The paper explores the enhancement of reward modeling in reinforcement learning for large language models, focusing on inference-time scalability. It introduces Self-Principled Critique Tuning (SPCT) to improve generative reward modeling and proposes a meta reward model to optimize performance during inference. Empirical results demonstrate that SPCT significantly enhances the quality and scalability of reward models compared to existing methods.