Think Multilingual, Not Harder: A Data-Efficient Framework for Teaching Reasoning Models to Code-Switch

This research introduces a data-efficient fine-tuning framework to teach large language models to effectively code-switch for reasoning tasks. It identifies beneficial code-switched behaviors, moving beyond treating code-switching as an error, through systematic analysis of diverse reasoning traces.