Overton Pluralistic Reinforcement Learning for Large Language Models
The OP-GRPO framework uses reinforcement learning to create models that produce diverse, pluralistic responses from a single query without explicit prompting. It employs a two-step process involving fine-tuning a similarity estimator and then training the model with a three-part reward system focusing on perspective coverage, uniqueness, and format quality. This allows smaller models to achieve state-of-the-art performance in capturing varied human viewpoints.