Technical Report of TeleChat2, TeleChat2.5 and T1
Authors: name: Zihan Wang; name: Xinzhang Liu; name: Yitong Yao; name: Chao Wang; name: Yu Zhao; name: Zhihao Yang; name: Wenmin Deng; name: Kaipeng Jia; name: Jiaxin Peng; name: Yuyao Huang; name: Sishi Xiong; name: Zhuo Jiang; name: Kaidong Yu; name: Xiaohui Hu; name: Fubei Yao; name: Ruiyu Fang; name: Zhuoru Jiang; name: Ruiting Song; name: Qiyi Xie; name: Rui Xue; name: Xuewei He; name: Yanlei Xue; name: Zhu Yuan; name: Zhaoxi Zhang; name: Zilu Huang; name: Shiquan Wang; name: Xin Wang; name: Hanming Wu; name: Mingyuan Wang; name: Xufeng Zhan; name: Yuhan Sun; name: Zhaohu Xing; name: Yuhao Jiang; name: Bingkai Yang; name: Shuangyong Song; name: Yongxiang Li; name: Zhongjiang He; name: Xuelong Li
Published: 2025-07-24 01:00:48

Abstract: We introduce the latest series of TeleChat models: TeleChat2,
TeleChat2.5, and T1, offering a significant upgrade over
their predecessor, TeleChat. Despite minimal changes to the model architecture,
the new series achieves substantial performance gains through enhanced training
strategies in both pre-training and post-training stages. The series begins
with TeleChat2, which undergoes pretraining on 10 trillion
high-quality and diverse tokens. This is followed by Supervised Fine-Tuning
(SFT) and Direct Preference Optimization (DPO) to further enhance its
capabilities. TeleChat2.5 and T1 expand the pipeline by
incorporating a continual pretraining phase with domain-specific datasets,
combined with reinforcement learning (RL) to improve performance in code
generation and mathematical reasoning tasks. The T1 variant is
designed for complex reasoning, supporting long Chain-of-Thought (CoT)
reasoning and demonstrating substantial improvements in mathematics and coding.
In contrast, TeleChat2.5 prioritizes speed, delivering rapid
inference. Both flagship models of T1 and TeleChat2.5 are
dense Transformer-based architectures with 115B parameters, showcasing
significant advancements in reasoning and general task performance compared to
the original TeleChat. Notably, T1-115B outperform proprietary models
such as OpenAI's o1-mini and GPT-4o. We publicly release TeleChat2,
TeleChat2.5 and T1, including post-trained versions with 35B
and 115B parameters, to empower developers and researchers with
state-of-the-art language models tailored for diverse applications.