ACECODER: Acing Coder RL via Automated Test-Case Synthesis
Authors: name: Huaye Zeng, name: Dongfu Jiang, name: Haozhe Wang, name: Ping Nie, name: Xiaotong Chen, name: Wenhu Chen

Most progress in recent coder models has been driven by supervised
fine-tuning (SFT), while the potential of reinforcement learning (RL) remains
largely unexplored, primarily due to the lack of reliable reward data/model in
the code domain. In this paper, we address this challenge by leveraging
automated large-scale test-case synthesis to enhance code model training.
Specifically, we design a pipeline that generates extensive (question,
test-cases) pairs from existing code data. Using these test cases, we construct
preference pairs based on pass rates over sampled programs to train reward
models with Bradley-Terry loss. It shows an average of 10-point improvement for
Llama-3.1-8B-Ins and 5-point improvement for Qwen2.5-Coder-7B-Ins through
best-of-32 sampling, making the 7B model on par with 236B DeepSeek-V2.5.
Furthermore, we conduct reinforcement learning with both reward models and
test-case pass rewards, leading to consistent improvements across HumanEval,
MBPP, BigCodeBench, and LiveCodeBench (V4). Notably, we follow the R1-style
training to start from Qwen2.5-Coder-base directly and show that our RL
training can improve model on HumanEval-plus by over 25\% and MBPP-plus by 6\%
for merely 80 optimization steps. We believe our results highlight the huge
potential of reinforcement learning in coder models.