The Role of Reward Models and Reinforcement Learning in LLM Fine-tuning
DOI:
https://doi.org/10.32628/CSEIT25112381Keywords:
Reward Models, Reinforcement Learning from Human Feedback, Constitutional AI, Language Model Fine-tuning, Human Preference AlignmentAbstract
This comprehensive article explores the evolution and implementation of reward models and reinforcement learning techniques in fine-tuning Large Language Models (LLMs). The article examines the fundamental role of reward models in capturing human preferences, the methodological approaches to training these models, and the integration of Reinforcement Learning from Human Feedback (RLHF) in model optimization. It discusses recent advances in Constitutional AI and optimization algorithms, while highlighting current challenges in reward model robustness, scalability, and preference learning. The review analyzes various training approaches, their effectiveness, and the trade-offs involved in different implementation strategies, providing insights into future directions for improving LLM alignment with human preferences.
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