Online Reinforcement Learning for Large Language Models

Short summary

Online reinforcement learning for LLMs uses real-time user feedback instead of static datasets, enabling models to adapt to changing usage patterns in production. The framework maps language generation to RL concepts: state is conversation context, actions are token sequences, and reward models score output quality across dimensions like accuracy, coherence, and cost. The high-dimensional action space of natural language makes reward modeling far more complex than in classical RL.

• •Online RL enables LLMs to learn from live interactions rather than pre-existing datasets
• •State includes user text, conversation history, system instructions, and tool outputs
• •Reward models evaluate text quality across multiple dimensions, guiding policy optimization

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