  Language models can solve complex reasoning tasks better by learning to
generate rationales for their predictions. Often these models know how to solve
a task but their auto-regressive decoding nature leads to incorrect results if
they start incorrectly. We observe that smaller models in particular when
corrected, can solve a task that they would have otherwise struggled with. We
demonstrate this phenomenon by using a larger model to guide smaller models,
which leads to significantly improved performance (up to +24 points on the
GSM8K dataset by 7B models). To assist smaller models in initiating the
starting step, we propose QuestCoT, where a smaller model first asks itself how
to start, before proceeding with a chain of reasoning. On various multistep
mathematical reasoning datasets over multiple smaller models, we show that
getting the right start can lead to significant performance gains across all
models (gains of up to +6 points on GSM8K, +9 on SVAMP, +5 on ASDiv, and +7 on
MultiArith).
