Díaz-Rodríguez, E. (2025). RLHF y RLAIF, Revolución Silenciosa de la Retroalimentación Educativa. HETS Online Journal, 15(2), 33-48. https://doi.org/10.55420/2693.9193.v15.n2.33
Díaz-Rodríguez, E. (2025). RLHF y RLAIF, R evolución Silenciosa de la Retroalimentación Educativa . HETS Online Journal , 15 (2), 33-48. https://doi.org/10.55420/2693.9193.v15.n2.33 Abstract Reinforcement learning from feedback has emerged as an innovative technique in machine learning, enhancing artificial intelligence (AI) model training. Current research compares two key approaches: RLHF (Reinforcement Learning from Human Feedback) and RLAIF (Reinforcement Learning from AI Feedback). Most studies demonstrate a preference for RLAIF due to its superior scalability (Khedri & Höglund, 2023; Lee et al., 2022; Zhichao et al., 2024). However, other researchers advocate for a hybrid approach that strategically combines both methods (Dakota, 2024). These complementary frameworks can synergistically improve machine learning processes. by evondue in pixabay