Reward modeling aligns language models with human preferences, yet coding tasks, requiring strict logic and syntax, pose challenges due to their domain diversity. In this paper, we propose RewardCode, a generalist code reward model trained via a two-stage pipeline, enabling scalable and precise evaluation through principle-based scoring and unit test generation. Specifically, the first stage combines Structured Fine-Tuning and Rejective Fine-Tuning to distill cross-domain summarization and code evaluation capabilities from advanced LLMs. The second stage introduces Pairwise-GRPO, a reinforcement learning method that uses preference pairs to improve solution discrimination, while ensuring the generation of consistent and verifiable unit tests. Further, to train a cross-domain code reward model, we construct CodePair-18K, a dataset of verifiable code preference pairs and executable unit tests. Experiments on multiple benchmarks show that RewardCode outperforms strong baselines in evaluation accuracy, verifying its effectiveness in advancing general-purpose code LLMs.