期行为。在构建过程中，挑战主要体现在确保思维链轨迹的高质量与一致性，需对原始响应进行严格验证以剔除错误或低效的推理路径。同时，数据规模的扩展与不同模型家族响应的对齐亦构成显著障碍，要求构建者精确控制变量以分离数据、优化与模型能力对泛化性能的独立影响。

常用场景

经典使用场景

在大型语言模型推理能力微调的研究领域，DeepSeek-R1-20k数据集作为高质量长链思维轨迹的典型代表，常被用于探究监督微调过程中的跨领域泛化机制。该数据集包含由DeepSeek-R1模型生成的经过验证的详细推理步骤，为研究者提供了分析模型如何从特定领域数据中学习并迁移结构化推理模式至未见领域的实验基础。其经典应用场景在于系统评估不同优化策略、数据质量及模型能力对泛化性能的交互影响，为理解复杂推理任务的迁移学习本质提供了关键数据支撑。

衍生相关工作

围绕该数据集衍生的经典工作主要集中于泛化机制的深入探索与模型能力的系统性评估。相关研究扩展了原始论文的发现，例如探究不同教师模型生成轨迹的泛化等效性，或分析小规模模型在有限数据下的模式内化瓶颈。这些工作进一步验证了数据质量、模型规模与训练动态之间的复杂耦合关系，并催生了针对“不对称泛化”现象的专项研究，即在提升推理能力时如何缓解安全性能的退化。这些衍生成果共同构成了当前推理微调领域的方法论基石，持续推动着更高效、更安全的AI训练范式发展。

数据集最近研究

最新研究方向

在大型语言模型推理能力微调领域，DeepSeek-R1-20k数据集作为高质量长链思维数据，正推动对跨领域泛化机制的深入研究。前沿工作聚焦于优化动态、数据质量与模型能力之间的复杂交互，揭示了泛化过程中存在的“先降后升”轨迹及能力依赖现象。尤为关键的是，研究发现推理能力的提升可能伴随安全性的非对称退化，这一矛盾引发了关于如何平衡模型性能与安全属性的广泛探讨。该数据集支撑的实证分析为设计更鲁棒、可泛化的推理微调策略提供了重要依据，标志着该领域从单纯追求性能提升转向对泛化本质的系统性解构。