TxT360-3efforts 是由 Mohamed bin Zayed University of Artificial Intelligence 于 2025 年发布的一个用于监督微调（SFT）的超大规模语言模型训练数据集，相关论文成果为 K2-V2: A 360-Open, Reasoning-Enhanced LLM，旨在通过聊天模板控制模型的三种推理强度。 该数据集规模约为 1,000 万条样本、 100 亿训练 token，覆盖数学、代码、通用对话、 STEM 推理、指令遵循、工具调用、智能体轨迹、自我身份建模和安全对齐等九类任务，包含大量多轮对话及具备可验证约束的样本。数据来源于开源许可的公共数据集或高质量合成数据，并经过严格的质量过滤、去重及评测基准去污染处理，答案主要由 GPT-OSS-120B 在不同推理强度下生成。数据集通过统一的聊天模板显式区分低、中、高三种推理强度，使模型在训练阶段即可学习在不同推理需求下调整生成长度与推理深度。

Citation

@misc{k2team2025k2v2360openreasoningenhancedllm, title={K2-V2: A 360-Open, Reasoning-Enhanced LLM}, author={K2 Team and Zhengzhong Liu and Liping Tang and Linghao Jin and Haonan Li and Nikhil Ranjan and Desai Fan and Shaurya Rohatgi and Richard Fan and Omkar Pangarkar and Huijuan Wang and Zhoujun Cheng and Suqi Sun and Seungwook Han and Bowen Tan and Gurpreet Gosal and Xudong Han and Varad Pimpalkhute and Shibo Hao and Ming Shan Hee and Joel Hestness and Haolong Jia and Liqun Ma and Aaryamonvikram Singh and Daria Soboleva and Natalia Vassilieva and Renxi Wang and Yingquan Wu and Yuekai Sun and Taylor Killian and Alexander Moreno and John Maggs and Hector Ren and Guowei He and Hongyi Wang and Xuezhe Ma and Yuqi Wang and Mikhail Yurochkin and Eric P. Xing}, year={2025}, eprint={2512.06201}, archivePrefix={arXiv}, primaryClass={cs.LG}, url={https://arxiv.org/abs/2512.06201}, }