7 papers
Prompt-Level Reward Specifications for Open-Ended Post-Training
Zijun Weng, Xiaohui Hu, Shuangyong Song +3
Open-ended post-training benefits from rewards that make prompt-specific success conditions explicit, rather than relying only on post-hoc scalar scores. In instruction following,…
Beyond Binary: Turning Partial Success into Dense Verifiable Rewards for Reinforcement Learning in Code Generation
Longwen Wang, Yirui Liu, Xuan'er Wu +6
Effective reward design is a central challenge in Reinforcement Learning (RL) for code generation. Mainstream test-suite-level outcome rewards enforce functional correctness but in…
Awakening Dormant Experts:Counterfactual Routing to Mitigate MoE Hallucinations
Wentao Hu, Yanbo Zhai, Xiaohui Hu +6
Sparse Mixture-of-Experts (MoE) models have achieved remarkable scalability, yet they remain vulnerable to hallucinations, particularly when processing long-tail knowledge. We iden…
Training Report of TeleChat3-MoE
Xinzhang Liu, Chao Wang, Zhihao Yang +51
TeleChat3-MoE is the latest series of TeleChat large language models, featuring a Mixture-of-Experts (MoE) architecture with parameter counts ranging from 105 billion to over one t…
Improve LLM-as-a-Judge Ability as a General Ability
Jiachen Yu, Shaoning Sun, Xiaohui Hu +3
LLM-as-a-Judge leverages the generative and reasoning capabilities of large language models (LLMs) to evaluate LLM responses across diverse scenarios, providing accurate preference…
Technical Report of TeleChat2, TeleChat2.5 and T1
Zihan Wang, Xinzhang Liu, Yitong Yao +35
We introduce the latest series of TeleChat models: \textbf{TeleChat2}, \textbf{TeleChat2.5}, and \textbf{T1}, offering a significant upgrade over their predecessor, TeleChat. Despi…