CodeScaler: Scaling Code LLM Training and Test-Time Inference via Reward Models

Reinforcement Learning from Verifiable Rewards (RLVR) has driven recent progress in code large language models by leveraging execution-based feedback from unit tests, but its scalability is fundamentally constrained by the availability and reliability of high-quality test cases. We propose **CodeScaler**, a reward model designed to scale both reinforcement learning training and test-time inference for code generation. **CodeScaler** is trained on carefully curated preference data derived from verified code problems and incorporates syntax-aware code extraction and validity-preserving reward shaping to ensure stable and robust optimization. Across four coding benchmarks, **CodeScaler** consistently outperforms execution-based RL by **+1.55** points on Qwen3-8B-Base and **+4.23** points on Qwen3-14B-Base. By further scaling to 44K problems with additional synthetic data, **CodeScaler** yields **+14.64** points improvement over the base model without requiring any test cases. At inference time, **CodeScaler** serves as an effective test-time scaling method, achieving performance comparable to unit test approaches while providing a **10×** reduction in latency. Moreover, **CodeScaler** surpasses existing reward models on RM-Bench not only in the code domain (**+3.3** points), but also in general and reasoning domains (**+2.7** points on average).