Widely used alignment techniques, such as reinforcement learning from human feedback
(RLHF), rely on the ability of humans to supervise model behavior-for example, to evaluate …

Assessment and evaluation have long been critical challenges in artificial intelligence (AI)
and natural language processing (NLP). However, traditional methods, whether matching …

This paper studies post-training large language models (LLMs) using preference feedback
from a powerful oracle to help a model iteratively improve over itself. The typical approach …

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

Recent studies have shown that large language models'(LLMs) mathematical problem-
solving capabilities can be enhanced by integrating external tools, such as code …

The success of Large Language Models (LLMs) is inherently linked to the availability of vast,
diverse, and high-quality data for training and evaluation. However, the growth rate of high …

We present the workflow of Online Iterative Reinforcement Learning from Human Feedback
(RLHF) in this technical report, which is widely reported to outperform its offline counterpart …

Large Language Models (LLMs) exhibit remarkably powerful capabilities. One of the crucial
factors to achieve success is aligning the LLM's output with human preferences. This …

Reinforcement learning from human feedback (RLHF) plays a crucial role in aligning
language models with human preferences. While the significance of dataset quality is …

Alignment approaches such as RLHF and DPO are actively investigated to align large
language models (LLMs) with human preferences. Commercial large language models …