Introduction

This is the official implementation of our paper AP2O-Coder: Adaptively Progressive Preference Optimization for Reducing Compilation and Runtime Errors in LLM-Generated Code. Accepted by AAAI'26.

Adaptive Progressive Preference Optimization

Coding Error Reduction

Data Efficiency

Reward Curves

Requirements

• deepspeed 0.17.2
• python 3.11.11
• torch 2.7.0
• trl 0.14.0
• transformers 4.51.3
• vllm 0.9.2

Usage

To initiate the preference data self-generation and preference optimization processes, use the following command:

sh pipe-qwen2.5-coder.sh