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Enhancing Reasoning in LLMs through RLHF
This chapter explores the role of reinforcement learning with human feedback (RLHF) in improving the reasoning abilities of large language models (LLMs). It discusses the challenges of traditional RL methods, emphasizing the significance of fine-tuning pre-trained models and the need for diversity in model outputs to enhance performance. Additionally, the chapter highlights the exploration-exploitation dilemma and the impact of various algorithms on training efficiency and model adaptability.