Enterprise Reinforcement Learning with Rubrics as Rewards

A new method called Rubrics as Rewards (RaR) extends reinforcement learning to train AI models on complex, subjective enterprise problems that lack simple correct or incorrect answers. The technique uses a two-model loop where a student model produces a solution and a judge model evaluates it against a detailed, multi-faceted rubric to generate a reward signal. In a legal analysis use case, a small model fine-tuned with RaR outperformed the much larger GPT-4.1, demonstrating the method's effectiveness. This approach allows enterprises to build smaller, more efficient, and transparent models for specialized tasks at a lower cost and with smaller datasets.