3 papers
cs.LG2026
Unlabeled Data Can Provably Enhance In-Context Learning of Transformers
Renpu Liu, Jing Yang
Large language models (LLMs) exhibit impressive in-context learning (ICL) capabilities, yet the quality of their predictions is fundamentally limited by the few costly labeled demo…
cs.LG2025
On the Learn-to-Optimize Capabilities of Transformers in In-Context Sparse Recovery
Renpu Liu, Ruida Zhou, Cong Shen +1
An intriguing property of the Transformer is its ability to perform in-context learning (ICL), where the Transformer can solve different inference tasks without parameter updating…
cs.LG2025
A Shared Low-Rank Adaptation Approach to Personalized RLHF
Renpu Liu, Peng Wang, Donghao Li +2
Reinforcement Learning from Human Feedback (RLHF) has emerged as a pivotal technique for aligning artificial intelligence systems with human values, achieving remarkable success in…