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

• URL: http://arxiv.org/abs/2602.17684v1
• Date: Wed, 04 Feb 2026 17:56:00 GMT
• Title: CodeScaler: Scaling Code LLM Training and Test-Time Inference via Execution-Free Reward Models
• Authors: Xiao Zhu, Xinyu Zhou, Boyu Zhu, Hanxu Hu, Mingzhe Du, Haotian Zhang, Huiming Wang, Zhijiang Guo,
• Abstract summary: Execution-free reward model designed to scale both reinforcement learning training and test-time inference for code generation.<n>CodeScaler improves Qwen3-8B-Base by an average of +11.72 points, outperforming binary execution-based RL by +1.82 points.
• Score: 32.910307078704996
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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, an execution-free 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 five coding benchmarks, CodeScaler improves Qwen3-8B-Base by an average of +11.72 points, outperforming binary execution-based RL by +1.82 points, and enables scalable reinforcement learning on synthetic datasets without 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-fold 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).

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