Salīdzināt metodes

	Saskaņotā pastiprinātās mācīšanās (Fine-Tuned Reinforcement Learning)	Mācīšanās ar pastiprinājumu
Nozare	Dziļā mācīšanās	Dziļā mācīšanās
Saime	Machine learning	Machine learning
Izcelsmes gads	2017–2022	1950s–1998
Autors	Christiano, P. et al.; Ouyang, L. et al.	Sutton, R. S. & Barto, A. G. (formalised); Bellman, R. (foundations)
Tips	Policy adaptation via fine-tuning	Sequential decision-making framework
Pirmavots	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
Citi nosaukumi	RL fine-tuning, policy fine-tuning, RLHF, reinforcement learning from human feedback	RL, reward-based learning, trial-and-error learning, policy optimization
Saistītās	5	2
Kopsavilkums	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.