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Fine-Tuned Reinforcement Learning

Fine-Tuned Reinforcement Learning adapts a pre-trained policy or model to a new task or behavioral objective using reinforcement signals — including human feedback — rather than retraining from scratch. Popularized by RLHF, it is the core technique behind aligning large language models and adapting deep RL agents to specialized environments with minimal additional data.

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Sources

  1. Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., Schulman, J., Hilton, J., Kelton, F., Miller, L., Simens, M., Askell, A., Welinder, P., Christiano, P., Leike, J., & Lowe, R. (2022). Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems, 35, 27730–27744. link ↗
  2. Christiano, P., Leike, J., Brown, T. B., Martic, M., Legg, S., & Amodei, D. (2017). Deep reinforcement learning from human preferences. Advances in Neural Information Processing Systems, 30. link ↗

Related methods

Fine-Tuned BERT-based ClassificationFine-Tuned TransformerReinforcement LearningSelf-supervised Reinforcement LearningTransfer Learning with Reinforcement Learning

Referenced by

Multilingual Reinforcement LearningTransfer Learning with Reinforcement Learning

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ScholarGate — Fine-Tuned Reinforcement Learning (Fine-Tuned Reinforcement Learning (Policy Adaptation via Fine-Tuning)). Retrieved 2026-06-04 from https://scholargate.app/en/deep-learning/fine-tuned-reinforcement-learning