Methoden vergleichen

	Fine-Tuned Reinforcement Learning	Reinforcement Learning
Fachgebiet	Deep Learning	Deep Learning
Familie	Machine learning	Machine learning
Entstehungsjahr	2017–2022	1950s–1998
Urheber	Christiano, P. et al.; Ouyang, L. et al.	Sutton, R. S. & Barto, A. G. (formalised); Bellman, R. (foundations)
Typ	Policy adaptation via fine-tuning	Sequential decision-making framework
Wegweisende Quelle	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.	Sutton, R. S. & Barto, A. G. (2018). Reinforcement Learning: An Introduction (2nd ed.). MIT Press. ISBN: 978-0-262-03924-6
Aliasnamen	RL fine-tuning, policy fine-tuning, RLHF, reinforcement learning from human feedback	RL, reward-based learning, trial-and-error learning, policy optimization
Verwandt	5	2
Zusammenfassung	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.	Reinforcement Learning (RL) is a framework in which an agent learns to make sequential decisions by interacting with an environment, receiving scalar reward signals, and updating a policy to maximise cumulative future reward. Unlike supervised learning, no labeled examples are provided; the agent discovers optimal behavior entirely through experience and delayed feedback.