CursorCore: Assist Programming through Aligning Anything
Authors: name: Hao Jiang, name: Qi Liu, name: Rui Li, name: Shengyu Ye, name: Shijin Wang

Large language models have been successfully applied to programming
assistance tasks, such as code completion, code insertion, and instructional
code editing. However, these applications remain insufficiently automated and
struggle to effectively integrate various types of information during the
programming process, including coding history, current code, and user
instructions. In this work, we propose a new conversational framework that
comprehensively integrates these information sources, collect data to train our
models and evaluate their performance. Firstly, to thoroughly evaluate how well
models align with different types of information 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 synthesizes training data from diverse sources,
such as GitHub and online judge platforms. This pipeline can automatically
generate various types of messages throughout the programming process. Finally,
using this pipeline, we generate 219K samples, fine-tune multiple models, and
develop the CursorCore series. We show that CursorCore outperforms other models
of comparable size. This framework unifies applications such as inline chat and
automated editing, contributes to the advancement of coding assistants. Code,
models and data are freely available at
https://github.com/TechxGenus/CursorCore.