Challenges in Training Reinforcement Learning Models

Training reinforcement learning (RL) models presents significant difficulties, as highlighted by industry experts. Many researchers believe that traditional approaches often result in a complex training process that can yield unsatisfactory outcomes. Alternatives such as supervised fine-tuning, incorporating human feedback, and using clearer labels are suggested to enhance model performance. Emphasizing the need for structured learning before jumping into RL, experts argue that building foundational knowledge about the environment is crucial for successful model training.