3 citations · 7 across the 9 of their papers we have counts for
5 papers · 1 filter
ExTra: Exploratory Trajectory Optimization for Language Model Reinforcement Learning
Wenyang Hu, Junxiang Jia, Zhen Shu +3
Reinforcement Learning with Verifiable Rewards (RLVR) for language-model reasoning can fail at both extremes of task difficulty: easy prompts often produce all-correct, low-diversi…
Uncovering Scaling Laws for Large Language Models via Inverse Problems
Arun Verma, Zhaoxuan Wu, Zijian Zhou +15
Large Language Models (LLMs) are large-scale pretrained models that have achieved remarkable success across diverse domains. These successes have been driven by unprecedented compl…
Ferret: Federated Full-Parameter Tuning at Scale for Large Language Models
Yao Shu, Wenyang Hu, See-Kiong Ng +2
Large Language Models (LLMs) have become indispensable in numerous real-world applications. However, fine-tuning these models at scale, especially in federated settings where data…
Data-Centric AI in the Age of Large Language Models
Xinyi Xu, Zhaoxuan Wu, Rui Qiao +16
This position paper proposes a data-centric viewpoint of AI research, focusing on large language models (LLMs). We start by making the key observation that data is instrumental in…
Use Your INSTINCT: INSTruction optimization for LLMs usIng Neural bandits Coupled with Transformers
Xiaoqiang Lin, Zhaoxuan Wu, Zhongxiang Dai +5
Large language models (LLMs) have shown remarkable instruction-following capabilities and achieved impressive performances in various applications. However, the performances of LLM…