Fang-Ning Shao

A Survey on Rubric-Guided Reinforcement Learning for Language Models

A Bayesian framework that defines constitutions as prior distributions over evaluation criteria and rubrics as conditional instantiations is introduced, and a taxonomy of rubric-guided RL along the prior-posterior axis is presented, covering constitutional AI, instance-specific rubrics, process-level supervision, self-evolving rubrics, and their agentic and multimodal extensions.