Reinforcement learning (RL) is essential for training large language models (LLMs), but there is a lack of effective scaling methodologies in this area. This study presents a framework for analyzing RL scaling, demonstrating through extensive experimentation that certain design choices can optimize compute efficiency while maintaining performance. The authors propose a best-practice recipe, ScaleRL, which successfully predicts validation performance using a significant compute budget.