2 papers
cs.AI2025
A Framework for Quantifying How Pre-Training and Context Benefit In-Context Learning
Bingqing Song, Jiaxiang Li, Rong Wang +2
Pre-trained large language models have demonstrated a strong ability to learn from context, known as in-context learning (ICL). Despite a surge of recent applications that leverage…
cs.LG2025
Aligning Frozen LLMs by Reinforcement Learning: An Iterative Reweight-then-Optimize Approach
Xinnan Zhang, Chenliang Li, Siliang Zeng +6
Aligning large language models (LLMs) with human preferences usually requires fine-tuning methods such as RLHF and DPO. These methods directly optimize the model parameters, so the…