jasonrqh/NuminaMath-20k
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下载链接：
https://hf-mirror.com/datasets/jasonrqh/NuminaMath-20k
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资源简介：
---
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}
}
```
提供机构：
jasonrqh
搜集汇总
数据集介绍
构建方式
在数学推理领域，NuminaMath-20k数据集作为一项关键资源，其构建过程体现了对数据源严谨性的追求。该数据集源自NuminaMath-1.5，通过精心的匹配与筛选，汇集了总计20,480条数学问题实例。其核心特征在于移除了传统的思维链（CoT）推理步骤，仅保留了与问题直接对应的查询语句及最终答案，从而形成了一种无推理过程的纯问答数据格式。这种构建方式旨在探究在监督微调中，不含显式推理路径的数据对模型泛化能力的影响，为理解数据结构与模型性能的关系提供了基础。
特点
NuminaMath-20k的显著特点在于其纯粹的问题-答案配对结构，完全剥离了思维链的中间推理过程。这种设计使得数据集在数学推理任务中呈现出一种简洁而直接的形态，与包含长链推理的数据集形成鲜明对比。数据集规模达到两万余条，确保了足够的样本覆盖度，能够支持大规模的语言模型训练实验。其无CoT的特性使其成为研究数据质量与模型泛化之间关系的理想对照样本，尤其在分析推理能力提升与安全性权衡的复杂动态时，提供了不可或缺的视角。
使用方法
该数据集主要用于大型语言模型在数学推理任务上的监督微调研究，特别是作为对比实验的关键组成部分。研究人员可以将其与包含长思维链的数学数据集（如Math-CoT-20k）结合使用，以系统评估不同数据格式对模型跨领域泛化能力的影响。在实际应用中，数据集可直接通过Hugging Face或ModelScope平台加载，并按照标准的指令微调流程进行模型训练。通过调整学习率、训练轮数等超参数，可以深入探究无CoT数据在优化动态、模型能力内化以及安全性与推理性能不对称泛化等方面的具体作用。
背景与挑战
背景概述
在大型语言模型推理能力微调的研究浪潮中，NuminaMath-20k数据集应运而生，作为论文《Rethinking Generalization in Reasoning SFT: A Conditional Analysis on Optimization, Data, and Model Capability》的关键组成部分。该数据集由研究团队于2026年构建，旨在探究监督微调过程中数据质量与结构对模型跨领域泛化能力的影响。其核心研究问题聚焦于，当模型在数学推理任务上进行微调时，不含思维链的纯答案数据如何塑造其泛化行为。这一工作深化了社区对数据模态与模型能力交互关系的理解，为优化推理微调策略提供了实证基础。
当前挑战
NuminaMath-20k数据集所应对的领域挑战，在于揭示不含思维链的数学数据在监督微调中对模型泛化能力的塑造机制。具体而言，研究需厘清此类数据是促进稳健的跨领域推理迁移，还是仅诱导模型记忆表面模式。在构建层面，挑战源于从NuminaMath-1.5等源数据中精确匹配查询并确保答案的高质量验证，同时需维持与其他对比数据集在规模与主题上的一致性，以支撑严谨的对照实验。
常用场景
经典使用场景
在数学推理领域，NuminaMath-20k数据集作为一项关键资源，其经典应用场景在于探究监督微调过程中数据质量与结构对大型语言模型跨域泛化能力的影响。该数据集源自NuminaMath-1.5，包含两万余条无思维链的数学问题与答案配对，为研究者提供了一个纯净的基准，用以对比分析思维链数据与无思维链数据在模型训练中的差异化效应。通过系统性地比较不同数据配置下的模型表现，该数据集能够揭示数据内部结构如何塑造模型的推理模式习得过程，进而深化对监督微调泛化机制的理解。
衍生相关工作
围绕NuminaMath-20k数据集，衍生出了一系列深入探究监督微调泛化机制的经典研究工作。其关联论文《Rethinking Generalization in Reasoning SFT》系统性地分析了优化动态、数据质量与模型能力之间的条件性关系，并开源了涵盖多种数据配置（如Math-CoT-20k、Math-NoCoT-20k）与模型架构（如Qwen、InternLM系列）的完整实验模型集合。这些工作共同构建了一个多维度的分析框架，不仅验证了‘先降后升’的优化轨迹、模型能力依赖的泛化趋势等核心发现，还进一步探讨了推理提升与安全退化之间的不对称泛化现象，推动了整个领域对微调泛化本质的重新思考。
数据集最近研究
最新研究方向
在数学推理领域，大型语言模型的跨域泛化能力已成为当前研究焦点。NuminaMath-20k数据集作为无思维链（No-CoT）数学数据的重要代表，其最新研究揭示了监督微调中优化动态、数据质量与模型能力间的复杂交互关系。前沿探索聚焦于长思维链数据引发的“先降后升”泛化轨迹，以及模型在吸收可迁移推理模式时表现出的能力依赖性。值得注意的是，研究发现推理能力提升与安全性下降之间存在不对称泛化现象，这为平衡模型性能与安全对齐提供了关键实证依据。该方向的研究正推动着对数据构造策略、训练调度机制及模型架构设计的系统性反思，对构建稳健可靠的数学推理系统具有深远意义。
以上内容由遇见数据集搜集并总结生成
