ICML 2025

CursorCore: Assist Programming through Aligning Anything
Hao Jiang 1 Qi Liu 1 2 Rui Li 1 Shengyu Ye 1 Shijin Wang 3
Abstract
Large language models have been successfully
applied to programming assistance tasks, such
as code completion, code insertion, and instruc-
tional code editing. However, these applications
remain insufficiently automated and struggle to
effectively integrate various types of information
during the programming process, including cod-
ing history, code context, and user instructions. In
this work, we propose a new framework that com-
prehensively integrates these information sources,
and collect data to train models and evaluate their
performance. Firstly, to thoroughly evaluate how
well models align with different types of informa-
tion and the quality of their outputs, we introduce
a new benchmark, APEval (Assist Programming
Eval), to comprehensively assess the performance
of models in programming assistance tasks. Then,
for data collection, we develop a data generation
pipeline, Programming-Instruct, which synthe-
sizes training data from diverse sources, such as
GitHub and online judge platforms. This pipeline
can automatically generate various types of mes-
sages throughout the programming process. Fi-
nally, using this pipeline, we generate 219K sam-
ples, fine-tune multiple models, and develop the
CursorCore series. We show that CursorCore out-
performs other models of comparable size. This
framework unifies applications such as inline chat
and automated editing, contributes to the advance-
ment of coding assistants.
1. Introduction
Since the rise of large language models (LLMs), AI-assisted
programming technology has developed rapidly, with many
powerful LLMs being applied in this field (Zan et al., 2022;
1State Key Laboratory of Cognitive Intelligence, University of
Science and Technology of China 2Institute of Artificial Intelli-
gence, Hefei Comprehensive National Science Center 3iFLYTEK
Co., Ltd. Correspondence to: Qi Liu <qiliuql@us...