RunRL

Teams struggle to reliably improve LLMs and AI agents for specific tasks without costly prompt tinkering and underused observability data.

RunRL provides reinforcement-learning-as-a-service that takes a model, prompt, and reward signal to automatically train the model to increase reward on the target task.

AI developers and organizations building LLM-powered applications and agents who want their models to self-improve.