Online RLHF: A Comprehensive Introduction
Online Reinforcement Learning from Human Feedback (RLHF) is a methodology designed to enhance the alignment of large language models (LLMs) through an online iterative feedback process. This innovative approach is known for its significant performance improvement over traditional offline methods, and the Online RLHF project aims to provide an open-source framework that empowers researchers and developers to achieve these gains using publicly available data.
Overview
The Online RLHF project aims to bridge the gap between the traditional offline learning frameworks for language models and the more effective online iterative methods. By offering a detailed and easily reproducible recipe for online RLHF, this project enables users to replicate or even surpass the results of state-of-the-art models like LLaMA3-8B-instruct using open-source data.
Model Releases
The project boasts a variety of released models, each serving a different stage of the RLHF process:
- Supervised Fine-Tuning (SFT) Models: Available on Huggingface for direct implementation.
- Reward Models: These models, such as FsfairX-LLaMA3-RM-v0.1, facilitate the evaluation and ranking of model outputs.
- RLHF Models: Models like LLaMA3-iterative-DPO-final are iteratively refined using the online feedback mechanism.
Installation Instructions
The recommended setup involves creating separate environments for inference and training, emphasizing stability and preventing potential issues with package dependencies, notably with numpy versions below 2.0.
- Inference Environment: Set up involves installing dependencies such as
vllm,accelerate,deepspeed, andtransformersalongside specific versions that ensure compatibility. - Training Environment: This includes cloning repositories, setting up PyTorch, installing various essential packages, and configuring integrations with tools such as wandb and Huggingface.
Starting the Project
The project is structured into clear, actionable steps that guide users through the entire process:
Step 1: Supervised Fine-Tuning
In this initial phase, users preprocess their datasets and train models using the provided scripts, adjustable according to the user's computational capacity.
Step 2: Reward Modeling
For those interested in training reward and preference models, the project provides a detailed guide and access to state-of-the-art models.
Step 3.1: Data Generation
To generate data efficiently, the project utilizes VLLM, offering various scripts and prompts to cater to different user environments.
Step 3.2: Data Annotation
The process continues with the annotation of the generated data using the reward models, enabling the ranking of model responses based on quality and relevance.
Step 3.3: Training
This step involves training the refined models using scripts that are adaptable to varying hardware configurations.
Integration & Automation
For seamless operation, the project allows users to automate the entire iterative process through a provided script that can be customized to fit different system setups. This comprehensive automation facilitates continuous improvement and refinement of models with minimal manual intervention.
Acknowledgements
This project acknowledges the contributions of several open-source communities and teams that have provided invaluable resources such as models, code, and datasets.
How to Cite
For those referencing this work, citation details are provided, acknowledging the contributions of the project's authors in relevant academic and research contexts.
By providing a structured and detailed approach to online RLHF, this project empowers users to maximize the potential of their language models while benefiting from cutting-edge research and methodologies.