When human programmers have mastered a programming language, it would be easier when they learn a new programming language.
In this report, we focus on exploring whether programming languages can boost each other during the instruction fine-tuning phase of code large language models.
We conduct extensive experiments ofpopular programming languages (Python, JavaScript, TypeScript, C, C++, Java, Go, HTML)  on StarCoder.
Results demonstrate that programming languages can significantly improve each other.
For example,CodeM-PythonB trained on Python is able to increase Java by an absolute% passon HumanEval-X.
More surprisingly, we found thatCodeM-HTMLB trained on the HTML corpus can improve Java by an absolute% pass.
Our training data is released athttps://github.com/NL2Code/CodeM.