  Recent advancements in pretraining have demonstrated that modern Large
Language Models (LLMs) possess the capability to effectively learn arithmetic
operations. However, despite acknowledging the significance of digit order in
arithmetic computation, current methodologies predominantly rely on sequential,
step-by-step approaches for teaching LLMs arithmetic, resulting in a conclusion
where obtaining better performance involves fine-grained step-by-step.
Diverging from this conventional path, our work introduces a novel strategy
that not only reevaluates the digit order by prioritizing output from the least
significant digit but also incorporates a step-by-step methodology to
substantially reduce complexity. We have developed and applied this method in a
comprehensive set of experiments. Compared to the previous state-of-the-art
(SOTA) method, our findings reveal an overall improvement of in accuracy while
requiring only a third of the tokens typically used during training. For the
purpose of facilitating replication and further research, we have made our code
and dataset publicly available at
\url{https://anonymous.4open.science/r/RAIT-9FB7/}.
