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.

Key findings

8

Paper digest

What problem does the paper attempt to solve? Is this a new problem?

The paper aims to address the issue of high expert query costs in reinforcement learning with human feedback (RLHF) by exploring cost-effective proxy reward oracle construction strategies for labeling preferences or rewards with limited labeled data and expert query budgets . This problem is not entirely new, as traditional methods relied on offline preference dataset constructions, but recent approaches have shifted towards online settings to iteratively construct new preference data through self-generated responses and high-quality reward/preference feedback .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the scientific hypothesis related to cost-effective proxy reward model construction strategies for labeling preferences or rewards with limited labeled data and expert query budgets. The key innovations explored in this study include on-policy query to address out-of-distribution and imbalance issues in seed data, as well as active learning to select the most informative data for preference queries . The methodology introduced in this paper focuses on training an evaluation model with minimal expert-labeled data to effectively label more preference pairs for reinforcement learning from human feedback (RLHF) training .


What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?

The paper "Cost-Effective Proxy Reward Model Construction with On-Policy and Active Learning" proposes several innovative ideas, methods, and models in the field of reinforcement learning with human feedback (RLHF) for large language model pipelines . Here are some key points from the paper:

  1. Cost-Effective Proxy Reward Model Construction:

    • The paper introduces strategies for constructing cost-effective proxy reward oracles to label preferences or rewards with limited labeled data and expert query budgets .
    • It focuses on on-policy queries to avoid out-of-distribution (OOD) and imbalance issues in seed data, along with active learning to select the most informative data for preference queries .
  2. Direct Preference Optimization (DPO):

    • The paper utilizes Direct Preference Optimization (DPO) to train an evaluation model with minimal expert-labeled data, which then effectively labels more preference pairs for RLHF training .
    • The DPO approach leads to significant improvements in tasks like AlpacaEval2, MMLU-5shot, and MMLU-0shot with minimal query costs .
  3. Exploratory Strategies:

    • The paper proposes maintaining an extra exploratory strategy to cover more space, combining both on-policy and off-policy strategies for RLHF .
    • This combination of on-policy and off-policy strategies aims to enhance the overall performance of the RLHF system by exploring a wider range of possibilities .
  4. Active Learning and Core-set Selection:

    • The paper discusses the use of active learning strategies, particularly focusing on classical coreset selection methods, to annotate diverse inputs in the representation space .
    • It introduces two methods for extracting embeddings - coresetEFT and coresetIFT, each with its own benefits and considerations .
  5. Integration with Existing Strategies:

    • The proposed methodology is orthogonal to other direct expert query-based strategies, suggesting that it can be integrated with existing approaches to further reduce query costs in RLHF systems .

Overall, the paper presents a comprehensive framework for constructing proxy reward models efficiently, leveraging on-policy queries, active learning, and innovative preference labeling strategies to enhance the performance of RLHF systems in large language model pipelines . The paper "Cost-Effective Proxy Reward Model Construction with On-Policy and Active Learning" introduces several key characteristics and advantages compared to previous methods in the field of reinforcement learning with human feedback (RLHF) for large language model pipelines .

  1. Cost-Effective Proxy Reward Oracle Construction:

    • The paper focuses on constructing cost-effective proxy reward oracles to label preferences with limited labeled seed data and expert query budgets .
    • It introduces innovative strategies like on-policy queries and active learning to efficiently label preferences or rewards, leading to significant improvements in training evaluation models with minimal expert-labeled data .
  2. Direct Preference Optimization (DPO):

    • The paper utilizes Direct Preference Optimization (DPO) to train evaluation models effectively, resulting in the labeling of nine times more preference pairs for RLHF training with minimal query costs .
    • This approach, combined with on-policy queries, demonstrates over 1% average improvement on tasks like AlpacaEval2, MMLU-5shot, and MMLU-0shot .
  3. Exploratory Strategies:

    • The paper proposes an extra exploratory strategy to cover more space, combining on-policy and off-policy strategies to enhance the overall performance of RLHF systems .
    • By incorporating active learning techniques, the paper aims to select the most informative data points for preference queries, improving the efficiency of the labeling process .
  4. Integration with Existing Strategies:

    • The methodology presented in the paper is orthogonal to other direct expert query-based strategies, allowing for potential integration with existing approaches to further reduce query costs in RLHF systems .
    • The focus on constructing proxy reward models efficiently, leveraging on-policy queries and active learning, sets this approach apart from traditional methods that rely on offline preference dataset constructions .

Overall, the paper's approach stands out for its emphasis on cost-effective proxy reward oracle construction, utilization of on-policy queries and active learning, and the significant improvements in labeling preferences with minimal expert-labeled data, showcasing its potential to enhance RLHF systems in large language model pipelines .


Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?

Several related research works exist in the field, and some noteworthy researchers on this topic include Yizhong Wang, Yeganeh Kordi, Swaroop Mishra, Alisa Liu, Noah A Smith, Daniel Khashabi, Hannaneh Hajishirzi, Wei Xiong, Hanze Dong, Chenlu Ye, Ziqi Wang, Han Zhong, Heng Ji, Nan Jiang, Tong Zhang, and many others . The key to the solution mentioned in the paper involves exploring cost-effective proxy reward oracles construction strategies for labeling preferences or rewards with limited labeled data and expert query budgets. The approach introduces on-policy query to avoid out-of-distribution and imbalance issues in seed data, along with active learning to select the most informative data for preference queries, enabling the training of an evaluation model with minimal expert-labeled data .


How were the experiments in the paper designed?

The experiments in the paper were designed with a focus on evaluating the performance of different strategies for constructing a cost-effective proxy reward model with limited labeled seed data . The paper introduced three main approaches: random on-policy, and two active on-policy strategies called coresetIFT and coresetEFT . These approaches were compared with other methods like SPIN and self-rewarding to investigate their effectiveness in constructing a larger set of preferences with minimal labeled data . The experiments aimed to address key questions such as the sufficiency of training a weak evaluator on a small budget to construct a larger preference set, the impact of active learning strategies on performance compared to random on-policy, and how the on-policy+AL strategy compares with other candidate approaches like off-policy query and variants of SPIN .


What is the dataset used for quantitative evaluation? Is the code open source?

The dataset used for quantitative evaluation in the study is AlpacaEval2, MMLU-5shot, and MMLU-0shot . The code for AlpacaEval, an automatic evaluator of instruction-following models, is open source and available on GitHub at the following link: https://github.com/tatsu-lab/alpaca_eval .


Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.

The experiments and results presented in the paper provide strong support for the scientific hypotheses that needed verification. The study explores cost-effective proxy reward model construction strategies for labeling preferences or rewards with limited data and expert query budgets . The research introduces innovative methods like on-policy query and active learning to address issues such as out-of-distribution and data imbalance, resulting in the effective labeling of preference pairs for reinforcement learning from human feedback (RLHF) training . The experiments demonstrate the efficacy of the approach by achieving significant improvements in performance metrics like AlpacaEval2, MMLU-5shot, and MMLU-0shot with minimal query costs .

Furthermore, the study compares the proposed methods with existing approaches like SPIN and off-policy query, adapting and reproducing their strategies in the current setting . By evaluating the performance across different strategies and query budgets, the research provides a comprehensive analysis of the effectiveness of the cost-effective proxy reward model construction approach . The results showcase the advantages of the proposed strategies over traditional methods, highlighting the potential for reducing expert query costs while maintaining or improving performance .

Overall, the experiments and results in the paper offer robust empirical evidence to support the scientific hypotheses under investigation. The findings demonstrate the feasibility and effectiveness of the cost-effective proxy reward model construction approach, providing valuable insights for advancing reinforcement learning with human feedback methodologies .


What are the contributions of this paper?

The paper "Cost-Effective Proxy Reward Model Construction with On-Policy and Active Learning" introduces two key innovations in reinforcement learning with human feedback (RLHF) :

  1. On-Policy Query: This approach is used to avoid Out-of-Distribution (OOD) and imbalance issues in seed data.
  2. Active Learning: The paper utilizes active learning to select the most informative data for preference queries.

These methods enable the training of an evaluation model with minimal expert-labeled data, which can effectively label nine times more preference pairs for further RLHF training . The paper's methodology focuses on constructing cost-effective proxy reward oracles to label preferences or rewards with limited labeled data and expert query budgets, leading to significant improvements in model performance with minimal query costs .


What work can be continued in depth?

To delve deeper into the research field, one can further explore the continuation of works related to active learning strategies in reinforcement learning. Specifically, there is room for in-depth investigation into active on-policy query strategies that aim to select the most informative data for preference queries . Additionally, exploring the effectiveness of combining on-policy and off-policy strategies in reinforcement learning could be a valuable area for further research .

Tables

1

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 this study differ from previous works in RLHF, particularly in terms of expert involvement and evaluation model?
What is the primary focus of the paper in terms of reinforcement learning with human feedback (RLHF)?
How does the proposed method address the challenges of out-of-distribution and imbalance issues in seed data?
What tasks does the method improve performance on, and by how much does it reduce query costs?

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.
Mind map
Sampling methods and data augmentation
Techniques for detecting and mitigating OOD samples
Budget allocation and sampling techniques
Exploration-exploitation trade-off
Selection of informative data points
Real-world application examples
Query cost reduction and alignment improvement
AlpacaEval2, MMLU-5shot, and MMLU-0shot task results
Different approaches and their impact on performance and cost
Advantages and limitations in query minimization
Comparison with on-policy methods
Performance gains with minimal expert involvement
Managing expert resources efficiently
Model selection and validation
The role of a simplified evaluation model in guiding learning
Imbalance Reduction
Out-of-Distribution Data Handling
Query Optimization
Query Strategy
Minimize query costs while improving model performance
To develop a method for efficient use of expert input
Challenges with out-of-distribution and imbalance in seed data
Evolution of RLHF in reinforcement learning
List of cited literature and methodologies
Limitations and future research directions
Summary of findings and contributions
Case Studies
Performance Evaluation
Query Strategy Comparison
Off-Policy Methods Exploration
Limited Query Budget Approach
Weak Evaluation Model Utilization
Addressing Data Issues
On-Policy Query and Active Learning Integration
Objective
Background
References
Conclusion
Experiments and Results
Method
Introduction
Outline
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
Key findings
8

Paper digest

What problem does the paper attempt to solve? Is this a new problem?

The paper aims to address the issue of high expert query costs in reinforcement learning with human feedback (RLHF) by exploring cost-effective proxy reward oracle construction strategies for labeling preferences or rewards with limited labeled data and expert query budgets . This problem is not entirely new, as traditional methods relied on offline preference dataset constructions, but recent approaches have shifted towards online settings to iteratively construct new preference data through self-generated responses and high-quality reward/preference feedback .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the scientific hypothesis related to cost-effective proxy reward model construction strategies for labeling preferences or rewards with limited labeled data and expert query budgets. The key innovations explored in this study include on-policy query to address out-of-distribution and imbalance issues in seed data, as well as active learning to select the most informative data for preference queries . The methodology introduced in this paper focuses on training an evaluation model with minimal expert-labeled data to effectively label more preference pairs for reinforcement learning from human feedback (RLHF) training .


What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?

The paper "Cost-Effective Proxy Reward Model Construction with On-Policy and Active Learning" proposes several innovative ideas, methods, and models in the field of reinforcement learning with human feedback (RLHF) for large language model pipelines . Here are some key points from the paper:

  1. Cost-Effective Proxy Reward Model Construction:

    • The paper introduces strategies for constructing cost-effective proxy reward oracles to label preferences or rewards with limited labeled data and expert query budgets .
    • It focuses on on-policy queries to avoid out-of-distribution (OOD) and imbalance issues in seed data, along with active learning to select the most informative data for preference queries .
  2. Direct Preference Optimization (DPO):

    • The paper utilizes Direct Preference Optimization (DPO) to train an evaluation model with minimal expert-labeled data, which then effectively labels more preference pairs for RLHF training .
    • The DPO approach leads to significant improvements in tasks like AlpacaEval2, MMLU-5shot, and MMLU-0shot with minimal query costs .
  3. Exploratory Strategies:

    • The paper proposes maintaining an extra exploratory strategy to cover more space, combining both on-policy and off-policy strategies for RLHF .
    • This combination of on-policy and off-policy strategies aims to enhance the overall performance of the RLHF system by exploring a wider range of possibilities .
  4. Active Learning and Core-set Selection:

    • The paper discusses the use of active learning strategies, particularly focusing on classical coreset selection methods, to annotate diverse inputs in the representation space .
    • It introduces two methods for extracting embeddings - coresetEFT and coresetIFT, each with its own benefits and considerations .
  5. Integration with Existing Strategies:

    • The proposed methodology is orthogonal to other direct expert query-based strategies, suggesting that it can be integrated with existing approaches to further reduce query costs in RLHF systems .

Overall, the paper presents a comprehensive framework for constructing proxy reward models efficiently, leveraging on-policy queries, active learning, and innovative preference labeling strategies to enhance the performance of RLHF systems in large language model pipelines . The paper "Cost-Effective Proxy Reward Model Construction with On-Policy and Active Learning" introduces several key characteristics and advantages compared to previous methods in the field of reinforcement learning with human feedback (RLHF) for large language model pipelines .

  1. Cost-Effective Proxy Reward Oracle Construction:

    • The paper focuses on constructing cost-effective proxy reward oracles to label preferences with limited labeled seed data and expert query budgets .
    • It introduces innovative strategies like on-policy queries and active learning to efficiently label preferences or rewards, leading to significant improvements in training evaluation models with minimal expert-labeled data .
  2. Direct Preference Optimization (DPO):

    • The paper utilizes Direct Preference Optimization (DPO) to train evaluation models effectively, resulting in the labeling of nine times more preference pairs for RLHF training with minimal query costs .
    • This approach, combined with on-policy queries, demonstrates over 1% average improvement on tasks like AlpacaEval2, MMLU-5shot, and MMLU-0shot .
  3. Exploratory Strategies:

    • The paper proposes an extra exploratory strategy to cover more space, combining on-policy and off-policy strategies to enhance the overall performance of RLHF systems .
    • By incorporating active learning techniques, the paper aims to select the most informative data points for preference queries, improving the efficiency of the labeling process .
  4. Integration with Existing Strategies:

    • The methodology presented in the paper is orthogonal to other direct expert query-based strategies, allowing for potential integration with existing approaches to further reduce query costs in RLHF systems .
    • The focus on constructing proxy reward models efficiently, leveraging on-policy queries and active learning, sets this approach apart from traditional methods that rely on offline preference dataset constructions .

Overall, the paper's approach stands out for its emphasis on cost-effective proxy reward oracle construction, utilization of on-policy queries and active learning, and the significant improvements in labeling preferences with minimal expert-labeled data, showcasing its potential to enhance RLHF systems in large language model pipelines .


Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?

Several related research works exist in the field, and some noteworthy researchers on this topic include Yizhong Wang, Yeganeh Kordi, Swaroop Mishra, Alisa Liu, Noah A Smith, Daniel Khashabi, Hannaneh Hajishirzi, Wei Xiong, Hanze Dong, Chenlu Ye, Ziqi Wang, Han Zhong, Heng Ji, Nan Jiang, Tong Zhang, and many others . The key to the solution mentioned in the paper involves exploring cost-effective proxy reward oracles construction strategies for labeling preferences or rewards with limited labeled data and expert query budgets. The approach introduces on-policy query to avoid out-of-distribution and imbalance issues in seed data, along with active learning to select the most informative data for preference queries, enabling the training of an evaluation model with minimal expert-labeled data .


How were the experiments in the paper designed?

The experiments in the paper were designed with a focus on evaluating the performance of different strategies for constructing a cost-effective proxy reward model with limited labeled seed data . The paper introduced three main approaches: random on-policy, and two active on-policy strategies called coresetIFT and coresetEFT . These approaches were compared with other methods like SPIN and self-rewarding to investigate their effectiveness in constructing a larger set of preferences with minimal labeled data . The experiments aimed to address key questions such as the sufficiency of training a weak evaluator on a small budget to construct a larger preference set, the impact of active learning strategies on performance compared to random on-policy, and how the on-policy+AL strategy compares with other candidate approaches like off-policy query and variants of SPIN .


What is the dataset used for quantitative evaluation? Is the code open source?

The dataset used for quantitative evaluation in the study is AlpacaEval2, MMLU-5shot, and MMLU-0shot . The code for AlpacaEval, an automatic evaluator of instruction-following models, is open source and available on GitHub at the following link: https://github.com/tatsu-lab/alpaca_eval .


Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.

The experiments and results presented in the paper provide strong support for the scientific hypotheses that needed verification. The study explores cost-effective proxy reward model construction strategies for labeling preferences or rewards with limited data and expert query budgets . The research introduces innovative methods like on-policy query and active learning to address issues such as out-of-distribution and data imbalance, resulting in the effective labeling of preference pairs for reinforcement learning from human feedback (RLHF) training . The experiments demonstrate the efficacy of the approach by achieving significant improvements in performance metrics like AlpacaEval2, MMLU-5shot, and MMLU-0shot with minimal query costs .

Furthermore, the study compares the proposed methods with existing approaches like SPIN and off-policy query, adapting and reproducing their strategies in the current setting . By evaluating the performance across different strategies and query budgets, the research provides a comprehensive analysis of the effectiveness of the cost-effective proxy reward model construction approach . The results showcase the advantages of the proposed strategies over traditional methods, highlighting the potential for reducing expert query costs while maintaining or improving performance .

Overall, the experiments and results in the paper offer robust empirical evidence to support the scientific hypotheses under investigation. The findings demonstrate the feasibility and effectiveness of the cost-effective proxy reward model construction approach, providing valuable insights for advancing reinforcement learning with human feedback methodologies .


What are the contributions of this paper?

The paper "Cost-Effective Proxy Reward Model Construction with On-Policy and Active Learning" introduces two key innovations in reinforcement learning with human feedback (RLHF) :

  1. On-Policy Query: This approach is used to avoid Out-of-Distribution (OOD) and imbalance issues in seed data.
  2. Active Learning: The paper utilizes active learning to select the most informative data for preference queries.

These methods enable the training of an evaluation model with minimal expert-labeled data, which can effectively label nine times more preference pairs for further RLHF training . The paper's methodology focuses on constructing cost-effective proxy reward oracles to label preferences or rewards with limited labeled data and expert query budgets, leading to significant improvements in model performance with minimal query costs .


What work can be continued in depth?

To delve deeper into the research field, one can further explore the continuation of works related to active learning strategies in reinforcement learning. Specifically, there is room for in-depth investigation into active on-policy query strategies that aim to select the most informative data for preference queries . Additionally, exploring the effectiveness of combining on-policy and off-policy strategies in reinforcement learning could be a valuable area for further research .

Tables
1
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