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LangProp is a framework for iteratively optimizing code generated by largelanguage models (LLMs) in a supervised/reinforcement learning setting. WhileLLMs can generate sensible solutions zero-shot, the solutions are oftensub-optimal. Especially for code generation tasks, it is likely that theinitial code will fail on certain edge cases. LangProp automatically evaluatesthe code performance on a dataset of input-output pairs, as well as catches anyexceptions, and feeds the results back to the LLM in the training loop, so thatthe LLM can iteratively improve the code it generates. By adopting a metric-and data-driven training paradigm for this code optimization procedure, onecould easily adapt findings from traditional machine learning techniques suchas imitation learning, DAgger, and reinforcement learning. We demonstrate thefirst proof of concept of automated code optimization for autonomous driving inCARLA, showing that LangProp can generate interpretable and transparent drivingpolicies that can be verified and improved in a metric- and data-driven way.Our code will be open-sourced and is available athttps://github.com/shuishida/LangProp.

LangProp: A code optimization framework using Language Models applied to driving