How Online Reinforcement Learning Helps Large Language Models Improve in Real Time

Large language models can be refined beyond initial training using techniques such as supervised fine-tuning and reinforcement learning to handle specific tasks. Online reinforcement learning differs from offline methods by incorporating live user feedback rather than relying on fixed datasets, allowing models to adapt continuously. In this framework, a model acts as an agent that generates token sequences as actions within an extremely high-dimensional space, guided by signals from its environment. Reward models evaluate output quality across dimensions like accuracy, coherence, and relevance, then feed signals back to adjust the model's parameters. This approach helps overcome the limitations of static training data by enabling models to correct errors and respond to shifting usage patterns in real-world deployments.