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| 미세조정 강화학습× | 강화학습에서의 전이 학습× | |
|---|---|---|
| 분야 | 딥러닝 | 딥러닝 |
| 계열 | Machine learning | Machine learning |
| 기원 연도≠ | 2017–2022 | 2009 (survey); concept from early 2000s |
| 창시자≠ | Christiano, P. et al.; Ouyang, L. et al. | Taylor, M. E. & Stone, P. |
| 유형≠ | Policy adaptation via fine-tuning | Transfer learning paradigm for sequential decision-making |
| 원전≠ | 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 ↗ | Taylor, M. E., & Stone, P. (2009). Transfer Learning for Reinforcement Learning Domains: A Survey. Journal of Machine Learning Research, 10, 1633–1685. link ↗ |
| 별칭 | RL fine-tuning, policy fine-tuning, RLHF, reinforcement learning from human feedback | Transfer RL, TL for RL, cross-task reinforcement learning, inductive transfer in RL |
| 관련≠ | 5 | 4 |
| 요약≠ | 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. | Transfer Learning with Reinforcement Learning (Transfer RL) is a training paradigm in which knowledge acquired by an agent in one or more source tasks — encoded as policy weights, value functions, or learned representations — is reused to accelerate or improve learning in a related but different target task. It directly addresses the sample-inefficiency that plagues reinforcement learning from scratch in complex or expensive environments. |
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