TeleChat2: Advanced Open-Source LLM

• TeleChat2 is a series of open-source large language models featuring key architectural innovations like Grouped Query Attention and Rotary Position Embeddings.
• It employs a multi-stage training pipeline with massive pre-training, supervised fine-tuning, and reinforcement learning to optimize reasoning and code generation.
• The model supports diverse applications from dialogue systems to automated programming, delivering competitive performance against proprietary systems.

TeleChat2 is a series of open-source LLMs representing a substantial advancement over the original TeleChat models in both training scale and post-training alignment strategies. TeleChat2 and its successors introduce improved reasoning, code generation, and mathematical capabilities through enhancements in both data pipeline and optimization techniques. The models are released in large-scale variants, including 35B and 115B parameters, with the T1-115B model demonstrating competitive or superior performance to leading proprietary systems on challenging benchmarks.

1. Architectural Innovations and Parameterization

TeleChat2 is constructed on a dense Transformer architecture, preserving the foundational stack of self-attention and feed-forward layers from the original TeleChat but with technical refinements. All core models, including TeleChat2, TeleChat2.5, and T1, use Pre-Norm configuration with RMSNorm (Root Mean Square Layer Normalization) and employ the SwiGLU activation function in the feed-forward blocks. Rotary Position Embeddings (RoPE) are standardized for efficient handling of extended contexts and improved long-context generalization.

The most substantial advance for TeleChat2-115B is the adoption of Grouped Query Attention (GQA) with 8 key–value heads, which optimizes for both computational efficiency and inference latency by improving the key–value cache utilization during generation.

Model Variant	Parameter Count	Key Attention Variant	Specialization
TeleChat2-35B	35B	Standard Multi-Head	General
TeleChat2-115B	115B	GQA (8 KV heads)	Reasoning, Long Context
TeleChat2.5-115B	115B	GQA	High-Speed Inference
T1-115B	115B	GQA	Chain-of-Thought Reasoning

2. Multi-Stage Training and Optimization Pipeline

The TeleChat2 training process is characterized by several stages designed to maximize model quality and alignment with human preferences:

• Massive Pre-training: Each model is trained on approximately 10 trillion curated tokens drawn from high-quality, diverse sources. This large-scale pretraining furnishes robust semantic, factual, and procedural knowledge.
• Long-Context Annealing: A curriculum of gradually increasing sequence lengths (up to 128K tokens) is used, with progressive adjustment of RoPE base frequency, to maintain both short-context and long-context performance.
• Supervised Fine-Tuning (SFT): Instruction-following abilities are instilled through SFT over domain-rich datasets covering conversation, code, math, and multi-turn instruction-response examples.
• Direct Preference Optimization (DPO): DPO operates on prompt-response pairs with explicit human preferences and rejections. The model is optimized to maximize agreement with preferred outputs while penalizing rejected outputs, guided by a variant of preference loss with normalization:

$$r_i^{(t+1)} = r_i^{(t)}\, \kappa^{\frac{s_i^{(t)} - \bar{s}^{(t)}}{\mu}}$$

$$\hat{r}_i^{(t+1)} = \frac{r_i^{(t+1)}}{\sum_{i=1}^{|\mathcal{V}|} r_i^{(t+1)}}$$

• Reinforcement Learning (RL): For TeleChat2.5 and T1, post-SFT RL is incorporated, with feedback signals derived from automated test-case execution in code generation or solution accuracy in mathematical tasks.

TeleChat2.5 emphasizes rapid inference while T1 is tuned for complex, structured Chain-of-Thought reasoning in mathematics and coding.

3. Task Performance and Benchmark Results

TeleChat2 consistently surpasses its predecessor and is competitive with proprietary models such as OpenAI’s o1-mini and GPT-4o, particularly in code generation and mathematical reasoning. In benchmark evaluations, the T1-115B achieves higher accuracy than o1-mini and outperforms GPT-4o in certain metrics.

Performance gains are realized as a consequence of both parameter scaling and targeted training strategies: the enlarged model sizes enable richer internal representations, while DPO and RL sharpen the model's responses for both general reasoning and specialized technical tasks.

4. Comparison to Prior Models and Industry Standards

While TeleChat2 retains the essential architectural motifs of the original TeleChat (itself influenced by designs such as GPT-3, LLaMA, and BLOOM), the introduction of GQA, extended context handling, and the more rigorous post-training process yield marked improvements. The released variants (35B, 115B) allow direct comparison against models such as LLaMA 2-Chat, ChatGLM, and Qwen, with TeleChat2 matching or exceeding state-of-the-art performance in both open and closed-source model categories on a range of established NLP, code, and math benchmarks (Wang et al., 24 Jul 2025).

5. Applications and Use Cases

TeleChat2’s capabilities make it suitable for:

• Dialogue Systems and Conversational Agents: Advanced chatbots, customer support, and tutoring systems requiring nuanced understanding of long, coherent multi-turn exchanges.
• Code Generation and Automated Programming Support: Code suggestion, synthesis, repair, and debugging, with improved correctness through RL fine-tuning against execution feedback.
• Mathematical and Logical Reasoning: Tutoring, automated mathematics assistants, and scientific research tools, benefitting from robust mathematical reasoning post-training.
• Document Analysis and Long-Context Reasoning: Legal or medical research where extended contexts and document-level coherence are critical.
• Research Platform: The release of pre-trained and post-trained weights, together with a portion of the massive pre-training data, supports continued investigation into scaling laws, RLHF, and domain-specific adaptation.

6. Security and Privacy Implications in Video Chat Applications

In practical deployments of TeleChat2 (notably TeleChat2-powered RTC systems), the application of LLMs in video chat settings must account for privacy and security threats as described in early analyses of video chat platforms (1007.1473). TeleChat2, by virtue of its language and reasoning proficiency, can potentially assist in mitigation of:

• De-anonymization: Adoption of trusted relay servers to obscure user IPs; user education to avoid unnecessary personal disclosure.
• Phishing: Real-time authentication protocols (gesture-based), detection of virtual webcam sources, or model-powered verification.
• Man-in-the-Middle (MIM) Attacks: Architecture choices that favor server-mediated connections and rigorous session integrity checks.

The integration of these countermeasures aligns technical safeguards with advanced conversational AI, reducing the risk of privacy compromise in next-generation AI chat and video communication platforms.

7. Future Directions and Open Research Challenges

Open questions remain concerning efficient scaling, further improvement in reasoning and safety, and optimal adaptation to domain-specific requirements. Extensions of continual pretraining, more resource-efficient attention mechanisms, and robust RL pipelines are active research areas. The public availability of TeleChat2 resources enables broad community-driven innovation and rigorous comparative research, serving as a foundation for subsequent iterations such as TeleChat2.5 and T1, and potentially informing best practices in multimodal, long-context, and real-time deployable LLM systems (Wang et al., 24 Jul 2025).