4 papers · 1 filter
CIPHER-MoE: Balancing Efficiency and Routing Fidelity in Trillion-Scale MoE Training
Jing Li, Jian Meng, Yingmeng Gao +8
Mixture-of-Experts (MoE) has been widely adopted in recent large language model (LLM) architectures. However, scaling up MoE in LLM training introduces system-level challenges on t…
Data Trajectory Alignment for LLM Domain Adaptation: A Two-Phase Synthesis Framework for Telecommunications Mathematics
Zhicheng Zhou, Jing Li, Suming Qiu +3
General-purpose large language models (LLMs) are increasingly deployed in verticals such as telecommunications, where adaptation is hindered by scarce, low-information-density corp…
Agentic-KGR: Co-evolutionary Knowledge Graph Construction through Multi-Agent Reinforcement Learning
Jing Li, Zhijie Sun, Zhicheng Zhou +4
Current knowledge-enhanced large language models (LLMs) rely on static, pre-constructed knowledge bases that suffer from coverage gaps and temporal obsolescence, limiting their eff…
Logits Replay + MoClip: Stabilized, Low-Cost Post-Training with Minimal Forgetting
Suming Qiu, Jing Li, Zhicheng Zhou +3
Large language models (LLMs) often face a trade-off in post-training: improvements on specialized domains frequently come at the expense of general capabilities. Existing solutions…