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 …

Preference optimization, particularly through Reinforcement Learning from Human
Feedback (RLHF), has achieved significant success in aligning Large Language Models …

This tutorial offers a comprehensive guide on using multi-armed bandit (MAB) algorithms to
improve Large Language Models (LLMs). As Natural Language Processing (NLP) tasks …

Reinforcement learning from human feedback (RLHF) has emerged as a central tool for
language model alignment. We consider online exploration in RLHF, which exploits …

Large language models (LLMs) have demonstrated remarkable performances in various
tasks. However, the performance of LLMs heavily depends on the input prompt, which has …

While problem solving is a standard evaluation task for foundation models, a crucial
component of problem solving--actively and strategically gathering information to test …

Leveraging human preferences for steering the behavior of Large Language Models (LLMs)
has demonstrated notable success in recent years. Nonetheless, data selection and labeling …

We study methods for efficiently aligning large language models (LLMs) with human
preferences given budgeted online feedback. We first formulate the LLM alignment problem …

A crucial capability of Machine Learning models in real-world applications is the ability to
continuously learn new tasks. This adaptability allows them to respond to potentially …

A sequential decision-making agent balances between exploring to gain new knowledge
about an environment and exploiting current knowledge to maximize immediate reward. For …