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K2-V2: A 360-Open, Reasoning-Enhanced LLM

K2 Team
Zhengzhong Liu
Liping Tang
Linghao Jin
Haonan Li
Nikhil Ranjan
Desai Fan
Shaurya Rohatgi
Richard Fan
Omkar Pangarkar
Huijuan Wang
Zhoujun Cheng
Suqi Sun
Seungwook Han
Bowen Tan
Gurpreet Gosal
Xudong Han
Varad Pimpalkhute
Shibo Hao
Ming Shan Hee
Joel Hestness
Haolong Jia
Liqun Ma
Aaryamonvikram Singh
Daria Soboleva
Natalia Vassilieva
Renxi Wang
Yingquan Wu
Yuekai Sun
Taylor Killian
Alexander Moreno
John Maggs
Hector Ren
Guowei He
Hongyi Wang
Xuezhe Ma
Yuqi Wang
Mikhail Yurochkin
Eric P. Xing
Main:72 Pages
38 Figures
Bibliography:2 Pages
12 Tables
Appendix:1 Pages
Abstract

We introduce K2-V2, a 360-open LLM built from scratch as a superior base for reasoning adaptation, in addition to functions such as conversation and knowledge retrieval from general LLMs. It stands as the strongest fully open model, rivals open-weight leaders in its size class, outperforms Qwen2.5-72B and approaches the performance of Qwen3-235B. We actively infuse domain knowledge, reasoning, long-context, and tool use throughout the training process. This explicitly prepares the model for complex reasoning tasks. We demonstrate this potential using simple supervised fine-tuning, establishing a strong baseline that indicates significant headroom for advanced alignment. By releasing the full training history and data composition, we maximize the effectiveness of continuous training, a key open source production scenario. We release the model weights and signature LLM360 artifacts, such as complete training data, to empower the community with a capable, reasoning-centric foundation.

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