Huaye Zeng

ACECODER: Acing Coder RL via Automated Test-Case Synthesis

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 auto…