Inverse-RLignment: Inverse Reinforcement Learning from Demonstrations for LLM Alignment

Hao Sun, Mihaela van der Schaar·May 24, 2024

Summary

This paper presents Inverse Reinforcement Learning from Demonstrations (AfD), a novel method for aligning large language models (LLMs) that addresses the limitations of preference-based approaches. AfD leverages high-quality demonstration data to learn a reward model, reducing the need for noisy preferences and inductive biases. It operates within a sequential decision-making framework, using divergence minimization objectives and a computationally efficient algorithm to create a tailored reward model. Experiments on the Harmless and Helpful tasks show that AfD is effective and simpler than existing techniques, outperforming or matching preference-based methods in some cases. The study highlights the benefits of using demonstration data over preferences, including improved data quality, reduced annotation costs, and privacy concerns. The paper also explores the connection between AfD and other learning methods, such as behavior cloning and inverse reinforcement learning, and discusses the challenges and potential for future research in this area.

Paper digest

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

The paper aims to address the challenge of aligning Large Language Models (LLMs) by introducing a novel approach called Alignment from Demonstrations (AfD) . This approach leverages high-quality demonstration data to overcome issues associated with preference datasets, such as noisy labels, high annotation costs, and privacy concerns . The unique challenge highlighted in the paper is the absence of reward signals in the alignment process, which distinguishes AfD from traditional methods . While the problem of aligning LLMs is not new, the paper introduces a fresh perspective by proposing AfD as an alternative approach that utilizes demonstration data to enhance alignment performance and address the limitations of preference-based methods .


What scientific hypothesis does this paper seek to validate?

This paper seeks to validate the scientific hypothesis related to Inverse Reinforcement Learning (IRL) from Demonstrations for Large Language Model (LLM) Alignment . The research focuses on aligning language models with human intentions during response generation by learning a reward model inspired by IRL . The main objective is to improve language model alignment by deriving objectives from IRL literature and addressing challenges associated with preference-based alignment in LLMs . The study aims to explore the effectiveness of different setups of RL, Offline-RL, Imitation Learning, Inverse-RL, Learning from Demonstrations, and Preference-based RL in the context of LLM alignment .


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

The paper "Inverse-RLignment: Inverse Reinforcement Learning from Demonstrations for LLM Alignment" introduces several novel ideas, methods, and models in the context of aligning Large Language Models (LLMs) . Here are the key contributions of the paper:

  1. Alignment from Demonstrations (AfD): The paper proposes Alignment from Demonstrations as a novel approach to LLM alignment, leveraging high-quality demonstration data to overcome challenges like noisy labels, high annotation costs, and privacy concerns associated with preference-based datasets . AfD is formalized within a sequential decision-making framework, addressing the unique challenge of missing reward signals in alignment tasks .

  2. Divergence Minimization Objectives: Drawing insights from forward and inverse reinforcement learning, the paper introduces divergence minimization objectives for AfD. These objectives aim to improve language model alignment by learning a reward model inspired by Inverse Reinforcement Learning (IRL) .

  3. Analytical Insights: The paper elucidates the mass-covering and mode-seeking behaviors of various approaches in LLM alignment, explaining when and why certain methods are superior. This analytical framework helps in understanding the effectiveness of different alignment strategies .

  4. Computational Efficiency: The paper proposes a computationally efficient algorithm that extrapolates over a tailored reward model for AfD, enhancing the performance of LLMs aligned through this approach .

  5. Empirical Validation: Through experiments on tasks like Harmless and Helpful, the paper demonstrates the strong empirical performance of the proposed methods while maintaining simplicity. This validation showcases the practical effectiveness of the novel ideas and models introduced in the paper .

Overall, the paper presents a comprehensive framework for LLM alignment, focusing on the nuances of reward modeling and alignment objectives, distinct from traditional GAN-based text generation methods . The proposed AfD approach and divergence minimization objectives offer a fresh perspective on addressing challenges in aligning Large Language Models. The paper "Inverse-RLignment: Inverse Reinforcement Learning from Demonstrations for LLM Alignment" introduces a novel approach called Alignment from Demonstrations (AfD) for aligning Large Language Models (LLMs) . Here are the key characteristics and advantages of this approach compared to previous methods outlined in the paper:

  1. Alignment from Demonstrations (AfD):

    • Characteristics: AfD focuses on using expert demonstration datasets, which are more accessible and of higher quality compared to preference datasets commonly used in LLM alignment research . It formulates the alignment problem as a Markov Decision Process (MDP) and addresses the challenge of lacking reward signals in LLM alignment .
    • Advantages: AfD does not require continuous querying and comparison, does not rely on assumptions inherent in preference-based methods, and enables LLM alignment without the need for external annotators, making it applicable to private datasets locally .
  2. Reward Modeling:

    • Characteristics: The paper discusses different approaches to building reward models, such as Init-Demo RM, SFT-Demo RM, Init-SFT RM, and Preference-based RM (BT-RM) . Each approach has unique characteristics in utilizing samples generated by different policies for training the reward model.
    • Advantages: The Init-SFT RM approach, which uses samples generated by the initial policy and the fine-tuned policy, aims to avoid potential reward hacking caused by heterogeneous data in reward model training . This method is expected to outperform other choices when applying learned reward models at inference time .
  3. Empirical Validation:

    • Characteristics: The paper conducts experiments to validate the insights and methods proposed, demonstrating the efficacy of alignment from demonstrations and the proposed reward modeling method .
    • Advantages: The results underscore the effectiveness of building reward models using demonstration datasets, with the IRL RM using the Init-SFT approach achieving the highest win rates and scores compared to other models, even matching or surpassing preference-based reward models without the need for preference annotations .
  4. Trajectory Distribution Matching:

    • Characteristics: The paper introduces trajectory distribution matching objectives for AfD, connecting divergence measures with different algorithms . It explores an efficient Inverse RL algorithm for the AfD problem to enhance alignment performance .
    • Advantages: By focusing on trajectory distribution matching, AfD offers a unified objective framework for improving language model alignment, providing theoretical rationales and empirical evidence on leveraging demonstration datasets for aligning LLMs .

Overall, the AfD approach in the paper presents unique characteristics and advantages, such as higher data quality, reduced reliance on external annotators, and improved alignment performance compared to traditional preference-based methods in LLM alignment research.


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 and notable researchers in the field of Inverse Reinforcement Learning (IRL) and Language Model (LLM) Alignment have been identified:

  • Noteworthy researchers in this field include Ian Goodfellow, Yoshua Bengio, and Aaron Courville .
  • Other prominent researchers are Daniel Brown, Scott Niekum, and Joar Skalse .
  • Key research papers in this area include "Generative Adversarial Nets" by Ian Goodfellow et al. , and "Direct Preference Optimization: Your language model is secretly a reward model" by Rafael Rafailov et al. .

The key to the solution mentioned in the paper involves framing the LLM alignment problem within the context of forward and inverse Reinforcement Learning (RL), which suggests that corresponding methodologies can be used to address the alignment challenge effectively . The paper proposes a unified objective framework that combines trajectory distribution matching objectives for Alignment from Demonstrations (AfD) . Additionally, the paper highlights the importance of understanding and addressing the challenge of reward hacking in AfD to ensure the effectiveness of the proposed approach .


How were the experiments in the paper designed?

The experiments in the paper were designed to achieve several objectives:

  • Alignment from Demonstrations: The experiments aimed to demonstrate the efficacy of aligning Language Model Models (LLMs) from demonstrations and verify insights derived from the Inverse Reinforcement Learning (IRL) perspective .
  • Evaluation of Reward Modeling: The paper sought to evaluate the necessity and performance of the proposed reward modeling method .
  • Assessment of Scalability and Effectiveness: The experiments were also designed to assess the scalability and effectiveness of the reward model in policy optimization, highlighting the feasibility of alignment without preference-based data .

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

The dataset used for quantitative evaluation in the study is the Harmless and Helpful tasks from the Anthropic HH-RLHF dataset . The code and the demonstration dataset are open source and available at https://github.com/holarissun/InverseRLignment .


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 focuses on aligning Language Models (LLMs) to human intentions through Inverse Reinforcement Learning (IRL) . The experiments evaluate the effectiveness of different methodologies, such as supervised fine-tuning (SFT), Direct Preference Optimization (DPO), and alignment from demonstrations (AfD) . These experiments aim to demonstrate the efficacy of aligning LLMs from demonstrations and provide insights from an Inverse RL perspective .

The paper outlines the hyper-parameters used in the experiments, maintaining consistency across different methods for each task studied . The study employs specific evaluation metrics, including golden reward model scoring and GPT4-as-a-critic evaluation, to measure alignment efficacy . Additionally, the experiments focus on the importance of using the correct data, such as the demonstration dataset, rather than preference-based data, to improve alignment .

Furthermore, the paper differentiates its approach from traditional GAN-based text generation methods by focusing on aligning LLMs to human intentions rather than solely generating text . The study's objective is to enhance language model alignment by learning a reward model inspired by IRL, which sets it apart from adversarial imitation techniques . The practical implementation of the study extrapolates the learned IRL reward model to enhance the performance of supervised fine-tuned LLMs .

In conclusion, the experiments and results in the paper provide robust support for the scientific hypotheses by demonstrating the effectiveness of aligning LLMs through Inverse Reinforcement Learning, utilizing specific evaluation metrics, and emphasizing the importance of using the correct data for alignment tasks.


What are the contributions of this paper?

The paper makes several key contributions:

  • Understanding the effects of rlhf on llm generalisation and diversity .
  • Defining and characterizing reward gaming .
  • Improving GANs with a dynamic discriminator .
  • Measuring data quality for dataset selection in offline reinforcement learning .
  • Towards robust offline reinforcement learning under diverse data corruption .
  • Data quality in imitation learning .
  • Understanding the effects of dataset characteristics on offline reinforcement learning .
  • Overcoming reward overoptimization via adversarial policy optimization with lightweight uncertainty estimation .
  • Reward model ensembles help mitigate overoptimization .
  • Language models are unsupervised multitask learners .
  • Nash learning from human feedback .
  • Scaling reinforcement learning from human feedback with AI feedback .
  • Direct language model alignment from online AI feedback .
  • D4rl: Datasets for deep data-driven reinforcement learning .
  • Understanding the performance gap between online and offline alignment algorithms .
  • Discriminator-actor-critic: Addressing sample inefficiency and reward bias in adversarial imitation learning .
  • Self-adversarial learning with comparative discrimination for text generation .
  • Language GANs falling short .
  • Proximal policy optimization algorithms .
  • Addressing function approximation error in actor-critic methods .
  • Soft actor-critic algorithms and applications .
  • Conservative Q-learning for offline reinforcement learning .
  • Rethinking goal-conditioned supervised learning and its connection to offline RL .
  • Accountable batched control with decision corpus .

What work can be continued in depth?

Further research in the field can explore extending the methods discussed to other divergences within the f-Divergence framework and exploring state-action distribution matching as an alternative learning objective . Additionally, investigating the empirical performance of these objectives, which assume token-level feedback, could be a valuable area for future work . Furthermore, delving into the effects of reinforcement learning from human feedback on large language models' generalization and diversity could provide insights for further advancements in the field .


Introduction
Background
Limitations of preference-based LLM alignment
Importance of high-quality data and privacy concerns
Objective
To develop a novel method for aligning LLMs using AfD
Improve over preference-based approaches with demonstration data
Method
Data Collection
Use of high-quality demonstration data
Selection criteria for data quality
Data Preprocessing
Cleaning and formatting demonstration data
Handling noisy and incomplete data
Divergence Minimization
Formulation of the reward model using divergence measures
Minimization objective for aligning LLM behavior
Algorithm
Computational efficiency of the proposed algorithm
Steps and implementation details
Comparison with Existing Techniques
Behavior cloning and IRL connections
Experimental comparison with preference-based methods
Experiments
Harmless and Helpful Tasks
Task description and setup
Performance evaluation metrics
Results
AfD's effectiveness in improving alignment
Outperformance or parity with preference-based methods
Benefits of AfD
Improved data quality over preferences
Reduced annotation costs
Privacy preservation
Challenges and Future Research
Open questions and limitations
Potential directions for future work in LLM alignment
Conclusion
Summary of key findings
Implications for large language model development and ethics
Basic info
papers
machine learning
artificial intelligence
Advanced features
Insights
What framework does AfD operate within, and what objective does it use for creating a tailored reward model?
How does AfD address the limitations of preference-based approaches in aligning large language models?
How do the experiments on the Harmless and Helpful tasks demonstrate the effectiveness of AfD compared to existing techniques?
What is the primary focus of the Inverse Reinforcement Learning from Demonstrations (AfD) method presented in the paper?

Inverse-RLignment: Inverse Reinforcement Learning from Demonstrations for LLM Alignment

Hao Sun, Mihaela van der Schaar·May 24, 2024

Summary

This paper presents Inverse Reinforcement Learning from Demonstrations (AfD), a novel method for aligning large language models (LLMs) that addresses the limitations of preference-based approaches. AfD leverages high-quality demonstration data to learn a reward model, reducing the need for noisy preferences and inductive biases. It operates within a sequential decision-making framework, using divergence minimization objectives and a computationally efficient algorithm to create a tailored reward model. Experiments on the Harmless and Helpful tasks show that AfD is effective and simpler than existing techniques, outperforming or matching preference-based methods in some cases. The study highlights the benefits of using demonstration data over preferences, including improved data quality, reduced annotation costs, and privacy concerns. The paper also explores the connection between AfD and other learning methods, such as behavior cloning and inverse reinforcement learning, and discusses the challenges and potential for future research in this area.
Mind map
Outperformance or parity with preference-based methods
AfD's effectiveness in improving alignment
Experimental comparison with preference-based methods
Behavior cloning and IRL connections
Minimization objective for aligning LLM behavior
Formulation of the reward model using divergence measures
Results
Comparison with Existing Techniques
Divergence Minimization
Selection criteria for data quality
Use of high-quality demonstration data
Improve over preference-based approaches with demonstration data
To develop a novel method for aligning LLMs using AfD
Importance of high-quality data and privacy concerns
Limitations of preference-based LLM alignment
Implications for large language model development and ethics
Summary of key findings
Potential directions for future work in LLM alignment
Open questions and limitations
Privacy preservation
Reduced annotation costs
Improved data quality over preferences
Harmless and Helpful Tasks
Algorithm
Data Preprocessing
Data Collection
Objective
Background
Conclusion
Challenges and Future Research
Benefits of AfD
Experiments
Method
Introduction
Outline
Introduction
Background
Limitations of preference-based LLM alignment
Importance of high-quality data and privacy concerns
Objective
To develop a novel method for aligning LLMs using AfD
Improve over preference-based approaches with demonstration data
Method
Data Collection
Use of high-quality demonstration data
Selection criteria for data quality
Data Preprocessing
Cleaning and formatting demonstration data
Handling noisy and incomplete data
Divergence Minimization
Formulation of the reward model using divergence measures
Minimization objective for aligning LLM behavior
Algorithm
Computational efficiency of the proposed algorithm
Steps and implementation details
Comparison with Existing Techniques
Behavior cloning and IRL connections
Experimental comparison with preference-based methods
Experiments
Harmless and Helpful Tasks
Task description and setup
Performance evaluation metrics
Results
AfD's effectiveness in improving alignment
Outperformance or parity with preference-based methods
Benefits of AfD
Improved data quality over preferences
Reduced annotation costs
Privacy preservation
Challenges and Future Research
Open questions and limitations
Potential directions for future work in LLM alignment
Conclusion
Summary of key findings
Implications for large language model development and ethics

Paper digest

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

The paper aims to address the challenge of aligning Large Language Models (LLMs) by introducing a novel approach called Alignment from Demonstrations (AfD) . This approach leverages high-quality demonstration data to overcome issues associated with preference datasets, such as noisy labels, high annotation costs, and privacy concerns . The unique challenge highlighted in the paper is the absence of reward signals in the alignment process, which distinguishes AfD from traditional methods . While the problem of aligning LLMs is not new, the paper introduces a fresh perspective by proposing AfD as an alternative approach that utilizes demonstration data to enhance alignment performance and address the limitations of preference-based methods .


What scientific hypothesis does this paper seek to validate?

This paper seeks to validate the scientific hypothesis related to Inverse Reinforcement Learning (IRL) from Demonstrations for Large Language Model (LLM) Alignment . The research focuses on aligning language models with human intentions during response generation by learning a reward model inspired by IRL . The main objective is to improve language model alignment by deriving objectives from IRL literature and addressing challenges associated with preference-based alignment in LLMs . The study aims to explore the effectiveness of different setups of RL, Offline-RL, Imitation Learning, Inverse-RL, Learning from Demonstrations, and Preference-based RL in the context of LLM alignment .


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

The paper "Inverse-RLignment: Inverse Reinforcement Learning from Demonstrations for LLM Alignment" introduces several novel ideas, methods, and models in the context of aligning Large Language Models (LLMs) . Here are the key contributions of the paper:

  1. Alignment from Demonstrations (AfD): The paper proposes Alignment from Demonstrations as a novel approach to LLM alignment, leveraging high-quality demonstration data to overcome challenges like noisy labels, high annotation costs, and privacy concerns associated with preference-based datasets . AfD is formalized within a sequential decision-making framework, addressing the unique challenge of missing reward signals in alignment tasks .

  2. Divergence Minimization Objectives: Drawing insights from forward and inverse reinforcement learning, the paper introduces divergence minimization objectives for AfD. These objectives aim to improve language model alignment by learning a reward model inspired by Inverse Reinforcement Learning (IRL) .

  3. Analytical Insights: The paper elucidates the mass-covering and mode-seeking behaviors of various approaches in LLM alignment, explaining when and why certain methods are superior. This analytical framework helps in understanding the effectiveness of different alignment strategies .

  4. Computational Efficiency: The paper proposes a computationally efficient algorithm that extrapolates over a tailored reward model for AfD, enhancing the performance of LLMs aligned through this approach .

  5. Empirical Validation: Through experiments on tasks like Harmless and Helpful, the paper demonstrates the strong empirical performance of the proposed methods while maintaining simplicity. This validation showcases the practical effectiveness of the novel ideas and models introduced in the paper .

Overall, the paper presents a comprehensive framework for LLM alignment, focusing on the nuances of reward modeling and alignment objectives, distinct from traditional GAN-based text generation methods . The proposed AfD approach and divergence minimization objectives offer a fresh perspective on addressing challenges in aligning Large Language Models. The paper "Inverse-RLignment: Inverse Reinforcement Learning from Demonstrations for LLM Alignment" introduces a novel approach called Alignment from Demonstrations (AfD) for aligning Large Language Models (LLMs) . Here are the key characteristics and advantages of this approach compared to previous methods outlined in the paper:

  1. Alignment from Demonstrations (AfD):

    • Characteristics: AfD focuses on using expert demonstration datasets, which are more accessible and of higher quality compared to preference datasets commonly used in LLM alignment research . It formulates the alignment problem as a Markov Decision Process (MDP) and addresses the challenge of lacking reward signals in LLM alignment .
    • Advantages: AfD does not require continuous querying and comparison, does not rely on assumptions inherent in preference-based methods, and enables LLM alignment without the need for external annotators, making it applicable to private datasets locally .
  2. Reward Modeling:

    • Characteristics: The paper discusses different approaches to building reward models, such as Init-Demo RM, SFT-Demo RM, Init-SFT RM, and Preference-based RM (BT-RM) . Each approach has unique characteristics in utilizing samples generated by different policies for training the reward model.
    • Advantages: The Init-SFT RM approach, which uses samples generated by the initial policy and the fine-tuned policy, aims to avoid potential reward hacking caused by heterogeneous data in reward model training . This method is expected to outperform other choices when applying learned reward models at inference time .
  3. Empirical Validation:

    • Characteristics: The paper conducts experiments to validate the insights and methods proposed, demonstrating the efficacy of alignment from demonstrations and the proposed reward modeling method .
    • Advantages: The results underscore the effectiveness of building reward models using demonstration datasets, with the IRL RM using the Init-SFT approach achieving the highest win rates and scores compared to other models, even matching or surpassing preference-based reward models without the need for preference annotations .
  4. Trajectory Distribution Matching:

    • Characteristics: The paper introduces trajectory distribution matching objectives for AfD, connecting divergence measures with different algorithms . It explores an efficient Inverse RL algorithm for the AfD problem to enhance alignment performance .
    • Advantages: By focusing on trajectory distribution matching, AfD offers a unified objective framework for improving language model alignment, providing theoretical rationales and empirical evidence on leveraging demonstration datasets for aligning LLMs .

Overall, the AfD approach in the paper presents unique characteristics and advantages, such as higher data quality, reduced reliance on external annotators, and improved alignment performance compared to traditional preference-based methods in LLM alignment research.


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 and notable researchers in the field of Inverse Reinforcement Learning (IRL) and Language Model (LLM) Alignment have been identified:

  • Noteworthy researchers in this field include Ian Goodfellow, Yoshua Bengio, and Aaron Courville .
  • Other prominent researchers are Daniel Brown, Scott Niekum, and Joar Skalse .
  • Key research papers in this area include "Generative Adversarial Nets" by Ian Goodfellow et al. , and "Direct Preference Optimization: Your language model is secretly a reward model" by Rafael Rafailov et al. .

The key to the solution mentioned in the paper involves framing the LLM alignment problem within the context of forward and inverse Reinforcement Learning (RL), which suggests that corresponding methodologies can be used to address the alignment challenge effectively . The paper proposes a unified objective framework that combines trajectory distribution matching objectives for Alignment from Demonstrations (AfD) . Additionally, the paper highlights the importance of understanding and addressing the challenge of reward hacking in AfD to ensure the effectiveness of the proposed approach .


How were the experiments in the paper designed?

The experiments in the paper were designed to achieve several objectives:

  • Alignment from Demonstrations: The experiments aimed to demonstrate the efficacy of aligning Language Model Models (LLMs) from demonstrations and verify insights derived from the Inverse Reinforcement Learning (IRL) perspective .
  • Evaluation of Reward Modeling: The paper sought to evaluate the necessity and performance of the proposed reward modeling method .
  • Assessment of Scalability and Effectiveness: The experiments were also designed to assess the scalability and effectiveness of the reward model in policy optimization, highlighting the feasibility of alignment without preference-based data .

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

The dataset used for quantitative evaluation in the study is the Harmless and Helpful tasks from the Anthropic HH-RLHF dataset . The code and the demonstration dataset are open source and available at https://github.com/holarissun/InverseRLignment .


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 focuses on aligning Language Models (LLMs) to human intentions through Inverse Reinforcement Learning (IRL) . The experiments evaluate the effectiveness of different methodologies, such as supervised fine-tuning (SFT), Direct Preference Optimization (DPO), and alignment from demonstrations (AfD) . These experiments aim to demonstrate the efficacy of aligning LLMs from demonstrations and provide insights from an Inverse RL perspective .

The paper outlines the hyper-parameters used in the experiments, maintaining consistency across different methods for each task studied . The study employs specific evaluation metrics, including golden reward model scoring and GPT4-as-a-critic evaluation, to measure alignment efficacy . Additionally, the experiments focus on the importance of using the correct data, such as the demonstration dataset, rather than preference-based data, to improve alignment .

Furthermore, the paper differentiates its approach from traditional GAN-based text generation methods by focusing on aligning LLMs to human intentions rather than solely generating text . The study's objective is to enhance language model alignment by learning a reward model inspired by IRL, which sets it apart from adversarial imitation techniques . The practical implementation of the study extrapolates the learned IRL reward model to enhance the performance of supervised fine-tuned LLMs .

In conclusion, the experiments and results in the paper provide robust support for the scientific hypotheses by demonstrating the effectiveness of aligning LLMs through Inverse Reinforcement Learning, utilizing specific evaluation metrics, and emphasizing the importance of using the correct data for alignment tasks.


What are the contributions of this paper?

The paper makes several key contributions:

  • Understanding the effects of rlhf on llm generalisation and diversity .
  • Defining and characterizing reward gaming .
  • Improving GANs with a dynamic discriminator .
  • Measuring data quality for dataset selection in offline reinforcement learning .
  • Towards robust offline reinforcement learning under diverse data corruption .
  • Data quality in imitation learning .
  • Understanding the effects of dataset characteristics on offline reinforcement learning .
  • Overcoming reward overoptimization via adversarial policy optimization with lightweight uncertainty estimation .
  • Reward model ensembles help mitigate overoptimization .
  • Language models are unsupervised multitask learners .
  • Nash learning from human feedback .
  • Scaling reinforcement learning from human feedback with AI feedback .
  • Direct language model alignment from online AI feedback .
  • D4rl: Datasets for deep data-driven reinforcement learning .
  • Understanding the performance gap between online and offline alignment algorithms .
  • Discriminator-actor-critic: Addressing sample inefficiency and reward bias in adversarial imitation learning .
  • Self-adversarial learning with comparative discrimination for text generation .
  • Language GANs falling short .
  • Proximal policy optimization algorithms .
  • Addressing function approximation error in actor-critic methods .
  • Soft actor-critic algorithms and applications .
  • Conservative Q-learning for offline reinforcement learning .
  • Rethinking goal-conditioned supervised learning and its connection to offline RL .
  • Accountable batched control with decision corpus .

What work can be continued in depth?

Further research in the field can explore extending the methods discussed to other divergences within the f-Divergence framework and exploring state-action distribution matching as an alternative learning objective . Additionally, investigating the empirical performance of these objectives, which assume token-level feedback, could be a valuable area for future work . Furthermore, delving into the effects of reinforcement learning from human feedback on large language models' generalization and diversity could provide insights for further advancements in the field .

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