This research demonstrates how Reinforcement Learning with Verifiable Rewards (RLVR) can be successfully expanded beyond coding and math to diverse practical domains.

• Cross-domain effectiveness: RLVR shows significant performance gains across medicine, chemistry, psychology, economics and more
• Scalable approach: The methodology effectively bridges structured and unstructured domains
• Performance improvements: Consistently enhances LLM capabilities in complex real-world applications
• Practical implementation: Provides a framework for applying RLVR to less-structured knowledge areas

Medical Impact: For healthcare applications, this approach enables more reliable AI reasoning with verifiable outputs, potentially improving clinical decision support systems and medical information processing.

Crossing the Reward Bridge: Expanding RL with Verifiable Rewards Across Diverse Domains