  One of the primary driving forces contributing to the superior performance of
Large Language Models (LLMs) is the extensive availability of human-annotated
natural language data, which is used for alignment fine-tuning. This inspired
researchers to investigate self-training methods to mitigate the extensive
reliance on human annotations. However, the current success of self-training
has been primarily observed in natural language scenarios, rather than in the
increasingly important neural-symbolic scenarios. To this end, we propose an
environment-guided neural-symbolic self-training framework named ENVISIONS. It
aims to overcome two main challenges: (1) the scarcity of symbolic data, and
(2) the limited proficiency of LLMs in processing symbolic language. Extensive
evaluations conducted on three distinct domains demonstrate the effectiveness
of our approach. Additionally, we have conducted a comprehensive analysis to
uncover the factors contributing to ENVISIONS's success, thereby offering
valuable insights for future research in this area. Code will be available at
\url{https://github.com/xufangzhi/ENVISIONS}.
