1 citations · 1 across the 4 of their papers we have counts for
5 papers
Ranking and Partitioning of Attention Heads for Sparse and Continual Reinforcement Learning Fine-tuning
Ruizhe Shi, Zaiwen Yang, Qiubo Wang +2
We study sparse and continual reinforcement learning fine-tuning of language models through attention-head ranking and partitioning. For sparse learning, we rank attention heads by…
On the Nonlinearity of Learning Rate Scaling for LLM Training
Zaiwen Yang, Huaqing Zhang, Jing Xu +1
Learning-rate transfer can reduce the cost of training large language models: instead of sweeping learning rates at target scale, practitioners extrapolate from smaller runs. Exist…
Position: The Inevitable End of One-Architecture-Fits-All-Domains in Time Series Forecasting
Qinwei Ma, Jingzhe Shi, Jiahao Qiu +1
Recent work has questioned the effectiveness and robustness of neural network architectures for time series forecasting tasks. We summarize these concerns and analyze groundly thei…
QuestA: Expanding Reasoning Capacity in LLMs via Question Augmentation
Jiazheng Li, Hongzhou Lin, Hong Lu +5
Reinforcement learning (RL) has emerged as a central paradigm for training large language models (LLMs) in reasoning tasks. Yet recent studies question RL's ability to incentivize…
CHOPS: CHat with custOmer Profile Systems for Customer Service with LLMs
Jingzhe Shi, Jialuo Li, Qinwei Ma +3
Businesses and software platforms are increasingly turning to Large Language Models (LLMs) such as GPT-3.5, GPT-4, GLM-3, and LLaMa-2 for chat assistance with file access or as rea…