3 papers
cs.CL2025
FLAME-MoE: A Transparent End-to-End Research Platform for Mixture-of-Experts Language Models
Hao Kang, Zichun Yu, Chenyan Xiong
Recent large language models such as Gemini-1.5, DeepSeek-V3, and Llama-4 increasingly adopt Mixture-of-Experts (MoE) architectures, which offer strong efficiency-performance trade…
cs.CL2025
Efficient Multi-Agent System Training with Data Influence-Oriented Tree Search
Wentao Shi, Zichun Yu, Fuli Feng +2
Monte Carlo Tree Search (MCTS) based methods provide promising approaches for generating synthetic data to enhance the self-training of Large Language Model (LLM) based multi-agent…
cs.CL2024
Montessori-Instruct: Generate Influential Training Data Tailored for Student Learning
Xiaochuan Li, Zichun Yu, Chenyan Xiong
Synthetic data has been widely used to train large language models, but their generative nature inevitably introduces noisy, non-informative, and misleading learning signals. In th…