A Comprehensive Survey of Reward Models: Taxonomy, Applications, Challenges, and Future
Reward Model (RM) has demonstrated impressive potential for enhancing Large Language Models (LLM), as RM can serve as a proxy for human preferences, providing signals to guide LLMs' behavior in various tasks. In this paper, we provide a comprehensive overview of relevant research, exploring RMs from the perspectives of preference collection, reward modeling, and usage. Next, we introduce the applications of RMs and discuss the benchmarks for evaluation. Furthermore, we conduct an in-depth analysis of the challenges existing in the field and dive into the potential research directions. This paper is dedicated to providing beginners with a comprehensive introduction to RMs and facilitating future studies. The resources are publicly available at github\footnote{https://github.com/JLZhong23/awesome-reward-models}.
Code (1)
Similar Papers 제목 키워드 기반
Survey on Large Language Model-Enhanced Reinforcement Learning: Concept, Taxonomy, and Methods
With extensive pre-trained knowledge and high-level general capabilities, large language models (LLMs) emerge as a promising avenue to augment reinforcement learning (RL) in aspects such as multi-task learning, sample ef…
Autonomous DrivingLanguage ModelingLanguage ModellingLarge Language Model+3Trends in Integration of Knowledge and Large Language Models: A Survey and Taxonomy of Methods, Benchmarks, and Applications
Large language models (LLMs) exhibit superior performance on various natural language tasks, but they are susceptible to issues stemming from outdated data and domain-specific limitations. In order to address these chall…
knowledge editingRetrievalSurveyArabic Multimodal Machine Learning: Datasets, Applications, Approaches, and Challenges
Multimodal Machine Learning (MML) aims to integrate and analyze information from diverse modalities, such as text, audio, and visuals, enabling machines to address complex tasks like sentiment analysis, emotion recogniti…
Emotion RecognitionSentiment AnalysisA Comprehensive Survey of Text Classification Techniques and Their Research Applications: Observational and Experimental Insights
The exponential growth of textual data presents substantial challenges in management and analysis, notably due to high storage and processing costs. Text classification, a vital aspect of text mining, provides robust sol…
Classificationtext-classificationText ClassificationProtein Large Language Models: A Comprehensive Survey
Protein-specific large language models (Protein LLMs) are revolutionizing protein science by enabling more efficient protein structure prediction, function annotation, and design. While existing surveys focus on specific…
ArticlesProtein Structure Predictionscientific discoverySurvey