[ PAPER ] · 2025 · arXiv.org
Kimi k1.5: Scaling Reinforcement Learning with LLMs
Kimi Team, Angang Du, Bofei Gao, Bowei Xing, Changjiu Jiang, Cheng Chen, Cheng Li, Chenjun Xiao, Chenzhuang Du, Chonghua Liao, Chuning Tang, Congcong Wang, Dehao Zhang, Enming Yuan, Enzhe Lu, Feng Tang, Flood Sung, Guangda Wei, Guokun Lai, Haiqing Guo, Han Zhu, Haochen Ding, Hao-Xing Hu, Haoming Yang, Hao Zhang, Haotian Yao, Hao-Dong Zhao, Haoyu Lu, Haoze Li, Hao Yu, Hongcheng Gao, Huabin Zheng, Huan Yuan, Jia Chen, Jianhang Guo, Jianling Su, Jianzhou Wang, Jie Zhao, Jin Zhang, Jingyuan Liu, Junjie Yan, Junyan Wu, Li-Na Shi, Li-tao Ye, Long Yu, Meng-xiao Dong, Neo Y. Zhang, Ning Ma, Qi Pan, Qucheng Gong, Shaowei Liu, Shen Ma, Shu-Yan Wei, S. Cao, Si-Da Huang, Tao Jiang, Wei-Wei Gao, Weiming Xiong, Weiran He, Weixiao Huang, Wenhao Wu, Wen He, Xian-sen Wei, Xian-Xian Jia, Xingzhe Wu, Xinran Xu, Xinxing Zu, Xinyu Zhou, Xue-biao Pan, Y. Charles, Yang Li, Yan-Ling Hu, Yangyang Liu, Yanru Chen, Ye-Jia Wang, Yibo Liu, Yidao Qin, Yifeng Liu, Yingbo Yang, Yiping Bao, Yulun Du, Yuxin Wu, Yuzhi Wang, Zaida Zhou, Zhaoji Wang, Zhaowei Li, Zhengxin Zhu, Zheng Zhang, Zhexu Wang, Zhilin Yang, Zhiqi Huang, Zihao Huang, Ziya Xu, Zonghan Yang
[ TLDR ]
This work reports on the training practice of Kimi k1.5, the latest multi-modal LLM trained with RL, including its RL training techniques, multi-modal data recipes, and infrastructure optimization, which establishes a simplistic, effective RL framework without relying on more complex techniques such as Monte Carlo tree search, value functions, and process reward models.
[ ABSTRACT ]
Language model pretraining with next token prediction has proved effective for scaling compute but is limited to the amount of available training data. Scaling reinforcement learning (RL) unlocks a new axis for the continued improvement of artificial intelligence, with the promise that large language models (LLMs) can scale their training data by learning to explore with rewards. However, prior published work has not produced competitive results. In light of this, we report on the training practice of Kimi k1.5, our latest multi-modal LLM trained with RL, including its RL training techniques, multi-modal data recipes, and infrastructure optimization. Long context scaling and improved policy optimization methods are key ingredients of our approach, which establishes a simplistic, effective RL framework without relying on more complex techniques such as Monte Carlo tree search, value functions, and process reward models. Notably, our system achieves state-of-the-art reasoning performance across multiple benchmarks and modalities -- e.g., 77.5 on AIME, 96.2 on MATH 500, 94-th percentile on Codeforces, 74.9 on MathVista -- matching OpenAI's o1. Moreover, we present effective long2short methods that use long-CoT techniques to improve short-CoT models, yielding state-of-the-art short-CoT reasoning results -- e.g., 60.8 on AIME, 94.6 on MATH500, 47.3 on LiveCodeBench -- outperforming existing short-CoT models such as GPT-4o and Claude Sonnet 3.5 by a large margin (up to +550%).