While large-scale unsupervised language models (LMs) learn broad world knowledge and
some reasoning skills, achieving precise control of their behavior is difficult due to the …

Using reinforcement learning with human feedback (RLHF) has shown significant promise in
fine-tuning diffusion models. Previous methods start by training a reward model that aligns …

Reinforcement learning from human feedback (RLHF) is a variant of reinforcement learning
(RL) that learns from human feedback instead of relying on an engineered reward function …

We consider the problem of contextual bandits and imitation learning, where the learner
lacks direct knowledge of the executed action's reward. Instead, the learner can actively …

Reinforcement Learning algorithms that learn from human feedback (RLHF) need to be
efficient in terms of statistical complexity, computational complexity, and query complexity. In …

We present a novel unified bilevel optimization-based framework,\textsf {PARL}, formulated
to address the recently highlighted critical issue of policy alignment in reinforcement …

Reinforcement Learning from Human Feedback (RLHF) has become the standard approach
for aligning Large Language Models (LLMs) with human preferences, allowing LLMs to …

The diversity of contexts in which large language models (LLMs) are deployed requires the
ability to modify or customize default model behaviors to incorporate nuanced requirements …

In this paper, we take a step towards a deeper understanding of learning from human
preferences by systematically comparing the paradigm of reinforcement learning from …

Understanding the inner mechanisms of black-box foundation models (FMs) is essential yet
challenging in artificial intelligence and its applications. Over the last decade, the long …