LangProp is a framework for iteratively optimizing code generated by large
language models (LLMs) in a supervised/reinforcement learning setting. While
LLMs can generate sensible solutions zero-shot, the solutions are often
sub-optimal. Especially for code generation tasks, it is likely that the
initial code will fail on certain edge cases. LangProp automatically evaluates
the code performance on a dataset of input-output pairs, as well as catches any
exceptions, and feeds the results back to the LLM in the training loop, so that
the LLM can iteratively improve the code it generates. By adopting a metric-
and data-driven training paradigm for this code optimization procedure, one
could easily adapt findings from traditional machine learning techniques such
as imitation learning, DAgger, and reinforcement learning. We demonstrate the
first proof of concept of automated code optimization for autonomous driving in
CARLA, showing that LangProp can generate interpretable and transparent driving
policies that can be verified and improved in a metric- and data-driven way.
Our code will be open-sourced and is available at
this https URL

LangProp 是一个用于大型语言模型（LLMs）生成的代码的迭代优化框架，在监督 / 强化学习环境中使用。LangProp 自动评估代码在输入输出数据集上的性能，并捕获任何异常，然后将结果反馈给 LLM 以在训练循环中迭代改进生成的代码。这是自动驾驶的代码优化的第一个概念验证，表明 LangProp 能够生成可解释和透明的驾驶策略，可以在基于度量和数据的方式上进行验证和改进。