Rubrics Beyond Evaluation: Exploiting Rubric Structure in Reinforcement Learning

Key points

• Rubrics are widely used to evaluate open-ended question answering by breaking overall quality into explicit criteria.1
• These explicit criteria make rubrics a rich source of learning guidance, yet their potential for guiding the training process has been largely overlooked.1
• At its core, RSRL builds on three key designs: (1) Rubric-to-Capability Structuring first recovers the capability structure hidden in rubric criteria, (2) Self-Sufficient Capability Learning then uses this structure to bootstrap underdeveloped capabilities into effective learning, (3) Capability-Residual Credit Routing finally preserves capability-specific gains during optimization.1
• Experiments show that RSRL consistently improves both overall performance and underdeveloped capabilities, boosting downstream scientific question-answering performance by up to 7.43 over the initial policy and 7.60 over non-rubric structured RL baselines, showing that reinforcement learning can substantially benefit from the capability structure encoded in rubrics.1

Sources (1)

• [1]Rubrics Beyond Evaluation: Exploiting Rubric Structure in Reinforcement Learning
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 19, 06:06 AM

  We introduce Rubric-Structured Reinforcement Learning (RSRL), an RL framework that unlocks the full potential of rubrics by exploiting their structure throughout the training process, rather than treating them simply as direct reward signals.

  Rubrics are widely used to evaluate open-ended question answering by breaking overall quality into explicit criteria.

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