Rubrics as Rewards (RaR): A Reinforcement Learning Framework for Training Language Models with Structured, Multi-Criteria Evaluation Signals
Reinforcement Learning with Verifiable Rewards (RLVR) allows LLMs to perform complex reasoning on tasks with clear, verifiable outcomes, with strong performance in mathematics and coding. However, many real-world scenarios lack such explicit verifiable answers, posing a challenge for training models without direct reward signals. Current methods address this gap through RLHF via preference ranking, where…
