Transcendence: Generative Models Can Outperform The Experts That Train Them

Edwin Zhang, Vincent Zhu, Naomi Saphra, Anat Kleiman, Benjamin L. Edelman, Milind Tambe, Sham M. Kakade, Eran Malach·June 17, 2024

Summary

This paper investigates the concept of "transcendence" in generative models, where AI surpasses human performance, using the example of an autoregressive transformer trained on chess games. Low-temperature sampling is found to be crucial for achieving transcendence, as it enables the model to outperform individual human experts by combining and denoising expert knowledge. The study presents two theorems that establish conditions for transcendence, both in the case of a single noisy expert and multiple experts. Experiments with ChessFormer models demonstrate the model's ability to transcend human-level performance, particularly when trained on diverse datasets. The research also connects transcendence to ensemble learning and offline reinforcement learning, highlighting the importance of dataset diversity for optimal performance. The study suggests future research in various domains and addresses the implications of AI models surpassing human abilities, while emphasizing the role of denoising in decision-making.

Key findings

8

Paper digest

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

The paper aims to address the concept of transcendence in generative models, where these models trained on expert data can outperform the individual human experts who provided the training data . This concept involves generative models surpassing the expertise of their human trainers, showcasing the potential for models to achieve superior performance compared to the experts who trained them. While the idea of generative models outperforming their trainers is not entirely new, the paper delves into the specific mechanisms, such as low-temperature sampling, that enable this transcendence, contributing to a deeper understanding of this phenomenon .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the scientific hypothesis of transcendence in generative models, which refers to the phenomenon where these models trained on expert data can outperform the individual human experts who generated the training data . The study explores how generative models can achieve capabilities that surpass those of the human experts who trained them, particularly focusing on the role of low-temperature sampling in enabling transcendence by denoising expert biases and consolidating diverse knowledge . The research delves into the implications of this hypothesis across various domains beyond chess, such as natural language processing and computer vision, to understand the generalizability of these findings .


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

The paper "Transcendence: Generative Models Can Outperform The Experts That Train Them" introduces the concept of transcendence in generative models, where these models trained on expert data can outperform individual experts . The key idea proposed in the paper is that low-temperature sampling is crucial for achieving transcendence by denoising expert biases and consolidating diverse knowledge . This denoising effect helps the models surpass the performance of the players who produced their training data, showcasing the potential for generative models to exceed human expertise .

Furthermore, the paper discusses the importance of dataset diversity for transcendence, emphasizing the role of varied expert perspectives in training generative models . It also highlights the connection to ensemble distillation methods, where models are trained with additional objectives to match a variety of weaker teacher models, and ensemble self-training approaches that train a learner directly on labels produced by varied teachers . These methods contribute to the improvement of models by leveraging diverse training data and perspectives.

Moreover, the paper draws connections to offline reinforcement learning settings, where the goal is to learn a new policy that improves upon a fixed dataset generated by a behavior policy . While the paper's focus is on imitation learning rather than explicit reinforcement learning objectives, it emphasizes the importance of avoiding training instabilities and reward labels by using a pure imitation or self-supervised learning objective . This approach enables the models to learn from expert data without introducing potential training challenges associated with reinforcement learning objectives.

In addition, the paper discusses the potential implications of transcendence in various domains beyond chess, such as natural language processing, computer vision, and text-to-video applications . By exploring transcendence in different contexts, the paper aims to understand the generalizability of the findings and the practical implementations of leveraging generative models to exceed human expertise across diverse applications . This broader exploration opens up new avenues for research and application of generative models in various domains beyond chess. The paper "Transcendence: Generative Models Can Outperform The Experts That Train Them" introduces the concept of transcendence in generative models, where these models trained on expert data can outperform individual experts . One key characteristic of this approach is the emphasis on low-temperature sampling to denoise expert biases and consolidate diverse knowledge, enabling models to surpass the performance of the players who produced their training data . This denoising effect is crucial for improving model performance by moving probability mass towards better moves in specific play contexts, ultimately enhancing decision-making processes .

Compared to previous methods, the paper highlights the importance of dataset diversity for transcendence, showcasing the role of varied expert perspectives in training generative models . The utilization of ensemble distillation methods, where models are trained with additional objectives to match a variety of weaker teacher models, and ensemble self-training approaches that train a learner directly on labels produced by varied teachers, further enhances model performance by leveraging diverse training data and perspectives . These methods contribute to the improvement of models by incorporating a range of expert insights and knowledge.

Moreover, the paper draws connections to offline reinforcement learning settings, where the goal is to learn a new policy that improves upon a fixed dataset generated by a behavior policy . Unlike traditional reinforcement learning objectives, the paper's focus on imitation learning avoids potential training instabilities and reward label assumptions, highlighting the benefits of using a pure imitation or self-supervised learning objective to train generative models . This approach enables models to learn from expert data effectively without introducing complexities associated with reinforcement learning objectives.

Additionally, the paper explores the implications of transcendence in various domains beyond chess, such as natural language processing, computer vision, and text-to-video applications . By investigating transcendence in diverse contexts, the paper aims to understand the generalizability of the findings and the practical implementations of leveraging generative models to exceed human expertise across a wide range of applications . This broader exploration opens up new avenues for research and application of generative models in various domains beyond chess, showcasing the potential for advancements in diverse fields through transcendence.


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 discussed in the paper. Noteworthy researchers in this area include A. Karpathy , A. Turing , D. Silver , and L. Breiman . These researchers have contributed significantly to the development of generative models and reinforcement learning in the context of chess and AI.

The key to the solution mentioned in the paper is the concept of "transcendence," where generative models trained on expert data outperform the best individual experts. This is achieved through low-temperature sampling, which helps denoise expert biases and consolidate diverse knowledge . The paper emphasizes the importance of dataset diversity for achieving transcendence, highlighting the role of varied expert perspectives in training models that surpass the performance of the experts who generated the training data.


How were the experiments in the paper designed?

The experiments in the paper were designed to evaluate the predictive power of the transcendence concept by modeling and training chess players using autoregressive transformer decoders . The experimental setup involved training several 50M parameter autoregressive transformer decoders on human chess games from the lichess.org open-source database . The dataset consisted of approximately one billion games, and during training, the models only saw data up to a certain rating, limiting exposure to expert players above a specified score . The models were trained on the next-token prediction objective using Portable Game Notation (PGN) strings to represent chess games . The experiments focused on testing for transcendence by training models to surpass the performance of the players who produced their training data . The Glicko-2 rating system was used for evaluation, and the experiments aimed to demonstrate how generative models can outperform individual experts by consolidating diverse knowledge and denoising expert biases through low-temperature sampling .


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

The dataset used for quantitative evaluation in the study is human chess games from the lichess.org open source database from January 2023 to October 2023, which contains approximately one billion games . The code used in the study is open source and can be accessed on the website provided by the authors at https://transcendence.eddie.win .


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 substantial support for the scientific hypotheses that needed verification. The paper introduces the concept of transcendence, where generative models trained on expert data outperform individual experts . The experimental setup involved training autoregressive transformer decoders on a dataset of human chess games from lichess.org, containing approximately one billion games . The models were trained without direct access to the board state and solely based on the moves and outcomes of the games .

The primary research question of whether low-temperature sampling can induce transcendence in practice was rigorously addressed through the experiments . The results definitively confirmed the existence of transcendence, showing that under low-temperature sampling, the models were able to surpass the performance of the players who produced their training data . This empirical validation of the theoretical framework laid out in the paper demonstrates the effectiveness of low-temperature sampling in achieving transcendence .

Furthermore, the paper discusses the necessity of dataset diversity for transcendence and emphasizes the role of varied expert perspectives in achieving superior performance . The experiments conducted on chess models showcased how diverse training data and low-temperature sampling can lead to models outperforming individual experts, supporting the theoretical claims made in the paper .

Overall, the experiments and results presented in the paper provide strong empirical evidence supporting the scientific hypotheses related to transcendence in generative models trained on expert data. The findings highlight the potential of leveraging generative models to exceed human expertise across various applications, pushing the boundaries of what these models can achieve .


What are the contributions of this paper?

The paper makes several key contributions:

  • It introduces the concept of transcendence in generative models, where models trained on expert data can outperform individual experts .
  • The theoretical analysis highlights the importance of low-temperature sampling in achieving transcendence by denoising expert biases and consolidating diverse knowledge .
  • The paper validates its findings empirically by training several chess models that, under low-temperature sampling, surpass the performance of the players who produced their training data .
  • It emphasizes the necessity of dataset diversity for transcendence, stressing the role of varied expert perspectives in enhancing model performance .
  • The work also discusses the practical implementations of transcendence and ethical considerations in the context of deployed generative models, aiming to leverage these models to exceed human expertise across diverse applications .

What work can be continued in depth?

Future research in the field of generative models can be expanded in several directions based on the existing work:

  • Investigating transcendence in various domains: Future research can explore transcendence beyond chess into areas like natural language processing, computer vision, and text-to-video to assess the generalizability of the findings .
  • Practical implementations and ethical considerations: Further work could delve into the practical applications of transcendence and ethical implications in the broader context of deployed generative models .
  • Dataset diversity and expert perspectives: Emphasizing the necessity of diverse datasets for transcendence, future studies can focus on the role of varied expert perspectives in enhancing generative models .
  • Connection to Offline Reinforcement Learning: Drawing parallels to Offline Reinforcement Learning, exploring Decision Transformer models, and investigating transcendence under different conditions can be promising avenues for further research .
  • Label Disagreement and Training Data Improvement: Research can delve into how label disagreement in training data can enhance model performance, building on the concept that learners can improve on diverse original labelers .
  • Exploration of Low-Temperature Sampling: Further exploration of the impact of low-temperature sampling on model performance and transcendence, drawing insights from Reinforcement Learning practices, can be a valuable area of study .
  • Enhancing Model Generalization: Investigating methods like ensemble distillation and ensemble self-training to improve model generalization and performance over expert-generated training data can be a fruitful direction for future research .

Introduction
Background
Evolution of AI in chess
Milestones in AI surpassing human performance
Objective
To define and analyze the concept of transcendence in AI
Investigate the role of low-temperature sampling in achieving transcendence
Establish conditions for transcendence in generative models
Theoretical Framework
Overview of autoregressive transformers and their application in chess
Connection to ensemble learning and offline reinforcement learning
Method
Data Collection
Chess game dataset preparation
Diverse datasets and their impact on model performance
Data Preprocessing
Low-temperature sampling techniques
Expert knowledge extraction and denoising
Theorems
Single Noisy Expert Theorem
Multiple Experts Theorem
Model Development
ChessFormer architecture and training process
Experimental setup and evaluation metrics
Results
Model performance surpassing human experts
Impact of dataset diversity on model transcendence
Case studies with ChessFormer models
Discussion
Connection to ensemble learning and decision-making
Future research directions in AI transcendence
Ethical and societal implications of AI surpassing human abilities
Denoising Mechanisms
The role of denoising in enhancing AI performance
Limitations and challenges in achieving transcendence
Conclusion
Summary of key findings
Implications for generative model development and AI ethics
Open questions and future research goals
Basic info
papers
machine learning
artificial intelligence
Advanced features
Insights
What are the two theorems presented in the study regarding transcendence in AI models?
What does the paper focus on in terms of generative models and AI performance?
How does the research connect transcendence to ensemble learning and offline reinforcement learning?
How does low-temperature sampling contribute to the model's performance in the context of chess games?

Transcendence: Generative Models Can Outperform The Experts That Train Them

Edwin Zhang, Vincent Zhu, Naomi Saphra, Anat Kleiman, Benjamin L. Edelman, Milind Tambe, Sham M. Kakade, Eran Malach·June 17, 2024

Summary

This paper investigates the concept of "transcendence" in generative models, where AI surpasses human performance, using the example of an autoregressive transformer trained on chess games. Low-temperature sampling is found to be crucial for achieving transcendence, as it enables the model to outperform individual human experts by combining and denoising expert knowledge. The study presents two theorems that establish conditions for transcendence, both in the case of a single noisy expert and multiple experts. Experiments with ChessFormer models demonstrate the model's ability to transcend human-level performance, particularly when trained on diverse datasets. The research also connects transcendence to ensemble learning and offline reinforcement learning, highlighting the importance of dataset diversity for optimal performance. The study suggests future research in various domains and addresses the implications of AI models surpassing human abilities, while emphasizing the role of denoising in decision-making.
Mind map
Multiple Experts Theorem
Single Noisy Expert Theorem
Limitations and challenges in achieving transcendence
The role of denoising in enhancing AI performance
Experimental setup and evaluation metrics
ChessFormer architecture and training process
Theorems
Diverse datasets and their impact on model performance
Chess game dataset preparation
Connection to ensemble learning and offline reinforcement learning
Overview of autoregressive transformers and their application in chess
Establish conditions for transcendence in generative models
Investigate the role of low-temperature sampling in achieving transcendence
To define and analyze the concept of transcendence in AI
Milestones in AI surpassing human performance
Evolution of AI in chess
Open questions and future research goals
Implications for generative model development and AI ethics
Summary of key findings
Denoising Mechanisms
Case studies with ChessFormer models
Impact of dataset diversity on model transcendence
Model performance surpassing human experts
Model Development
Data Preprocessing
Data Collection
Theoretical Framework
Objective
Background
Conclusion
Discussion
Results
Method
Introduction
Outline
Introduction
Background
Evolution of AI in chess
Milestones in AI surpassing human performance
Objective
To define and analyze the concept of transcendence in AI
Investigate the role of low-temperature sampling in achieving transcendence
Establish conditions for transcendence in generative models
Theoretical Framework
Overview of autoregressive transformers and their application in chess
Connection to ensemble learning and offline reinforcement learning
Method
Data Collection
Chess game dataset preparation
Diverse datasets and their impact on model performance
Data Preprocessing
Low-temperature sampling techniques
Expert knowledge extraction and denoising
Theorems
Single Noisy Expert Theorem
Multiple Experts Theorem
Model Development
ChessFormer architecture and training process
Experimental setup and evaluation metrics
Results
Model performance surpassing human experts
Impact of dataset diversity on model transcendence
Case studies with ChessFormer models
Discussion
Connection to ensemble learning and decision-making
Future research directions in AI transcendence
Ethical and societal implications of AI surpassing human abilities
Denoising Mechanisms
The role of denoising in enhancing AI performance
Limitations and challenges in achieving transcendence
Conclusion
Summary of key findings
Implications for generative model development and AI ethics
Open questions and future research goals
Key findings
8

Paper digest

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

The paper aims to address the concept of transcendence in generative models, where these models trained on expert data can outperform the individual human experts who provided the training data . This concept involves generative models surpassing the expertise of their human trainers, showcasing the potential for models to achieve superior performance compared to the experts who trained them. While the idea of generative models outperforming their trainers is not entirely new, the paper delves into the specific mechanisms, such as low-temperature sampling, that enable this transcendence, contributing to a deeper understanding of this phenomenon .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the scientific hypothesis of transcendence in generative models, which refers to the phenomenon where these models trained on expert data can outperform the individual human experts who generated the training data . The study explores how generative models can achieve capabilities that surpass those of the human experts who trained them, particularly focusing on the role of low-temperature sampling in enabling transcendence by denoising expert biases and consolidating diverse knowledge . The research delves into the implications of this hypothesis across various domains beyond chess, such as natural language processing and computer vision, to understand the generalizability of these findings .


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

The paper "Transcendence: Generative Models Can Outperform The Experts That Train Them" introduces the concept of transcendence in generative models, where these models trained on expert data can outperform individual experts . The key idea proposed in the paper is that low-temperature sampling is crucial for achieving transcendence by denoising expert biases and consolidating diverse knowledge . This denoising effect helps the models surpass the performance of the players who produced their training data, showcasing the potential for generative models to exceed human expertise .

Furthermore, the paper discusses the importance of dataset diversity for transcendence, emphasizing the role of varied expert perspectives in training generative models . It also highlights the connection to ensemble distillation methods, where models are trained with additional objectives to match a variety of weaker teacher models, and ensemble self-training approaches that train a learner directly on labels produced by varied teachers . These methods contribute to the improvement of models by leveraging diverse training data and perspectives.

Moreover, the paper draws connections to offline reinforcement learning settings, where the goal is to learn a new policy that improves upon a fixed dataset generated by a behavior policy . While the paper's focus is on imitation learning rather than explicit reinforcement learning objectives, it emphasizes the importance of avoiding training instabilities and reward labels by using a pure imitation or self-supervised learning objective . This approach enables the models to learn from expert data without introducing potential training challenges associated with reinforcement learning objectives.

In addition, the paper discusses the potential implications of transcendence in various domains beyond chess, such as natural language processing, computer vision, and text-to-video applications . By exploring transcendence in different contexts, the paper aims to understand the generalizability of the findings and the practical implementations of leveraging generative models to exceed human expertise across diverse applications . This broader exploration opens up new avenues for research and application of generative models in various domains beyond chess. The paper "Transcendence: Generative Models Can Outperform The Experts That Train Them" introduces the concept of transcendence in generative models, where these models trained on expert data can outperform individual experts . One key characteristic of this approach is the emphasis on low-temperature sampling to denoise expert biases and consolidate diverse knowledge, enabling models to surpass the performance of the players who produced their training data . This denoising effect is crucial for improving model performance by moving probability mass towards better moves in specific play contexts, ultimately enhancing decision-making processes .

Compared to previous methods, the paper highlights the importance of dataset diversity for transcendence, showcasing the role of varied expert perspectives in training generative models . The utilization of ensemble distillation methods, where models are trained with additional objectives to match a variety of weaker teacher models, and ensemble self-training approaches that train a learner directly on labels produced by varied teachers, further enhances model performance by leveraging diverse training data and perspectives . These methods contribute to the improvement of models by incorporating a range of expert insights and knowledge.

Moreover, the paper draws connections to offline reinforcement learning settings, where the goal is to learn a new policy that improves upon a fixed dataset generated by a behavior policy . Unlike traditional reinforcement learning objectives, the paper's focus on imitation learning avoids potential training instabilities and reward label assumptions, highlighting the benefits of using a pure imitation or self-supervised learning objective to train generative models . This approach enables models to learn from expert data effectively without introducing complexities associated with reinforcement learning objectives.

Additionally, the paper explores the implications of transcendence in various domains beyond chess, such as natural language processing, computer vision, and text-to-video applications . By investigating transcendence in diverse contexts, the paper aims to understand the generalizability of the findings and the practical implementations of leveraging generative models to exceed human expertise across a wide range of applications . This broader exploration opens up new avenues for research and application of generative models in various domains beyond chess, showcasing the potential for advancements in diverse fields through transcendence.


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 discussed in the paper. Noteworthy researchers in this area include A. Karpathy , A. Turing , D. Silver , and L. Breiman . These researchers have contributed significantly to the development of generative models and reinforcement learning in the context of chess and AI.

The key to the solution mentioned in the paper is the concept of "transcendence," where generative models trained on expert data outperform the best individual experts. This is achieved through low-temperature sampling, which helps denoise expert biases and consolidate diverse knowledge . The paper emphasizes the importance of dataset diversity for achieving transcendence, highlighting the role of varied expert perspectives in training models that surpass the performance of the experts who generated the training data.


How were the experiments in the paper designed?

The experiments in the paper were designed to evaluate the predictive power of the transcendence concept by modeling and training chess players using autoregressive transformer decoders . The experimental setup involved training several 50M parameter autoregressive transformer decoders on human chess games from the lichess.org open-source database . The dataset consisted of approximately one billion games, and during training, the models only saw data up to a certain rating, limiting exposure to expert players above a specified score . The models were trained on the next-token prediction objective using Portable Game Notation (PGN) strings to represent chess games . The experiments focused on testing for transcendence by training models to surpass the performance of the players who produced their training data . The Glicko-2 rating system was used for evaluation, and the experiments aimed to demonstrate how generative models can outperform individual experts by consolidating diverse knowledge and denoising expert biases through low-temperature sampling .


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

The dataset used for quantitative evaluation in the study is human chess games from the lichess.org open source database from January 2023 to October 2023, which contains approximately one billion games . The code used in the study is open source and can be accessed on the website provided by the authors at https://transcendence.eddie.win .


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 substantial support for the scientific hypotheses that needed verification. The paper introduces the concept of transcendence, where generative models trained on expert data outperform individual experts . The experimental setup involved training autoregressive transformer decoders on a dataset of human chess games from lichess.org, containing approximately one billion games . The models were trained without direct access to the board state and solely based on the moves and outcomes of the games .

The primary research question of whether low-temperature sampling can induce transcendence in practice was rigorously addressed through the experiments . The results definitively confirmed the existence of transcendence, showing that under low-temperature sampling, the models were able to surpass the performance of the players who produced their training data . This empirical validation of the theoretical framework laid out in the paper demonstrates the effectiveness of low-temperature sampling in achieving transcendence .

Furthermore, the paper discusses the necessity of dataset diversity for transcendence and emphasizes the role of varied expert perspectives in achieving superior performance . The experiments conducted on chess models showcased how diverse training data and low-temperature sampling can lead to models outperforming individual experts, supporting the theoretical claims made in the paper .

Overall, the experiments and results presented in the paper provide strong empirical evidence supporting the scientific hypotheses related to transcendence in generative models trained on expert data. The findings highlight the potential of leveraging generative models to exceed human expertise across various applications, pushing the boundaries of what these models can achieve .


What are the contributions of this paper?

The paper makes several key contributions:

  • It introduces the concept of transcendence in generative models, where models trained on expert data can outperform individual experts .
  • The theoretical analysis highlights the importance of low-temperature sampling in achieving transcendence by denoising expert biases and consolidating diverse knowledge .
  • The paper validates its findings empirically by training several chess models that, under low-temperature sampling, surpass the performance of the players who produced their training data .
  • It emphasizes the necessity of dataset diversity for transcendence, stressing the role of varied expert perspectives in enhancing model performance .
  • The work also discusses the practical implementations of transcendence and ethical considerations in the context of deployed generative models, aiming to leverage these models to exceed human expertise across diverse applications .

What work can be continued in depth?

Future research in the field of generative models can be expanded in several directions based on the existing work:

  • Investigating transcendence in various domains: Future research can explore transcendence beyond chess into areas like natural language processing, computer vision, and text-to-video to assess the generalizability of the findings .
  • Practical implementations and ethical considerations: Further work could delve into the practical applications of transcendence and ethical implications in the broader context of deployed generative models .
  • Dataset diversity and expert perspectives: Emphasizing the necessity of diverse datasets for transcendence, future studies can focus on the role of varied expert perspectives in enhancing generative models .
  • Connection to Offline Reinforcement Learning: Drawing parallels to Offline Reinforcement Learning, exploring Decision Transformer models, and investigating transcendence under different conditions can be promising avenues for further research .
  • Label Disagreement and Training Data Improvement: Research can delve into how label disagreement in training data can enhance model performance, building on the concept that learners can improve on diverse original labelers .
  • Exploration of Low-Temperature Sampling: Further exploration of the impact of low-temperature sampling on model performance and transcendence, drawing insights from Reinforcement Learning practices, can be a valuable area of study .
  • Enhancing Model Generalization: Investigating methods like ensemble distillation and ensemble self-training to improve model generalization and performance over expert-generated training data can be a fruitful direction for future research .
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