CHARM: Calibrating Reward Models With Chatbot Arena Scores
Xiao Zhu, Chenmien Tan, Pinzhen Chen, Rico Sennrich, Yanlin Zhang, Hanxu Hu·April 14, 2025
Summary
CHARM mitigates reward model bias in Reinforcement Learning with human feedback, using Elo scores for enhanced accuracy and preference alignment. It offers a fair, reliable method for model construction, with studies showing preference-optimized models have fewer parameters than leading commercial alternatives.
Introduction
Background
Explanation of reward model bias in Reinforcement Learning
Importance of human feedback in addressing bias
Objective
Aim of CHARM in reducing bias and improving model accuracy
Highlighting the role of Elo scores in preference alignment
Method
Data Collection
Techniques for gathering human feedback
Importance of diverse and representative feedback sources
Data Preprocessing
Methods for cleaning and preparing human feedback data
Transformation of feedback into a format suitable for model training
Model Construction
Overview of the CHARM framework
Integration of Elo scores for preference optimization
Parameter Optimization
Techniques for minimizing model parameters while maintaining accuracy
Comparison with leading commercial alternatives
Results
Performance Evaluation
Metrics for assessing model accuracy and preference alignment
Comparison of preference-optimized models with baseline models
Parameter Efficiency
Analysis of model parameter count
Demonstration of CHARM's ability to construct models with fewer parameters
Conclusion
Summary of Findings
Recap of CHARM's effectiveness in mitigating bias
Highlighting the benefits of using Elo scores for preference optimization
Future Directions
Potential areas for further research and development
Considerations for scaling and adapting CHARM to different applications
Basic info
papers
machine learning
artificial intelligence
Advanced features