Technical Report of TeleChat2, TeleChat2.5 and T1
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.
68834762846d2e78b7548bfb
Zihan Wang
claimed_verified
2025-07-25T13:36:16.549Z
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Zihan Wang
user
ZihanWang99
68834762846d2e78b7548bfc
Xinzhang Liu
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Yitong Yao
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Chao Wang
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Yu Zhao
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Zhihao Yang
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Wenmin Deng
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Kaipeng Jia
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Jiaxin Peng
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Yuyao Huang
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Sishi Xiong
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Zhuo Jiang
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Kaidong Yu
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Xiaohui Hu
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Fubei Yao
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Ruiyu Fang
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Zhuoru Jiang
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Ruiting Song
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Qiyi Xie
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Rui Xue
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Xuewei He
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Yanlei Xue
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Zhu Yuan
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Zhaoxi Zhang
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Zilu Huang
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Shiquan Wang
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Xin Wang
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Hanming Wu
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Mingyuan Wang
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Xufeng Zhan
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Yuhan Sun
68834762846d2e78b7548c1a
Zhaohu Xing
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Yuhao Jiang
68834762846d2e78b7548c1c
Bingkai Yang
68834762846d2e78b7548c1d
Shuangyong Song
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Yongxiang Li
68834762846d2e78b7548c1f
Zhongjiang He
68834762846d2e78b7548c20
Xuelong Li
Technical Report of TeleChat2, TeleChat2.5 and T1
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.
The TeleChat2, TeleChat2.5, and T1 models achieve performance improvements through enhanced training strategies, including Supervised Fine-Tuning, Direct Preference Optimization, and reinforcement learning, with T1 focusing on complex reasoning and TeleChat2.5 on speed.
Supervised Fine-Tuning Direct Preference Optimization reinforcement learning Chain-of-Thought Transformer-based architectures