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This chapter explores the application of reinforcement learning (RL) in natural language processing, focusing on how language models can be optimized using feedback from environments, humans, and artificial intelligence. It covers key aspects of RL, including online and offline learning methods, and the use of reward models to align model behavior with desired outcomes. The chapter also discusses Reinforcement Learning from Human Feedback (RLHF) and AI Feedback (RLAIF), highlighting their roles in refining language model performance. Additionally, we also discuss how to enhance the reasoning capabilities of LLMs using RL. By leveraging these RL techniques, language models can better adapt to complex tasks and interactions.

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Reinforcement Learning

  • 章节

  • 发表日期：

    2026年

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  • Zekun Wang,
  • Guangzheng Xiong,
  • Jie Fu

摘要

This chapter explores the application of reinforcement learning (RL) in natural language processing, focusing on how language models can be optimized using feedback from environments, humans, and artificial intelligence. It covers key aspects of RL, including online and offline learning methods, and the use of reward models to align model behavior with desired outcomes. The chapter also discusses Reinforcement Learning from Human Feedback (RLHF) and AI Feedback (RLAIF), highlighting their roles in refining language model performance. Additionally, we also discuss how to enhance the reasoning capabilities of LLMs using RL. By leveraging these RL techniques, language models can better adapt to complex tasks and interactions.