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
Published: 2025-12-31 04:25:11

Abstract: 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.