  Large language models (LLMs) have shown remarkable performance on code
generation tasks. A recent application of LLMs for code generation is iterative
code repair, where a model fixes an incorrect program by rationalizing about
errors and generating a new program. However, code repair is primarily studied
on high-resource languages like Python, and the framework's efficacy is
under-explored on low-resource languages. To apply code repair for low-resource
languages, we propose Distilling Low-Resource Repairs (DistiLRR), an approach
that transfers the reasoning and code generation ability from a teacher model
to a student model. Our results show that DistiLRR consistently outperforms
baselines on low-resource languages, but has similar performance on
high-resource languages. To investigate this behavior, we perform a further
analysis and find that the correlation between rationale quality and code
correctness is weaker than previously perceived. We hypothesize this weakness
is magnified in low-resource settings where base models lack deep knowledge of
a programming language, leading to wavering benefits of code repair between
high-resource and low-resource languages.
