From the 1 of 20 linked papers with an AI index.
7 papers · 1 filter
Counterfactually Safe Reinforcement Learning
Jingyi Li, Peng Wu, Chengchun Shi
Reinforcement learning algorithms are generally designed to maximize the expected return across a population. However, a policy that is optimal on average may be suboptimal for cer…
Perturbation is All You Need for Extrapolating Language Models
Zetai Cen, Jin Zhu, Xinwei Shen +1
This paper develops a statistical theory of extrapolation for large language models, by reinterpreting them through pre-post-additive noise models. In contrast to the standard auto…
Reinforcement Learning from Human Feedback: A Statistical Perspective
Pangpang Liu, Chengchun Shi, Will Wei Sun
Reinforcement learning from human feedback (RLHF) has emerged as a central framework for aligning large language models (LLMs) with human preferences. Despite its practical success…
Robust Reinforcement Learning from Human Feedback for Large Language Models Fine-Tuning
Kai Ye, Hongyi Zhou, Jin Zhu +2
Reinforcement learning from human feedback (RLHF) has emerged as a key technique for aligning the output of large language models (LLMs) with human preferences. To learn the reward…
Statistical Inference in Reinforcement Learning: A Selective Survey
Chengchun Shi
Reinforcement learning (RL) is concerned with how intelligence agents take actions in a given environment to maximize the cumulative reward they receive. In healthcare, applying RL…
Off-policy Evaluation with Deeply-abstracted States
Meiling Hao, Pingfan Su, Liyuan Hu +3
Off-policy evaluation (OPE) is crucial for assessing a target policy's impact offline before its deployment. However, achieving accurate OPE in large state spaces remains challengi…