Prototypical Reward Network for Data-Efficient RLHF

The paper presents Proto-RM, a framework that integrates prototypical networks with reward models to improve the efficiency of RLHF, allowing for more effective fine-tuning of LLMs using less human feedback data without compromising performance.

Abstract

The reward model for Reinforcement Learning from Human Feedback (RLHF) has proven effective in fine-tuning Large Language Models (LLMs). Notably, collecting human feedback for RLHF can be resource-intensive and lead to scalability issues for LLMs and complex tasks. Our proposed framework Proto-RM leverages prototypical networks to enhance reward models under limited human feedback. By enabling stable and reliable structural learning from fewer samples, Proto-RM significantly enhances LLMs' adaptability and accuracy in interpreting human preferences. Extensive experiments on various datasets demonstrate that Proto-RM significantly improves the performance of reward models and LLMs in human feedback tasks, achieving comparable and usually better results than traditional methods, while requiring significantly less data. in data-limited scenarios. This research offers a promising direction for enhancing the efficiency of reward models and optimizing the fine-tuning of language models under restricted feedback conditions.

Method

The authors used a novel methodology called Proto-RM, which integrates prototypical networks with the reward model in Reinforcement Learning from Human Feedback (RLHF). This approach allows for stable and reliable structural learning from fewer human feedback samples, enhancing the adaptability and accuracy of Large Language Models (LLMs) in interpreting human preferences. The methodology involves three main steps: Sample Encoding and Prototype Initialization, Prototype Update and Addition, and Reward Model Fine-tuning. Prototypical networks are used to learn representative prototypes for similar examples, which are then used to refine the reward model and guide the fine-tuning process of LLMs. The authors conducted extensive experiments to validate the effectiveness of Proto-RM across different dataset sizes and compared its performance with traditional RLHF methods.

Main Finding

The authors discovered that their proposed Proto-RM framework significantly improves the performance of reward models and LLMs in human feedback tasks, achieving comparable or better results than traditional methods while requiring significantly less data. This makes Proto-RM a promising direction for enhancing the efficiency of reward models and optimizing the fine-tuning of language models under restricted feedback conditions. The experiments demonstrated that Proto-RM can effectively learn from limited human feedback samples and generalize well to new tasks, offering a robust solution for data-limited scenarios.

Conclusion

The conclusion of the paper is that the Proto-RM framework, which leverages prototypical networks to enhance reward models for Reinforcement Learning from Human Feedback (RLHF), has been shown to be effective in improving the efficiency and performance of Large Language Models (LLMs) in tasks involving human feedback. Proto-RM achieves this by enabling the reward model to learn stable and reliable data representation structures from fewer samples, thus reducing the reliance on extensive human feedback data without compromising the quality of the model's outputs. The framework has been validated through extensive experiments, demonstrating its superiority over traditional methods in terms of data efficiency and performance, particularly in data-limited scenarios. The authors suggest that future work could explore the application of Proto-RM to more diverse datasets and languages to further establish its effectiveness and adaptability.

Keywords

Reinforcement Learning from Human Feedback (RLHF), Large Language Models (LLMs), Prototypical Networks, Proto-RM, Data Efficiency, Reward Models, Fine-tuning, Human Preferences, Prototypical Reward Model, Few-shot Learning, Prototype Initialization, Prototype Update, Reward Model Fine-tuning, Infinite Mixture Prototypes (IMP), Data-Efficient RLHF, Human Feedback Samples, Embedding Process, Prototype Vectors, Diversity Loss, Toxicity Dataset, Human Evaluations, Time Complexity, Prototypical Learning, Pretrained Language Models, Policy Optimization, Human Annotations, Text Quality, Factual Accuracy, Text Relevance, Information Completeness, Clarity of Expression, Dropout Method, Cosine Similarity Dropout, Random Dropout, Prototype Numbers, Prototype Quantities.