Reinforcement learning (RL) is central to aligning language models , from reinforcement learning with human feedback (RLHF) within AI assistants to newer reinforcement learning with verifiable rewards (RLVR) workflows for reasoning and agent tasks.

RL is now becoming a practical technique for specialized AI where enterprises need more accurate agents for domain-specific workflows. Open models provide more control over data, IP, and deployment, while RL turns domain success criteria into training signals.

Frontier labs have shown RL can improve general model capabilities.…