Towards Comprehensive Preference Data Collection for Reward Modeling

Yulan Hu, Qingyang Li, Sheng Ouyang, Ge Chen, Kaihui Chen, Lijun Mei, Xucheng Ye, Fuzheng Zhang, Yong Liu·June 24, 2024

Summary

The paper presents a comprehensive framework for collecting high-quality preference data in Reinforcement Learning from Human Feedback (RLHF) for training reward models in aligning large language models with human preferences. The framework consists of four steps: prompt generation, response generation using stronger models like SFT or GPT-4, filtering to ensure diversity and quality, and human labeling. It addresses noise and efficiency by using AI filtering and minimizing labor. Experiments show improved performance with each step, with preference benchmarks and Best-of-N experiments demonstrating the effectiveness of the refined data. The framework is particularly useful for later stages of resource management and specific verticals, but may not be ideal for quick data generation. The research also explores various techniques, challenges, and applications in large language model alignment, including Chinese models, instruction-following, and reinforcement learning.

Key findings

3

Paper digest

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

The paper aims to address the challenge of collecting high-quality preference data for training reward models (RMs) . This problem is highlighted as previous technical reports and research studies have not extensively analyzed the collection of preference data specifically for RM training . The proposed framework in the paper focuses on decomposing the preference data collection process into four sub-steps: Prompt Generation, Response Generation, Response Filtering, and Human Labeling, to ensure the quality of the collected data for RM training . This problem of collecting high-quality preference data for RMs is a significant focus of the paper, indicating a new and important area of research within the field of reward modeling.


What scientific hypothesis does this paper seek to validate?

This paper aims to validate a scientific hypothesis related to the collection of preference data for training reward models (RMs) in the context of Reinforcement Learning from Human Feedback (RLHF) . The study proposes a comprehensive framework for preference data collection, focusing on four key sub-steps: Prompt Generation, Response Generation, Response Filtering, and Human Labeling . The goal is to ensure the high-quality collection of preferences while reducing the reliance on human labor . The framework is designed to enhance the alignment of Large Language Models (LLMs) with human preferences by improving the quality of responses generated through reinforcement learning .


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

The paper "Towards Comprehensive Preference Data Collection for Reward Modeling" proposes a novel framework for collecting high-quality preference data for training reward models (RMs) . This framework consists of four key sub-steps:

  1. Prompt Generation: Selects challenging prompts that the SFT model struggles to handle, ensuring the generation of diverse and high-quality prompts .
  2. Response Generation: Produces varied responses to enhance model generalization, with a focus on generating responses that are superior to the current model being optimized. This involves using stronger models like GPT-4 to generate responses .
  3. Response Filtering: Involves filtering the generated responses to ensure the training candidate set contains instances that provide a supervisory signal for RM training. GPT-4 is utilized to score each instance in the training set based on response quality, with a five-level scoring criteria to refine the dataset before human labeling .
  4. Human Labeling: Annotates a modest amount of pseudo preference data, ensuring that the RM is trained on high-quality data reviewed by human labelers .

The paper also discusses the limitations of the proposed framework, particularly highlighting the challenge of long-term data production due to the extensive filtering required at each step of the data collection process . Additionally, it emphasizes the importance of combining AI filtering with human intervention to effectively reflect human preferences while reducing the amount of human labor required for data collection . The proposed framework for collecting preference data for training reward models (RMs) offers several key characteristics and advantages compared to previous methods outlined in the paper "Towards Comprehensive Preference Data Collection for Reward Modeling" .

  1. Structured Approach: The framework decomposes the preference data collection process into four sub-steps: Prompt Generation, Response Generation, Response Filtering, and Human Labeling. This structured approach ensures the gathering of high-quality preferences while reducing reliance on human labor .

  2. Quality Improvement: By utilizing stronger models like GPT-4 for response generation and filtering, the framework aims to enhance the quality of the collected preference data. This results in improved data quality, as demonstrated by the results showing enhancements in performance with refined preference data .

  3. Diversity and Supervisory Signal: The framework emphasizes the importance of ensuring diversity in responses and the presence of a supervisory signal in the collected data. It addresses the challenge of generating diverse responses by combining results from various models and employing off-the-shelf strong models. Additionally, it filters out useless training samples using GPT-4 based on a five-level scoring criteria to refine the dataset before human labeling .

  4. Reduction of Human Labor: By integrating AI filtering with human intervention, the framework effectively reflects human preferences while significantly reducing the amount of human labor required for data collection. This reduction in human labor is achieved through the incorporation of AI filtering techniques and the annotation of a modest amount of pseudo preference data .

  5. Validation and Benchmarking: The framework's effectiveness is validated using preference data benchmarks and policy learning, showcasing improvements in data quality. The results demonstrate the enhancement in performance with the refinement of preference data, as illustrated by the win rates of the reward model trained with preference data in different steps .

Overall, the proposed framework offers a systematic and effective approach to collecting high-quality preference data for training reward models, addressing key challenges and limitations of previous methods while emphasizing the importance of data quality and reducing human labor involvement in the process .


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 papers exist in the field of reinforcement learning from human feedback (RLHF) and reward modeling. Noteworthy researchers in this field include Reiichiro Nakano, Jacob Hilton, Suchir Balaji, Jeff Wu, Long Ouyang, Christina Kim, Christopher Hesse, Shantanu Jain, Vineet Kosaraju, William Saunders, Alexandre Ramé, Nino Vieillard, Léonard Hussenot, Robert Dadashi, Geoffrey Cideron, Olivier Bachem, Johan Ferret, and many others .

The key to the solution mentioned in the paper is a comprehensive framework for preference data collection, which is divided into four incremental steps: Prompt Generation, Response Generation, Response Filtering, and Human Labeling. This structured approach ensures the collection of high-quality preferences while reducing reliance on human labor. The proposed method aims to filter out noise and ensure diversity in the collected data, ultimately enhancing the effectiveness of RLHF in aligning large language models with human preferences .


How were the experiments in the paper designed?

The experiments in the paper were designed to evaluate the proposed data collection method at different stages . The experiments aimed to demonstrate the effectiveness of the framework by conducting comprehensive tests on the preference data collected . Two SFT models of varying sizes (13B and 65B) were used as the base model, with the architecture built upon LLaMA . The preference data were collected from two sources, including available open-source preference data . The experiments involved training two RMs based on the data collected at different steps, specifically steps 3 and 4, to assess the refinement of preference data and its impact on performance . The results of the experiments on preference benchmarks validated the improvement from step 3 to step 4, indicating the enhancement in performance with the refinement of data quality . Additionally, the experiments integrated the trained RMs with the BoN reranking policy to select the best answers based on RM scores, further verifying the performance enhancement with refined preference data .


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

The dataset used for quantitative evaluation in the study is based on preference benchmarks such as Anthropic Helpfulness, OpenAI Summarize, OpenAI WebGPT, and Stanford SHP . The code for the study is not explicitly mentioned as open source in the provided context.


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 proposes a comprehensive framework for preference data collection for training reward models, decomposing the process into four key steps: Prompt Generation, Response Generation, Response Filtering, and Human Labeling . Through conducting experiments at different stages based on the collected data, the study demonstrates the effectiveness of the proposed data collection method .

The framework ensures the collection of high-quality preferences while reducing the reliance on human labor, addressing concerns about noise in preference data and ensuring diversity in the collected data . By structuring the data collection process into incremental steps and incorporating AI filtering with human intervention, the study effectively reflects human preferences while reducing the amount of human labor required .

Furthermore, the study highlights the importance of refining the collected data through Response Filtering, where useless training samples are filtered out before being sent to annotators . This step involves scoring each instance using in-context learning techniques and employing GPT-4 to filter out low-quality responses, enhancing the efficiency of the labeling process .

Overall, the experiments and results in the paper provide robust evidence supporting the effectiveness of the proposed framework for preference data collection, demonstrating performance enhancement as data progresses through different stages and filtering strategies .


What are the contributions of this paper?

The paper makes several key contributions in the field of preference data collection for training reward models:

  • Proposing a comprehensive framework: The paper introduces a structured approach for preference data collection, dividing the process into four incremental steps: Prompt Generation, Response Generation, Response Filtering, and Human Labeling .
  • Enhancing data quality: By decomposing the data collection process into these steps, the framework ensures the collection of high-quality preferences while reducing the reliance on human labor .
  • Demonstrating effectiveness: Comprehensive experiments conducted at different stages of data collection demonstrate the effectiveness of the proposed method in enhancing performance through preference data collected at various stages .
  • Addressing noise in preference data: The paper highlights the importance of filtering out noise in preference data used for training reward models, emphasizing the need for high-quality data to optimize reward models effectively .

What work can be continued in depth?

To further advance the research in the field of collecting preference data for training reward models, several areas can be explored in depth:

  1. Enhancing Data Collection Framework: Future work can focus on refining the proposed framework for collecting high-quality preference data. This could involve optimizing the existing sub-steps such as Prompt Generation, Response Generation, Response Filtering, and Human Labeling to ensure the effectiveness and efficiency of the data collection process .

  2. Exploring Data Diversity: Research can delve into strategies to enhance the diversity of preference data collected for training reward models. This could involve investigating methods to generate diverse responses that challenge the model's generalization capabilities, thereby improving the quality of the collected data .

  3. Utilizing Advanced Models: Further studies could explore the use of advanced language models, such as larger-sized models like GPT-4, to generate responses for training data. By leveraging stronger models, researchers can ensure that the generated responses are of superior quality, aligning with the goal of training effective reward models .

  4. Refinement through Filtering: Future work can focus on refining the response filtering process by incorporating techniques like in-context learning and utilizing models like GPT-4 to filter out irrelevant or low-quality training samples. This step aims to improve the overall quality of the preference data used for training reward models .

By delving deeper into these areas, researchers can further advance the understanding and effectiveness of collecting preference data for training reward models, ultimately contributing to the optimization of reinforcement learning processes in the context of human feedback .

Tables

1

Introduction
Background
Evolution of RLHF in LLM alignment
Importance of high-quality human feedback
Objective
To develop a comprehensive framework for efficient and noise-reduced preference data collection
Align large language models with human preferences
Method
Prompt Generation
Techniques for creating clear and concise prompts
Customization for different domains and tasks
Response Generation using Stronger Models
Utilization of SFT and GPT-4 for response generation
Comparison with baseline models
Data Filtering
AI Filtering
Noise reduction techniques using AI algorithms
Criteria for filtering responses
Diversity and Quality Assurance
Ensuring a diverse range of preferences
Quality control measures
Human Labeling
Involvement of human annotators for final validation
Efficiency improvements through AI-assisted labeling
Experimentation
Performance evaluation with each step
Preference benchmarks and Best-of-N experiments
Results and Evaluation
Improved performance with each step of the framework
Quantitative analysis of data quality and model alignment
Applications and Limitations
Use cases in resource management and specific verticals
Non-ideal for quick data generation scenarios
Challenges and Future Directions
Large language model alignment techniques (Chinese models, instruction-following)
Integration with reinforcement learning for more complex tasks
Conclusion
Summary of the framework's contributions
Implications for the advancement of LLM alignment and RLHF research
Basic info
papers
artificial intelligence
Advanced features
Insights
In what scenarios or applications does the framework demonstrate improved performance?
What are the key steps involved in the presented framework for preference data collection?
What is the primary focus of the paper on collecting human feedback in RLHF?
How does the framework address noise and efficiency in the process?

Towards Comprehensive Preference Data Collection for Reward Modeling

Yulan Hu, Qingyang Li, Sheng Ouyang, Ge Chen, Kaihui Chen, Lijun Mei, Xucheng Ye, Fuzheng Zhang, Yong Liu·June 24, 2024

Summary

The paper presents a comprehensive framework for collecting high-quality preference data in Reinforcement Learning from Human Feedback (RLHF) for training reward models in aligning large language models with human preferences. The framework consists of four steps: prompt generation, response generation using stronger models like SFT or GPT-4, filtering to ensure diversity and quality, and human labeling. It addresses noise and efficiency by using AI filtering and minimizing labor. Experiments show improved performance with each step, with preference benchmarks and Best-of-N experiments demonstrating the effectiveness of the refined data. The framework is particularly useful for later stages of resource management and specific verticals, but may not be ideal for quick data generation. The research also explores various techniques, challenges, and applications in large language model alignment, including Chinese models, instruction-following, and reinforcement learning.
Mind map
Preference benchmarks and Best-of-N experiments
Performance evaluation with each step
Quality control measures
Ensuring a diverse range of preferences
Criteria for filtering responses
Noise reduction techniques using AI algorithms
Integration with reinforcement learning for more complex tasks
Large language model alignment techniques (Chinese models, instruction-following)
Non-ideal for quick data generation scenarios
Use cases in resource management and specific verticals
Experimentation
Diversity and Quality Assurance
AI Filtering
Comparison with baseline models
Utilization of SFT and GPT-4 for response generation
Customization for different domains and tasks
Techniques for creating clear and concise prompts
Align large language models with human preferences
To develop a comprehensive framework for efficient and noise-reduced preference data collection
Importance of high-quality human feedback
Evolution of RLHF in LLM alignment
Implications for the advancement of LLM alignment and RLHF research
Summary of the framework's contributions
Challenges and Future Directions
Applications and Limitations
Human Labeling
Data Filtering
Response Generation using Stronger Models
Prompt Generation
Objective
Background
Conclusion
Results and Evaluation
Method
Introduction
Outline
Introduction
Background
Evolution of RLHF in LLM alignment
Importance of high-quality human feedback
Objective
To develop a comprehensive framework for efficient and noise-reduced preference data collection
Align large language models with human preferences
Method
Prompt Generation
Techniques for creating clear and concise prompts
Customization for different domains and tasks
Response Generation using Stronger Models
Utilization of SFT and GPT-4 for response generation
Comparison with baseline models
Data Filtering
AI Filtering
Noise reduction techniques using AI algorithms
Criteria for filtering responses
Diversity and Quality Assurance
Ensuring a diverse range of preferences
Quality control measures
Human Labeling
Involvement of human annotators for final validation
Efficiency improvements through AI-assisted labeling
Experimentation
Performance evaluation with each step
Preference benchmarks and Best-of-N experiments
Results and Evaluation
Improved performance with each step of the framework
Quantitative analysis of data quality and model alignment
Applications and Limitations
Use cases in resource management and specific verticals
Non-ideal for quick data generation scenarios
Challenges and Future Directions
Large language model alignment techniques (Chinese models, instruction-following)
Integration with reinforcement learning for more complex tasks
Conclusion
Summary of the framework's contributions
Implications for the advancement of LLM alignment and RLHF research
Key findings
3

Paper digest

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

The paper aims to address the challenge of collecting high-quality preference data for training reward models (RMs) . This problem is highlighted as previous technical reports and research studies have not extensively analyzed the collection of preference data specifically for RM training . The proposed framework in the paper focuses on decomposing the preference data collection process into four sub-steps: Prompt Generation, Response Generation, Response Filtering, and Human Labeling, to ensure the quality of the collected data for RM training . This problem of collecting high-quality preference data for RMs is a significant focus of the paper, indicating a new and important area of research within the field of reward modeling.


What scientific hypothesis does this paper seek to validate?

This paper aims to validate a scientific hypothesis related to the collection of preference data for training reward models (RMs) in the context of Reinforcement Learning from Human Feedback (RLHF) . The study proposes a comprehensive framework for preference data collection, focusing on four key sub-steps: Prompt Generation, Response Generation, Response Filtering, and Human Labeling . The goal is to ensure the high-quality collection of preferences while reducing the reliance on human labor . The framework is designed to enhance the alignment of Large Language Models (LLMs) with human preferences by improving the quality of responses generated through reinforcement learning .


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

The paper "Towards Comprehensive Preference Data Collection for Reward Modeling" proposes a novel framework for collecting high-quality preference data for training reward models (RMs) . This framework consists of four key sub-steps:

  1. Prompt Generation: Selects challenging prompts that the SFT model struggles to handle, ensuring the generation of diverse and high-quality prompts .
  2. Response Generation: Produces varied responses to enhance model generalization, with a focus on generating responses that are superior to the current model being optimized. This involves using stronger models like GPT-4 to generate responses .
  3. Response Filtering: Involves filtering the generated responses to ensure the training candidate set contains instances that provide a supervisory signal for RM training. GPT-4 is utilized to score each instance in the training set based on response quality, with a five-level scoring criteria to refine the dataset before human labeling .
  4. Human Labeling: Annotates a modest amount of pseudo preference data, ensuring that the RM is trained on high-quality data reviewed by human labelers .

The paper also discusses the limitations of the proposed framework, particularly highlighting the challenge of long-term data production due to the extensive filtering required at each step of the data collection process . Additionally, it emphasizes the importance of combining AI filtering with human intervention to effectively reflect human preferences while reducing the amount of human labor required for data collection . The proposed framework for collecting preference data for training reward models (RMs) offers several key characteristics and advantages compared to previous methods outlined in the paper "Towards Comprehensive Preference Data Collection for Reward Modeling" .

  1. Structured Approach: The framework decomposes the preference data collection process into four sub-steps: Prompt Generation, Response Generation, Response Filtering, and Human Labeling. This structured approach ensures the gathering of high-quality preferences while reducing reliance on human labor .

  2. Quality Improvement: By utilizing stronger models like GPT-4 for response generation and filtering, the framework aims to enhance the quality of the collected preference data. This results in improved data quality, as demonstrated by the results showing enhancements in performance with refined preference data .

  3. Diversity and Supervisory Signal: The framework emphasizes the importance of ensuring diversity in responses and the presence of a supervisory signal in the collected data. It addresses the challenge of generating diverse responses by combining results from various models and employing off-the-shelf strong models. Additionally, it filters out useless training samples using GPT-4 based on a five-level scoring criteria to refine the dataset before human labeling .

  4. Reduction of Human Labor: By integrating AI filtering with human intervention, the framework effectively reflects human preferences while significantly reducing the amount of human labor required for data collection. This reduction in human labor is achieved through the incorporation of AI filtering techniques and the annotation of a modest amount of pseudo preference data .

  5. Validation and Benchmarking: The framework's effectiveness is validated using preference data benchmarks and policy learning, showcasing improvements in data quality. The results demonstrate the enhancement in performance with the refinement of preference data, as illustrated by the win rates of the reward model trained with preference data in different steps .

Overall, the proposed framework offers a systematic and effective approach to collecting high-quality preference data for training reward models, addressing key challenges and limitations of previous methods while emphasizing the importance of data quality and reducing human labor involvement in the process .


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 papers exist in the field of reinforcement learning from human feedback (RLHF) and reward modeling. Noteworthy researchers in this field include Reiichiro Nakano, Jacob Hilton, Suchir Balaji, Jeff Wu, Long Ouyang, Christina Kim, Christopher Hesse, Shantanu Jain, Vineet Kosaraju, William Saunders, Alexandre Ramé, Nino Vieillard, Léonard Hussenot, Robert Dadashi, Geoffrey Cideron, Olivier Bachem, Johan Ferret, and many others .

The key to the solution mentioned in the paper is a comprehensive framework for preference data collection, which is divided into four incremental steps: Prompt Generation, Response Generation, Response Filtering, and Human Labeling. This structured approach ensures the collection of high-quality preferences while reducing reliance on human labor. The proposed method aims to filter out noise and ensure diversity in the collected data, ultimately enhancing the effectiveness of RLHF in aligning large language models with human preferences .


How were the experiments in the paper designed?

The experiments in the paper were designed to evaluate the proposed data collection method at different stages . The experiments aimed to demonstrate the effectiveness of the framework by conducting comprehensive tests on the preference data collected . Two SFT models of varying sizes (13B and 65B) were used as the base model, with the architecture built upon LLaMA . The preference data were collected from two sources, including available open-source preference data . The experiments involved training two RMs based on the data collected at different steps, specifically steps 3 and 4, to assess the refinement of preference data and its impact on performance . The results of the experiments on preference benchmarks validated the improvement from step 3 to step 4, indicating the enhancement in performance with the refinement of data quality . Additionally, the experiments integrated the trained RMs with the BoN reranking policy to select the best answers based on RM scores, further verifying the performance enhancement with refined preference data .


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

The dataset used for quantitative evaluation in the study is based on preference benchmarks such as Anthropic Helpfulness, OpenAI Summarize, OpenAI WebGPT, and Stanford SHP . The code for the study is not explicitly mentioned as open source in the provided context.


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 proposes a comprehensive framework for preference data collection for training reward models, decomposing the process into four key steps: Prompt Generation, Response Generation, Response Filtering, and Human Labeling . Through conducting experiments at different stages based on the collected data, the study demonstrates the effectiveness of the proposed data collection method .

The framework ensures the collection of high-quality preferences while reducing the reliance on human labor, addressing concerns about noise in preference data and ensuring diversity in the collected data . By structuring the data collection process into incremental steps and incorporating AI filtering with human intervention, the study effectively reflects human preferences while reducing the amount of human labor required .

Furthermore, the study highlights the importance of refining the collected data through Response Filtering, where useless training samples are filtered out before being sent to annotators . This step involves scoring each instance using in-context learning techniques and employing GPT-4 to filter out low-quality responses, enhancing the efficiency of the labeling process .

Overall, the experiments and results in the paper provide robust evidence supporting the effectiveness of the proposed framework for preference data collection, demonstrating performance enhancement as data progresses through different stages and filtering strategies .


What are the contributions of this paper?

The paper makes several key contributions in the field of preference data collection for training reward models:

  • Proposing a comprehensive framework: The paper introduces a structured approach for preference data collection, dividing the process into four incremental steps: Prompt Generation, Response Generation, Response Filtering, and Human Labeling .
  • Enhancing data quality: By decomposing the data collection process into these steps, the framework ensures the collection of high-quality preferences while reducing the reliance on human labor .
  • Demonstrating effectiveness: Comprehensive experiments conducted at different stages of data collection demonstrate the effectiveness of the proposed method in enhancing performance through preference data collected at various stages .
  • Addressing noise in preference data: The paper highlights the importance of filtering out noise in preference data used for training reward models, emphasizing the need for high-quality data to optimize reward models effectively .

What work can be continued in depth?

To further advance the research in the field of collecting preference data for training reward models, several areas can be explored in depth:

  1. Enhancing Data Collection Framework: Future work can focus on refining the proposed framework for collecting high-quality preference data. This could involve optimizing the existing sub-steps such as Prompt Generation, Response Generation, Response Filtering, and Human Labeling to ensure the effectiveness and efficiency of the data collection process .

  2. Exploring Data Diversity: Research can delve into strategies to enhance the diversity of preference data collected for training reward models. This could involve investigating methods to generate diverse responses that challenge the model's generalization capabilities, thereby improving the quality of the collected data .

  3. Utilizing Advanced Models: Further studies could explore the use of advanced language models, such as larger-sized models like GPT-4, to generate responses for training data. By leveraging stronger models, researchers can ensure that the generated responses are of superior quality, aligning with the goal of training effective reward models .

  4. Refinement through Filtering: Future work can focus on refining the response filtering process by incorporating techniques like in-context learning and utilizing models like GPT-4 to filter out irrelevant or low-quality training samples. This step aims to improve the overall quality of the preference data used for training reward models .

By delving deeper into these areas, researchers can further advance the understanding and effectiveness of collecting preference data for training reward models, ultimately contributing to the optimization of reinforcement learning processes in the context of human feedback .

Tables
1
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