Outcome-Refining Process Supervision for Code Generation
Authors: name: Zhuohao Yu, name: Weizheng Gu, name: Yidong Wang, name: Zhengran Zeng, name: Jindong Wang, name: Wei Ye, name: Shikun Zhang

Large Language Models have demonstrated remarkable capabilities in code
generation, yet they often struggle with complex programming tasks that require
deep algorithmic reasoning. While process supervision through learned reward
models shows promise in guiding reasoning steps, it requires expensive training
data and suffers from unreliable evaluation. We propose Outcome-Refining
Process Supervision, a novel paradigm that treats outcome refinement itself as
the process to be supervised. Our framework leverages concrete execution
signals to ground the supervision of reasoning steps, while using
tree-structured exploration to maintain multiple solution trajectories
simultaneously. Experiments demonstrate that our approach enables even smaller
models to achieve high success accuracy and performance metrics on competitive
programming tasks, creates more reliable verification than traditional reward
models without requiring training PRMs. Our approach achieves significant
improvements across 5 models and 3 datasets: an average of 26.9% increase in
correctness and 42.2% in efficiency. The results suggest that providing
structured reasoning space with concrete verification signals is crucial for
solving complex programming tasks. We open-source all our code and data at:
https://github.com/zhuohaoyu/ORPS