Investigating on RLHF methodology

TL;DR

This paper explores methods for aligning large language models with human preferences, focusing on preference modeling, reinforcement learning fine-tuning, and direct preference optimization, while introducing a cost-effective dataset collection approach.

Contribution

It introduces a novel approach for collecting preference datasets via perplexity filtering, simplifying and reducing costs in alignment data creation.

Findings

Effective preference dataset collection using perplexity filtering

Insights into reinforcement learning fine-tuning challenges and solutions

Successful application of direct preference optimization without a separate preference model

Abstract

In this article, we investigate the alignment of Large Language Models according to human preferences. We discuss the features of training a Preference Model, which simulates human preferences, and the methods and details we found essential for achieving the best results. We also discuss using Reinforcement Learning to fine-tune Large Language Models and describe the challenges we faced and the ways to overcome them. Additionally, we present our experience with the Direct Preference Optimization method, which enables us to align a Large Language Model with human preferences without creating a separate Preference Model. As our contribution, we introduce the approach for collecting a preference dataset through perplexity filtering, which makes the process of creating such a dataset for a specific Language Model much easier and more cost-effective.