Cost-Effective Proxy Reward Model Construction with On-Policy and Active Learning
Yifang Chen, Shuohang Wang, Ziyi Yang, Hiteshi Sharma, Nikos Karampatziakis, Donghan Yu, Kevin Jamieson, Simon Shaolei Du, Yelong Shen·July 02, 2024
Summary
This paper presents a cost-effective approach to constructing proxy reward models in reinforcement learning with human feedback (RLHF) by combining on-policy query and active learning. The method addresses out-of-distribution and imbalance issues in seed data, allowing for more efficient use of expert input. It improves performance on tasks like AlpacaEval2, MMLU-5shot, and MMLU-0shot, reducing query costs. The study differentiates from previous works by using a weak evaluation model and a limited query budget, demonstrating that even with minimal expert involvement, significant performance gains can be achieved. The research also explores off-policy methods and compares various query strategies, highlighting the potential of their approach to minimize query expenses and enhance model alignment.
Introduction
Background
Evolution of RLHF in reinforcement learning
Challenges with out-of-distribution and imbalance in seed data
Objective
To develop a method for efficient use of expert input
Minimize query costs while improving model performance
Method
On-Policy Query and Active Learning Integration
Query Strategy
Selection of informative data points
Exploration-exploitation trade-off
Query Optimization
Budget allocation and sampling techniques
Addressing Data Issues
Out-of-Distribution Data Handling
Techniques for detecting and mitigating OOD samples
Imbalance Reduction
Sampling methods and data augmentation
Weak Evaluation Model Utilization
The role of a simplified evaluation model in guiding learning
Model selection and validation
Limited Query Budget Approach
Managing expert resources efficiently
Performance gains with minimal expert involvement
Off-Policy Methods Exploration
Comparison with on-policy methods
Advantages and limitations in query minimization
Query Strategy Comparison
Different approaches and their impact on performance and cost
Experiments and Results
Performance Evaluation
AlpacaEval2, MMLU-5shot, and MMLU-0shot task results
Query cost reduction and alignment improvement
Case Studies
Real-world application examples
Conclusion
Summary of findings and contributions
Limitations and future research directions
References
List of cited literature and methodologies
Basic info
papers
computation and language
machine learning
artificial intelligence
Advanced features
Insights
How does the proposed method address the challenges of out-of-distribution and imbalance issues in seed data?
How does this study differ from previous works in RLHF, particularly in terms of expert involvement and evaluation model?
What tasks does the method improve performance on, and by how much does it reduce query costs?
What is the primary focus of the paper in terms of reinforcement learning with human feedback (RLHF)?