Reinforcement Learning without Verifiable Rewards — Will Brown, Prime Intellect
Will Brown's talk addresses the critical challenge of applying Reinforcement Learning to real-world tasks where verifiable rewards are absent. He outlines how Primordial AI tackles this by leveraging environments as the core anchor for building reward signals. Key strategies include using LLMs as "judges," grounding tasks in production traces or document corpora, and employing "reverse direction" techniques to generate training data. Brown also details methods for calibrating task difficulty, identifying reward hacking, and fostering continual learning by treating model optimization as a science, emphasizing the use of compute to refine environmental signals and abstract human expertise.
