  Large language models (LLMs) have shown exceptional performance as
general-purpose assistants, excelling across a variety of reasoning tasks. This
achievement represents a significant step toward achieving artificial general
intelligence (AGI). Despite these advancements, the effectiveness of LLMs often
hinges on the specific prompting strategies employed, and there remains a lack
of a robust framework to facilitate learning and generalization across diverse
reasoning tasks. To address these challenges, we introduce a novel learning
framework, THOUGHT-LIKE-PRO In this framework, we utilize imitation learning to
imitate the Chain-of-Thought (CoT) process which is verified and translated
from reasoning trajectories generated by a symbolic Prolog logic engine. This
framework proceeds in a self-driven manner, that enables LLMs to formulate
rules and statements from given instructions and leverage the symbolic Prolog
engine to derive results. Subsequently, LLMs convert Prolog-derived successive
reasoning trajectories into natural language CoT for imitation learning. Our
empirical findings indicate that our proposed approach substantially enhances
the reasoning abilities of LLMs and demonstrates robust generalization across
out-of-distribution reasoning tasks.
