Youtu-LLM: Unlocking the Native Agentic Potential for Lightweight Large Language Models
Authors: name: Junru Lu, name: Jiarui Qin, name: Lingfeng Qiao, name: Yinghui Li, name: Xinyi Dai, name: Bo Ke, name: Jianfeng He, name: Ruizhi Qiao, name: Di Yin, name: Xing Sun, name: Yunsheng Wu, name: Yinsong Liu, name: Shuangyin Liu, name: Mingkong Tang, name: Haodong Lin, name: Jiayi Kuang, name: Fanxu Meng, name: Xiaojuan Tang, name: Yunjia Xi, name: Junjie Huang, name: Haotong Yang, name: Zhenyi Shen, name: Yangning Li, name: Qianwen Zhang, name: Yifei Yu, name: Siyu An, name: Junnan Dong, name: Qiufeng Wang, name: Jie Wang, name: Keyu Chen, name: Wei Wen, name: Taian Guo, name: Zhifeng Shen, name: Daohai Yu, name: Jiahao Li, name: Ke Li, name: Zongyi Li, name: Xiaoyu Tan
Organization: Tencent

We introduce Youtu-LLM, a lightweight yet powerful language model that harmonizes high computational efficiency with native agentic intelligence. Unlike typical small models that rely on distillation, Youtu-LLM (1.96B) is pre-trained from scratch to systematically cultivate reasoning and planning capabilities. The key technical advancements are as follows: (1) Compact Architecture with Long-Context Support: Built on a dense Multi-Latent Attention (MLA) architecture with a novel STEM-oriented vocabulary, Youtu-LLM supports a 128k context window. This design enables robust long-context reasoning and state tracking within a minimal memory footprint, making it ideal for long-horizon agent and reasoning tasks. (2) Principled "Commonsense-STEM-Agent" Curriculum: We curated a massive corpus of approximately 11T tokens and implemented a multi-stage training strategy. By progressively shifting the pre-training data distribution from general commonsense to complex STEM and agentic tasks, we ensure the model acquires deep cognitive abilities rather than superficial alignment. (3) Scalable Agentic Mid-training: Specifically for the agentic mid-training, we employ diverse data construction schemes to synthesize rich and varied trajectories across math, coding, and tool-use domains. This high-quality data enables the model to internalize planning and reflection behaviors effectively. Extensive evaluations show that Youtu-LLM sets a new state-of-the-art for sub-2B LLMs. On general benchmarks, it achieves competitive performance against larger models, while on agent-specific tasks, it significantly surpasses existing SOTA baselines, demonstrating that lightweight models can possess strong intrinsic agentic capabilities.