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RLHFlow/RLHF-Reward-Modeling

A comprehensive collection of recipes and code for training various reward models crucial for Reinforcement Learning from Human Feedback (RLHF) in large language models.

Core Features

Diverse Reward Modeling Techniques: Implements classic Bradley-Terry, advanced Pairwise Preference, ArmoRM, OdinRM, MathRM (PRM/ORM), and Decision-Tree reward models.

State-of-the-Art Performance: Provides recipes and models that achieve top rankings on benchmarks like RewardBench, including SOTA 8B models.

Reproducible Research: Offers open-source code, data, hyperparameters, and models for easy reproduction of advanced reward modeling techniques.

Advanced Methodologies: Incorporates semi-supervised learning (SSRM) and causal inference (RRM) to enhance preference dataset quality and mitigate reward hacking.

Specialized Solutions: Addresses specific challenges such as length bias disentanglement (OdinRM) and interpretable preferences (ArmoRM, Decision-Tree RM).

Detailed Introduction

This project serves as a vital toolkit for researchers and developers focused on improving large language models through Reinforcement Learning from Human Feedback (RLHF). It provides a structured collection of code and recipes for training a wide array of reward models, from foundational Bradley-Terry to cutting-edge techniques like ArmoRM and Decision-Tree RMs. By offering reproducible methods, pre-trained models, and solutions to common challenges like reward hacking and length bias, it empowers the community to build more aligned, robust, and interpretable AI systems, pushing the boundaries of LLM performance and safety.