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Main Authors: K2 Team, Liu, Zhengzhong, Tang, Liping, Jin, Linghao, Li, Haonan, Ranjan, Nikhil, Fan, Desai, Rohatgi, Shaurya, Fan, Richard, Pangarkar, Omkar, Wang, Huijuan, Cheng, Zhoujun, Sun, Suqi, Han, Seungwook, Tan, Bowen, Gosal, Gurpreet, Han, Xudong, Pimpalkhute, Varad, Hao, Shibo, Hee, Ming Shan, Hestness, Joel, Jia, Haolong, Ma, Liqun, Singh, Aaryamonvikram, Soboleva, Daria, Vassilieva, Natalia, Wang, Renxi, Wu, Yingquan, Sun, Yuekai, Killian, Taylor, Moreno, Alexander, Maggs, John, Ren, Hector, He, Guowei, Wang, Hongyi, Ma, Xuezhe, Wang, Yuqi, Yurochkin, Mikhail, Xing, Eric P.
Format: Preprint
Published: 2025
Subjects:
Machine Learning
Online Access:https://arxiv.org/abs/2512.06201
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Table of Contents:
  • 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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